From d097e5e3e59a1ad6a44eaa18b84c3ad6325a716f Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Tue, 1 Dec 2020 00:50:17 +0100 Subject: [PATCH 001/385] First signs of life. --- hello.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 hello.txt diff --git a/hello.txt b/hello.txt new file mode 100644 index 00000000..8ecf0093 --- /dev/null +++ b/hello.txt @@ -0,0 +1 @@ +testing if git is set up correctly From e4e33a2ed26383a0dc5110f46634865a8cec6d84 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Tue, 1 Dec 2020 00:50:43 +0100 Subject: [PATCH 002/385] First signs of life detected. --- hello.txt | 1 - 1 file changed, 1 deletion(-) delete mode 100644 hello.txt diff --git a/hello.txt b/hello.txt deleted file mode 100644 index 8ecf0093..00000000 --- a/hello.txt +++ /dev/null @@ -1 +0,0 @@ -testing if git is set up correctly From 43ec0997d3269a8abdc2f3b80986e7fca7a51b5d Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Fri, 8 Jan 2021 17:56:47 +0100 Subject: [PATCH 003/385] Progress towards vectorization setup. --- .../Dialect/SPN/Analysis/SLP/SLPGraph.h | 44 ++++++++++++++++++ mlir/include/Dialect/SPN/SPNPasses.h | 4 ++ mlir/include/Dialect/SPN/SPNPasses.td | 5 +++ .../lib/Dialect/SPN/Analysis/SLP/SLPGraph.cpp | 45 +++++++++++++++++++ mlir/lib/Dialect/SPN/CMakeLists.txt | 2 + .../Dialect/SPN/Passes/SPNVectorization.cpp | 42 +++++++++++++++++ mlir/tools/spnc-opt/spnc-opt.cpp | 4 ++ 7 files changed, 146 insertions(+) create mode 100644 mlir/include/Dialect/SPN/Analysis/SLP/SLPGraph.h create mode 100644 mlir/lib/Dialect/SPN/Analysis/SLP/SLPGraph.cpp create mode 100644 mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPGraph.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPGraph.h new file mode 100644 index 00000000..8933c35b --- /dev/null +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPGraph.h @@ -0,0 +1,44 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#ifndef SPNC_MLIR_DIALECTS_INCLUDE_DIALECT_SPN_ANALYSIS_SLP_SLPGRAPH_H +#define SPNC_MLIR_DIALECTS_INCLUDE_DIALECT_SPN_ANALYSIS_SLP_SLPGRAPH_H + +#include "mlir/IR/Operation.h" +#include "mlir/IR/OpDefinition.h" +#include "llvm/ADT/SmallPtrSet.h" +#include "llvm/ADT/StringMap.h" + +namespace mlir { + namespace spn { + namespace slp { + + /// + /// Graph class storing Use-Def chains of an SPN. + class SLPGraph { + + public: + + /// Constructor, initialize analysis. + /// \param root Root node of a (sub-)graph or query operation. + explicit SLPGraph(Operation* root); + + /// + /// \return All seed operations. + const SmallPtrSet& getSeeds() const; + + private: + + void analyzeGraph(Operation* root); + void traverseSubgraph(Operation* root); + + SmallPtrSet seeds; + + }; + } + } +} + +#endif //SPNC_MLIR_DIALECTS_INCLUDE_DIALECT_SPN_ANALYSIS_SLP_SLPGRAPH_H diff --git a/mlir/include/Dialect/SPN/SPNPasses.h b/mlir/include/Dialect/SPN/SPNPasses.h index 4f8816d8..0ae66fbe 100644 --- a/mlir/include/Dialect/SPN/SPNPasses.h +++ b/mlir/include/Dialect/SPN/SPNPasses.h @@ -21,6 +21,10 @@ namespace mlir { /// \return Pass instance. std::unique_ptr> createSPNTypePinningPass(); + /// Instantiate the SPNVectorization pass vectorizing the computation of the SPN. + /// \return Pass instance. + std::unique_ptr> createSPNVectorizationPass(); + #define GEN_PASS_REGISTRATION #include "SPN/SPNPasses.h.inc" } diff --git a/mlir/include/Dialect/SPN/SPNPasses.td b/mlir/include/Dialect/SPN/SPNPasses.td index 93a59794..bbbd7ac3 100644 --- a/mlir/include/Dialect/SPN/SPNPasses.td +++ b/mlir/include/Dialect/SPN/SPNPasses.td @@ -13,4 +13,9 @@ def SPNTypePinning : Pass<"spn-type-pinning", "ModuleOp"> { let constructor = "mlir::spn::createSPNTypePinningPass()"; } +def SPNVectorization : Pass<"spn-vectorization", "ModuleOp"> { + let summary = "Vectorize SPN execution"; + let constructor = "mlir::spn::createSPNVectorizationPass()"; +} + #endif // MLIR_DIALECT_SPN_PASSES \ No newline at end of file diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPGraph.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPGraph.cpp new file mode 100644 index 00000000..7216e4ff --- /dev/null +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPGraph.cpp @@ -0,0 +1,45 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#include "SPN/Analysis/SLP/SLPGraph.h" +#include "SPN/SPNInterfaces.h" + +#include + +using namespace mlir; +using namespace mlir::spn; +using namespace mlir::spn::slp; + +SLPGraph::SLPGraph(Operation* root) : seeds{} { + assert(root); + analyzeGraph(root); +} + +const SmallPtrSet& SLPGraph::getSeeds() const { return seeds; } + +void SLPGraph::analyzeGraph(Operation* root) { + if (auto query = dyn_cast(root)) { + // If this is a query op, traverse all root nodes stored in the query. + for (auto r : query.getRootNodes()) { + analyzeGraph(r); + } + + } else { + traverseSubgraph(root); + } +} + +void SLPGraph::traverseSubgraph(Operation* root) { + std::cout << root->getName().getStringRef().str() << std::endl; + if (auto leaf = dyn_cast(root)) { + std::cout << "\tis a leaf." << std::endl; + } else { + std::cout << "\tis an inner node." << std::endl; + for (auto op : root->getOperands()) { + traverseSubgraph(op.getDefiningOp()); + } + } + +} diff --git a/mlir/lib/Dialect/SPN/CMakeLists.txt b/mlir/lib/Dialect/SPN/CMakeLists.txt index 23272bb7..d3305475 100644 --- a/mlir/lib/Dialect/SPN/CMakeLists.txt +++ b/mlir/lib/Dialect/SPN/CMakeLists.txt @@ -5,11 +5,13 @@ add_mlir_dialect_library(MLIRSPN SPNEnums.cpp Analysis/SPNNodeLevel.cpp Analysis/SPNGraphStatistics.cpp + Analysis/SLP/SLPGraph.cpp Canonicalization/CanonicalizationPatterns.cpp Simplification/SimplificationPatterns.cpp TypeAnalysis/TypePinningPatterns.cpp Passes/SPNOpSimplifier.cpp Passes/SPNTypePinning.cpp + Passes/SPNVectorization.cpp ADDITIONAL_HEADER_DIRS ${PROJECT_SOURCE_DIR}/mlir/include/Dialect diff --git a/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp b/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp new file mode 100644 index 00000000..38bd8e01 --- /dev/null +++ b/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp @@ -0,0 +1,42 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#include "mlir/IR/Matchers.h" +#include "mlir/IR/PatternMatch.h" +#include "SPN/SPNPasses.h" +#include "SPNPassDetails.h" +#include +#include +#include "SPN/Analysis/SLP/SLPGraph.h" +using namespace mlir; +using namespace mlir::spn; + +namespace { + + struct SPNVectorization : public SPNVectorizationBase { + + protected: + void runOnOperation() override { + // TODO + std::cout << "Starting SPN vectorization..." << std::endl; + auto module = getOperation(); + llvm::SmallVector queries; + module.walk([&queries](Operation* op) { + if (auto query = dyn_cast(op)) { + queries.push_back(op); + } + }); + slp::SLPGraph graph(queries.front()); + std::cout << queries.front()->getName().getStringRef().str() << std::endl; + + } + }; + +} + +std::unique_ptr> mlir::spn::createSPNVectorizationPass() { + return std::make_unique(); +} + diff --git a/mlir/tools/spnc-opt/spnc-opt.cpp b/mlir/tools/spnc-opt/spnc-opt.cpp index 8f7ffbaf..07d9c0be 100644 --- a/mlir/tools/spnc-opt/spnc-opt.cpp +++ b/mlir/tools/spnc-opt/spnc-opt.cpp @@ -71,6 +71,10 @@ int main(int argc, char** argv) { return mlir::spn::createSPNTypePinningPass(); }); + mlir::registerPass("spn-vectorize", "Vectorize the SPN execution", []() -> std::unique_ptr { + return mlir::spn::createSPNVectorizationPass(); + }); + mlir::registerPass("spn-to-standard", "Lower SPN to Standard dialect", []() -> std::unique_ptr { return mlir::spn::createSPNtoStandardConversionPass(); }); From 8a58225798206638a19bd32f464e847b1f5c883f Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 10 Jan 2021 02:52:58 +0100 Subject: [PATCH 004/385] Build graph progress. --- .../Dialect/SPN/Analysis/SLP/SLPNode.h | 39 ++++++++ .../Analysis/SLP/{SLPGraph.h => SLPTree.h} | 22 +++-- .../lib/Dialect/SPN/Analysis/SLP/SLPGraph.cpp | 45 --------- mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp | 20 ++++ mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 94 +++++++++++++++++++ mlir/lib/Dialect/SPN/CMakeLists.txt | 3 +- .../Dialect/SPN/Passes/SPNVectorization.cpp | 16 ++-- 7 files changed, 175 insertions(+), 64 deletions(-) create mode 100644 mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h rename mlir/include/Dialect/SPN/Analysis/SLP/{SLPGraph.h => SLPTree.h} (63%) delete mode 100644 mlir/lib/Dialect/SPN/Analysis/SLP/SLPGraph.cpp create mode 100644 mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp create mode 100644 mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h new file mode 100644 index 00000000..90a45bca --- /dev/null +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h @@ -0,0 +1,39 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#ifndef SPNC_MLIR_DIALECTS_INCLUDE_DIALECT_SPN_ANALYSIS_SLP_SLPNODE_H +#define SPNC_MLIR_DIALECTS_INCLUDE_DIALECT_SPN_ANALYSIS_SLP_SLPNODE_H + +#include "mlir/IR/Operation.h" +#include "mlir/IR/OpDefinition.h" +#include "llvm/ADT/SmallVector.h" + +namespace mlir { + namespace spn { + namespace slp { + + /// + /// Stores vectorizable instructions. + class SLPNode { + + public: + + /// Constructor, initialize empty SLP node. + /// \param width The width of the operations vector. + explicit SLPNode(size_t const& width); + + explicit SLPNode(std::vector const& values); + + private: + + size_t const width; + std::vector operations; + + }; + } + } +} + +#endif //SPNC_MLIR_DIALECTS_INCLUDE_DIALECT_SPN_ANALYSIS_SLP_SLPNODE_H diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPGraph.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h similarity index 63% rename from mlir/include/Dialect/SPN/Analysis/SLP/SLPGraph.h rename to mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h index 8933c35b..1e916729 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPGraph.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h @@ -10,6 +10,10 @@ #include "mlir/IR/OpDefinition.h" #include "llvm/ADT/SmallPtrSet.h" #include "llvm/ADT/StringMap.h" +#include "SLPNode.h" + +#include +#include namespace mlir { namespace spn { @@ -17,24 +21,24 @@ namespace mlir { /// /// Graph class storing Use-Def chains of an SPN. - class SLPGraph { + class SLPTree { public: /// Constructor, initialize analysis. /// \param root Root node of a (sub-)graph or query operation. - explicit SLPGraph(Operation* root); - - /// - /// \return All seed operations. - const SmallPtrSet& getSeeds() const; + explicit SLPTree(Operation* op); private: - void analyzeGraph(Operation* root); - void traverseSubgraph(Operation* root); + void buildGraph(std::vector const& values); + + bool vectorizable(std::vector const& values) const; + bool commutative(std::vector const& values) const; + + std::vector getOperands(std::vector const& values) const; - SmallPtrSet seeds; + std::vector graph; }; } diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPGraph.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPGraph.cpp deleted file mode 100644 index 7216e4ff..00000000 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPGraph.cpp +++ /dev/null @@ -1,45 +0,0 @@ -// -// This file is part of the SPNC project. -// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. -// - -#include "SPN/Analysis/SLP/SLPGraph.h" -#include "SPN/SPNInterfaces.h" - -#include - -using namespace mlir; -using namespace mlir::spn; -using namespace mlir::spn::slp; - -SLPGraph::SLPGraph(Operation* root) : seeds{} { - assert(root); - analyzeGraph(root); -} - -const SmallPtrSet& SLPGraph::getSeeds() const { return seeds; } - -void SLPGraph::analyzeGraph(Operation* root) { - if (auto query = dyn_cast(root)) { - // If this is a query op, traverse all root nodes stored in the query. - for (auto r : query.getRootNodes()) { - analyzeGraph(r); - } - - } else { - traverseSubgraph(root); - } -} - -void SLPGraph::traverseSubgraph(Operation* root) { - std::cout << root->getName().getStringRef().str() << std::endl; - if (auto leaf = dyn_cast(root)) { - std::cout << "\tis a leaf." << std::endl; - } else { - std::cout << "\tis an inner node." << std::endl; - for (auto op : root->getOperands()) { - traverseSubgraph(op.getDefiningOp()); - } - } - -} diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp new file mode 100644 index 00000000..179ac72e --- /dev/null +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp @@ -0,0 +1,20 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#include "SPN/Analysis/SLP/SLPNode.h" + +#include + +using namespace mlir; +using namespace mlir::spn; +using namespace mlir::spn::slp; + +SLPNode::SLPNode(size_t const& width) : width{width}, operations{} { +} + +SLPNode::SLPNode(std::vector const& values) : width{values.size()}, operations{values} { +} + + diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp new file mode 100644 index 00000000..365b9909 --- /dev/null +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -0,0 +1,94 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#include "SPN/Analysis/SLP/SLPTree.h" +#include "SPN/SPNInterfaces.h" + +#include +#include "llvm/ADT/StringMap.h" + +using namespace mlir; +using namespace mlir::spn; +using namespace mlir::spn::slp; + +SLPTree::SLPTree(Operation* root) { + assert(root); + llvm::StringMap> operationsByOpCode; + for (auto& op : root->getBlock()->getOperations()) { + operationsByOpCode[op.getName().getStringRef().str()].emplace_back(&op); + } + for (auto const& entry : operationsByOpCode) { + buildGraph(entry.getValue()); + } + +} + +/* +void SLPTree::analyzeGraph(Operation* root) { + traverseSubgraph(root); +} + +void SLPTree::traverseSubgraph(Operation* root) { + std::cout << root->getName().getStringRef().str() << std::endl; + if (auto leaf = dyn_cast(root)) { + std::cout << "\tis a leaf." << std::endl; + } else { + std::cout << "\tis an inner node." << std::endl; + for (auto op : root->getOperands()) { + traverseSubgraph(op.getDefiningOp()); + } + } + +}*/ + + +void SLPTree::buildGraph(std::vector const& values) { + for (auto const& op : values) { + std::cout << op->getName().getStringRef().str() << std::endl; + } + // Stop growing graph + if (!vectorizable(values)) { + // Create new node for values and add to graph + graph.emplace_back(values); + // Recursion call to grow graph further + // 1. Commutative + if (commutative(values)) { + // A. Coarsening Mode + for (auto const& operation : values) { + auto const& valueOpCode = values.front()->getName(); + bool sameOpCode = true; + for (auto const& operand : operation->getOperands()) { + if (operand.getDefiningOp()->getName() != valueOpCode) { + sameOpCode = false; + break; + } + } + + if (!sameOpCode) { + + } + + } + } + } +} + +bool SLPTree::vectorizable(std::vector const& values) const { + // TODO + assert(false); + return false; +} + +bool SLPTree::commutative(std::vector const& values) const { + // TODO + assert(false); + return false; +} + +std::vector SLPTree::getOperands(std::vector const& values) const { + // TODO + assert(false); + return {}; +} \ No newline at end of file diff --git a/mlir/lib/Dialect/SPN/CMakeLists.txt b/mlir/lib/Dialect/SPN/CMakeLists.txt index d3305475..d940db7e 100644 --- a/mlir/lib/Dialect/SPN/CMakeLists.txt +++ b/mlir/lib/Dialect/SPN/CMakeLists.txt @@ -5,7 +5,8 @@ add_mlir_dialect_library(MLIRSPN SPNEnums.cpp Analysis/SPNNodeLevel.cpp Analysis/SPNGraphStatistics.cpp - Analysis/SLP/SLPGraph.cpp + Analysis/SLP/SLPTree.cpp + Analysis/SLP/SLPNode.cpp Canonicalization/CanonicalizationPatterns.cpp Simplification/SimplificationPatterns.cpp TypeAnalysis/TypePinningPatterns.cpp diff --git a/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp b/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp index 38bd8e01..7dd92a03 100644 --- a/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp +++ b/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp @@ -9,7 +9,7 @@ #include "SPNPassDetails.h" #include #include -#include "SPN/Analysis/SLP/SLPGraph.h" +#include "SPN/Analysis/SLP/SLPTree.h" using namespace mlir; using namespace mlir::spn; @@ -19,18 +19,16 @@ namespace { protected: void runOnOperation() override { - // TODO std::cout << "Starting SPN vectorization..." << std::endl; - auto module = getOperation(); - llvm::SmallVector queries; - module.walk([&queries](Operation* op) { + auto func = getOperation(); + + func.walk([](Operation* op) { if (auto query = dyn_cast(op)) { - queries.push_back(op); + for (auto r : query.getRootNodes()) { + slp::SLPTree graph(r); + } } }); - slp::SLPGraph graph(queries.front()); - std::cout << queries.front()->getName().getStringRef().str() << std::endl; - } }; From aba65c8a928c48c805ee830334648bed21e3b863 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 10 Jan 2021 03:45:45 +0100 Subject: [PATCH 005/385] SLP Node changes. --- .../Dialect/SPN/Analysis/SLP/SLPNode.h | 7 ++- .../Dialect/SPN/Analysis/SLP/SLPTree.h | 5 +- mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp | 4 +- mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 53 ++++++++++--------- 4 files changed, 34 insertions(+), 35 deletions(-) diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h index 90a45bca..919070fd 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h @@ -14,14 +14,10 @@ namespace mlir { namespace spn { namespace slp { - /// - /// Stores vectorizable instructions. class SLPNode { public: - /// Constructor, initialize empty SLP node. - /// \param width The width of the operations vector. explicit SLPNode(size_t const& width); explicit SLPNode(std::vector const& values); @@ -29,7 +25,10 @@ namespace mlir { private: size_t const width; + + /// List of operations that can be executed in any order/in parallel when width > 1. std::vector operations; + std::vector operands; }; } diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h index 1e916729..b29323a3 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h @@ -35,10 +35,9 @@ namespace mlir { bool vectorizable(std::vector const& values) const; bool commutative(std::vector const& values) const; + bool attachableOperands(Operation* operation) const; - std::vector getOperands(std::vector const& values) const; - - std::vector graph; + SLPNode graph; }; } diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp index 179ac72e..0ace5dce 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp @@ -11,10 +11,10 @@ using namespace mlir; using namespace mlir::spn; using namespace mlir::spn::slp; -SLPNode::SLPNode(size_t const& width) : width{width}, operations{} { +SLPNode::SLPNode(size_t const& width) : width{width}, operations{}, operands{} { } -SLPNode::SLPNode(std::vector const& values) : width{values.size()}, operations{values} { +SLPNode::SLPNode(std::vector const& values) : width{values.size()}, operations{values}, operands{} { } diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index 365b9909..8efed9f5 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -50,45 +50,46 @@ void SLPTree::buildGraph(std::vector const& values) { } // Stop growing graph if (!vectorizable(values)) { - // Create new node for values and add to graph - graph.emplace_back(values); - // Recursion call to grow graph further - // 1. Commutative - if (commutative(values)) { - // A. Coarsening Mode - for (auto const& operation : values) { - auto const& valueOpCode = values.front()->getName(); - bool sameOpCode = true; - for (auto const& operand : operation->getOperands()) { - if (operand.getDefiningOp()->getName() != valueOpCode) { - sameOpCode = false; - break; - } - } - - if (!sameOpCode) { - + return; + } + // Create new node for values and add to graph + graph.emplace_back(values); + // Recursion call to grow graph further + // 1. Commutative + if (commutative(values)) { + // A. Coarsening Mode + for (auto const& operation : values) { + if (attachableOperands(operation)) { + std::vector operands; + for (auto operand : operation->getOperands()) { + operands.emplace_back(operand.getDefiningOp()); } + buildGraph(operands); + } else { } + } + } } bool SLPTree::vectorizable(std::vector const& values) const { // TODO - assert(false); - return false; + return true; } bool SLPTree::commutative(std::vector const& values) const { // TODO - assert(false); - return false; + return true; } -std::vector SLPTree::getOperands(std::vector const& values) const { - // TODO - assert(false); - return {}; +bool SLPTree::attachableOperands(Operation* operation) const { + // TODO operands escape multi-node? + for (auto const& operand : operation->getOperands()) { + if (operand.getDefiningOp()->getName() != operation->getName()) { + return false; + } + } + return true; } \ No newline at end of file From 43ee1675e7989d94e0b5d1ee4cbdfb88c20ba7ae Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 10 Jan 2021 20:11:35 +0100 Subject: [PATCH 006/385] Operands fix. --- .../Dialect/SPN/Analysis/SLP/SLPNode.h | 2 ++ .../Dialect/SPN/Analysis/SLP/SLPTree.h | 7 ++-- mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp | 5 ++- mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 32 +++++++++---------- .../Dialect/SPN/Passes/SPNVectorization.cpp | 2 +- 5 files changed, 27 insertions(+), 21 deletions(-) diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h index 919070fd..b8d000d3 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h @@ -22,6 +22,8 @@ namespace mlir { explicit SLPNode(std::vector const& values); + SLPNode& addOperands(std::vector const& values); + private: size_t const width; diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h index b29323a3..9420793b 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h @@ -27,15 +27,16 @@ namespace mlir { /// Constructor, initialize analysis. /// \param root Root node of a (sub-)graph or query operation. - explicit SLPTree(Operation* op); + /// \param width The target width of the SLP vectors. + explicit SLPTree(Operation* op, size_t width); private: - void buildGraph(std::vector const& values); + void buildGraph(std::vector const& values, SLPNode& parentNode); bool vectorizable(std::vector const& values) const; bool commutative(std::vector const& values) const; - bool attachableOperands(Operation* operation) const; + bool attachableOperands(OperationName const& currentOperation, std::vector const& operands) const; SLPNode graph; diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp index 0ace5dce..67165865 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp @@ -17,4 +17,7 @@ SLPNode::SLPNode(size_t const& width) : width{width}, operations{}, operands{} { SLPNode::SLPNode(std::vector const& values) : width{values.size()}, operations{values}, operands{} { } - +SLPNode& SLPNode::addOperands(std::vector const& values) { + operands.emplace_back(SLPNode{values}); + return operands.back(); +} diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index 8efed9f5..ce5f9554 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -13,14 +13,14 @@ using namespace mlir; using namespace mlir::spn; using namespace mlir::spn::slp; -SLPTree::SLPTree(Operation* root) { +SLPTree::SLPTree(Operation* root, size_t width) : graph{width} { assert(root); llvm::StringMap> operationsByOpCode; for (auto& op : root->getBlock()->getOperations()) { operationsByOpCode[op.getName().getStringRef().str()].emplace_back(&op); } for (auto const& entry : operationsByOpCode) { - buildGraph(entry.getValue()); + buildGraph(entry.getValue(), graph); } } @@ -44,7 +44,7 @@ void SLPTree::traverseSubgraph(Operation* root) { }*/ -void SLPTree::buildGraph(std::vector const& values) { +void SLPTree::buildGraph(std::vector const& values, SLPNode& parentNode) { for (auto const& op : values) { std::cout << op->getName().getStringRef().str() << std::endl; } @@ -53,25 +53,25 @@ void SLPTree::buildGraph(std::vector const& values) { return; } // Create new node for values and add to graph - graph.emplace_back(values); + SLPNode currentNode = parentNode.addOperands(values); // Recursion call to grow graph further // 1. Commutative if (commutative(values)) { // A. Coarsening Mode + std::vector operands; for (auto const& operation : values) { - if (attachableOperands(operation)) { - std::vector operands; - for (auto operand : operation->getOperands()) { - operands.emplace_back(operand.getDefiningOp()); - } - buildGraph(operands); - } else { - + for (auto operand : operation->getOperands()) { + operands.emplace_back(operand.getDefiningOp()); } - + } + if (attachableOperands(values.front()->getName(), operands)) { + currentNode.addOperands(operands); + } else { + buildGraph(operands, currentNode); } } + } bool SLPTree::vectorizable(std::vector const& values) const { @@ -84,10 +84,10 @@ bool SLPTree::commutative(std::vector const& values) const { return true; } -bool SLPTree::attachableOperands(Operation* operation) const { +bool SLPTree::attachableOperands(OperationName const& currentOperation, std::vector const& operands) const { // TODO operands escape multi-node? - for (auto const& operand : operation->getOperands()) { - if (operand.getDefiningOp()->getName() != operation->getName()) { + for (auto const& operand : operands) { + if (operand->getName() != currentOperation) { return false; } } diff --git a/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp b/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp index 7dd92a03..15c1df0f 100644 --- a/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp +++ b/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp @@ -25,7 +25,7 @@ namespace { func.walk([](Operation* op) { if (auto query = dyn_cast(op)) { for (auto r : query.getRootNodes()) { - slp::SLPTree graph(r); + slp::SLPTree graph(r, 4); } } }); From f523062733e74922d7106dd7557d854ea1a3680f Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Mon, 11 Jan 2021 03:02:51 +0100 Subject: [PATCH 007/385] Better commutative check, better multi-node graph formation. --- .../Dialect/SPN/Analysis/SLP/SLPNode.h | 6 +++ .../Dialect/SPN/Analysis/SLP/SLPTree.h | 6 ++- mlir/include/Dialect/SPN/SPNOps.td | 2 +- mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp | 28 ++++++++++ mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 51 +++++++++++++------ 5 files changed, 75 insertions(+), 18 deletions(-) diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h index b8d000d3..ecf7211a 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h @@ -24,6 +24,12 @@ namespace mlir { SLPNode& addOperands(std::vector const& values); + std::vector const& getOperations(); + OperationName operationName(); + + bool isMultiNode() const; + bool attachable(std::vector const& otherOperations); + private: size_t const width; diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h index 9420793b..543c375a 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h @@ -36,7 +36,11 @@ namespace mlir { bool vectorizable(std::vector const& values) const; bool commutative(std::vector const& values) const; - bool attachableOperands(OperationName const& currentOperation, std::vector const& operands) const; + std::vector getOperands(std::vector const& values) const; + std::vector getOperands(Operation* value) const; + bool attachableOperands(OperationName const& currentOperation, OperandRange operands) const; + + void reorderOperands(SLPNode& node); SLPNode graph; diff --git a/mlir/include/Dialect/SPN/SPNOps.td b/mlir/include/Dialect/SPN/SPNOps.td index 5cff217d..19830170 100644 --- a/mlir/include/Dialect/SPN/SPNOps.td +++ b/mlir/include/Dialect/SPN/SPNOps.td @@ -213,7 +213,7 @@ def JointQuery : SPN_Op<"joint_query", let description = [{ Represents a joint probability computation by bottom-up evaluation of the SPN-graph. - The single region associated with this operation represents the the SPN graph. + The single region associated with this operation represents the SPN graph. }]; let arguments = (ins UI32Attr:$numFeatures, diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp index 67165865..afcd60d6 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp @@ -18,6 +18,34 @@ SLPNode::SLPNode(std::vector const& values) : width{values.size()}, } SLPNode& SLPNode::addOperands(std::vector const& values) { + //assert(values.size() == width); + if (attachable(values)) { + operations.insert(std::end(operations), std::begin(values), std::end(values)); + return *this; + } operands.emplace_back(SLPNode{values}); return operands.back(); + +} + +std::vector const& SLPNode::getOperations() { + return operations; +} + +OperationName SLPNode::operationName() { + return operations.front()->getName(); +} + +bool SLPNode::isMultiNode() const { + return (operations.size() / width) > 1; +} + +bool SLPNode::attachable(std::vector const& otherOperations) { + // TODO operands escape multi-node? + if (operations.empty()) { + return true; + } + return std::all_of(std::begin(otherOperations), + std::end(otherOperations), + [&](auto const& operation) { return operation->getName() == operationName(); }); } diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index ce5f9554..f81255fd 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -7,6 +7,7 @@ #include "SPN/SPNInterfaces.h" #include +#include #include "llvm/ADT/StringMap.h" using namespace mlir; @@ -58,18 +59,18 @@ void SLPTree::buildGraph(std::vector const& values, SLPNode& parentN // 1. Commutative if (commutative(values)) { // A. Coarsening Mode - std::vector operands; for (auto const& operation : values) { - for (auto operand : operation->getOperands()) { - operands.emplace_back(operand.getDefiningOp()); - } + buildGraph(getOperands(operation), currentNode); } - if (attachableOperands(values.front()->getName(), operands)) { - currentNode.addOperands(operands); - } else { - buildGraph(operands, currentNode); + // B. Normal Mode: Finished building multi-node + if (currentNode.isMultiNode()) { + reorderOperands(currentNode); + // TODO buildGraph() needed for operands? Currently don't think so because of smarter node.addOperands() handling. } - + } + // 2. Non-Commutative + else { + buildGraph(getOperands(values), currentNode); } } @@ -80,16 +81,34 @@ bool SLPTree::vectorizable(std::vector const& values) const { } bool SLPTree::commutative(std::vector const& values) const { - // TODO - return true; + return values.front()->hasTrait(); } -bool SLPTree::attachableOperands(OperationName const& currentOperation, std::vector const& operands) const { +bool SLPTree::attachableOperands(OperationName const& currentOperation, OperandRange operands) const { // TODO operands escape multi-node? - for (auto const& operand : operands) { - if (operand->getName() != currentOperation) { - return false; + return std::all_of(std::begin(operands), + std::end(operands), + [&](auto const& operand) { return operand.getDefiningOp()->getName() == currentOperation; }); +} + +std::vector SLPTree::getOperands(std::vector const& values) const { + std::vector operands; + for (auto const& operation : values) { + for (auto operand : operation->getOperands()) { + operands.emplace_back(operand.getDefiningOp()); } } - return true; + return operands; +} + +std::vector SLPTree::getOperands(Operation* operation) const { + std::vector operands; + for (auto operand : operation->getOperands()) { + operands.emplace_back(operand.getDefiningOp()); + } + return operands; +} + +void SLPTree::reorderOperands(SLPNode& node) { + // TODO } \ No newline at end of file From de6d661fa0ec104d7426fdb2f6217cd25edb3728 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Mon, 11 Jan 2021 04:41:51 +0100 Subject: [PATCH 008/385] Trait attempt (WIP). --- mlir/include/Dialect/SPN/SPNOps.h | 9 +++++++++ mlir/include/Dialect/SPN/SPNOps.td | 5 +++++ 2 files changed, 14 insertions(+) diff --git a/mlir/include/Dialect/SPN/SPNOps.h b/mlir/include/Dialect/SPN/SPNOps.h index c37fd81d..807e73bc 100644 --- a/mlir/include/Dialect/SPN/SPNOps.h +++ b/mlir/include/Dialect/SPN/SPNOps.h @@ -13,6 +13,15 @@ #include "SPN/SPNInterfaces.h" #include "SPN/SPNEnums.h" +namespace mlir { + namespace spn { +/* + template + class Vectorizable : public OpTrait::TraitBase { + };*/ + } +} + #define GET_OP_CLASSES #include "SPN/SPNOps.h.inc" diff --git a/mlir/include/Dialect/SPN/SPNOps.td b/mlir/include/Dialect/SPN/SPNOps.td index 19830170..93664020 100644 --- a/mlir/include/Dialect/SPN/SPNOps.td +++ b/mlir/include/Dialect/SPN/SPNOps.td @@ -6,6 +6,11 @@ include "SPNInterfaces.td" include "mlir/Interfaces/SideEffectInterfaces.td" include "mlir/IR/RegionKindInterface.td" +/// +/// Trait which can be attached to operations to mark them as vectorizable (such as sums or products). +/// +//def Vectorizable : NativeOpTrait<"Vectorizable">; + /// /// Abstract base class of n-ary arithmetic operations /// From 4b64af50f538233b8b8563748f4220de25a3537e Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Mon, 11 Jan 2021 23:39:29 +0100 Subject: [PATCH 009/385] Proper Vectorizable trait handling. --- mlir/include/Dialect/SPN/SPNOpTraits.h | 21 +++++++++++++++++++ mlir/include/Dialect/SPN/SPNOpTraits.td | 11 ++++++++++ mlir/include/Dialect/SPN/SPNOps.h | 10 +-------- mlir/include/Dialect/SPN/SPNOps.td | 8 ++----- mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 20 +++++++----------- 5 files changed, 43 insertions(+), 27 deletions(-) create mode 100644 mlir/include/Dialect/SPN/SPNOpTraits.h create mode 100644 mlir/include/Dialect/SPN/SPNOpTraits.td diff --git a/mlir/include/Dialect/SPN/SPNOpTraits.h b/mlir/include/Dialect/SPN/SPNOpTraits.h new file mode 100644 index 00000000..2e1b6c4a --- /dev/null +++ b/mlir/include/Dialect/SPN/SPNOpTraits.h @@ -0,0 +1,21 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#ifndef SPNC_COMPILER_SRC_CODEGEN_MLIR_INCLUDE_SPN_SPNOPTRAITS_H +#define SPNC_COMPILER_SRC_CODEGEN_MLIR_INCLUDE_SPN_SPNOPTRAITS_H + +#include "mlir/IR/OpDefinition.h" + +namespace mlir { + namespace OpTrait { + namespace spn { + + template + class Vectorizable : public TraitBase {}; + } + } +} + +#endif //SPNC_COMPILER_SRC_CODEGEN_MLIR_INCLUDE_SPN_SPNOPTRAITS_H diff --git a/mlir/include/Dialect/SPN/SPNOpTraits.td b/mlir/include/Dialect/SPN/SPNOpTraits.td new file mode 100644 index 00000000..ca8b9d6d --- /dev/null +++ b/mlir/include/Dialect/SPN/SPNOpTraits.td @@ -0,0 +1,11 @@ +#ifndef SPNOpTraits +#define SPNOpTraits + +include "SPNDialect.td" + +/// +/// Trait which can be attached to operations to mark them as vectorizable (such as sums or products). +/// +def Vectorizable : NativeOpTrait<"spn::Vectorizable">; + +#endif // SPNOpTraits \ No newline at end of file diff --git a/mlir/include/Dialect/SPN/SPNOps.h b/mlir/include/Dialect/SPN/SPNOps.h index 807e73bc..2057128b 100644 --- a/mlir/include/Dialect/SPN/SPNOps.h +++ b/mlir/include/Dialect/SPN/SPNOps.h @@ -11,17 +11,9 @@ #include "mlir/Interfaces/SideEffectInterfaces.h" #include "mlir/IR/RegionKindInterface.h" #include "SPN/SPNInterfaces.h" +#include "SPN/SPNOpTraits.h" #include "SPN/SPNEnums.h" -namespace mlir { - namespace spn { -/* - template - class Vectorizable : public OpTrait::TraitBase { - };*/ - } -} - #define GET_OP_CLASSES #include "SPN/SPNOps.h.inc" diff --git a/mlir/include/Dialect/SPN/SPNOps.td b/mlir/include/Dialect/SPN/SPNOps.td index 93664020..4a88e288 100644 --- a/mlir/include/Dialect/SPN/SPNOps.td +++ b/mlir/include/Dialect/SPN/SPNOps.td @@ -3,19 +3,15 @@ include "SPNDialect.td" include "SPNInterfaces.td" +include "SPNOpTraits.td" include "mlir/Interfaces/SideEffectInterfaces.td" include "mlir/IR/RegionKindInterface.td" -/// -/// Trait which can be attached to operations to mark them as vectorizable (such as sums or products). -/// -//def Vectorizable : NativeOpTrait<"Vectorizable">; - /// /// Abstract base class of n-ary arithmetic operations /// class SPN_NAry_Op : SPN_Op]> { + [NoSideEffect, Commutative, Vectorizable, SameOperandsAndResultType, DeclareOpInterfaceMethods]> { /// Operands: Child nodes /// opcount: Number of child nodes let arguments = (ins Variadic: $operands); diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index f81255fd..faecb7c3 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -4,11 +4,10 @@ // #include "SPN/Analysis/SLP/SLPTree.h" -#include "SPN/SPNInterfaces.h" +#include "SPN/SPNOpTraits.h" #include #include -#include "llvm/ADT/StringMap.h" using namespace mlir; using namespace mlir::spn; @@ -20,6 +19,7 @@ SLPTree::SLPTree(Operation* root, size_t width) : graph{width} { for (auto& op : root->getBlock()->getOperations()) { operationsByOpCode[op.getName().getStringRef().str()].emplace_back(&op); } + // TODO compute seeds in a proper fashion for (auto const& entry : operationsByOpCode) { buildGraph(entry.getValue(), graph); } @@ -76,19 +76,15 @@ void SLPTree::buildGraph(std::vector const& values, SLPNode& parentN } bool SLPTree::vectorizable(std::vector const& values) const { - // TODO - return true; + return std::all_of(std::begin(values), std::end(values), [&](Operation* operation) { + return operation->hasTrait(); + }); } bool SLPTree::commutative(std::vector const& values) const { - return values.front()->hasTrait(); -} - -bool SLPTree::attachableOperands(OperationName const& currentOperation, OperandRange operands) const { - // TODO operands escape multi-node? - return std::all_of(std::begin(operands), - std::end(operands), - [&](auto const& operand) { return operand.getDefiningOp()->getName() == currentOperation; }); + return std::all_of(std::begin(values), std::end(values), [&](Operation* operation) { + return operation->hasTrait(); + }); } std::vector SLPTree::getOperands(std::vector const& values) const { From fa84c6f9a64cf944bc7c4845b08a8bd3862906a4 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Tue, 12 Jan 2021 03:33:07 +0100 Subject: [PATCH 010/385] Node refactoring. --- .../Dialect/SPN/Analysis/SLP/SLPNode.h | 12 +++-- .../Dialect/SPN/Analysis/SLP/SLPTree.h | 8 +++- mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp | 46 +++++++++++++------ mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 24 ++++++++-- 4 files changed, 66 insertions(+), 24 deletions(-) diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h index ecf7211a..4b04c2dc 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h @@ -18,14 +18,15 @@ namespace mlir { public: - explicit SLPNode(size_t const& width); - explicit SLPNode(std::vector const& values); SLPNode& addOperands(std::vector const& values); + std::vector& getOperands(); + SLPNode& getOperand(size_t index); - std::vector const& getOperations(); - OperationName operationName(); + std::vector getLane(size_t index); + Operation* getOperation(size_t lane, size_t index); + OperationName const& name(); bool isMultiNode() const; bool attachable(std::vector const& otherOperations); @@ -33,9 +34,10 @@ namespace mlir { private: size_t const width; + OperationName const operationName; /// List of operations that can be executed in any order/in parallel when width > 1. - std::vector operations; + std::vector> lanes; std::vector operands; }; diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h index 543c375a..0a0f7688 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h @@ -40,9 +40,15 @@ namespace mlir { std::vector getOperands(Operation* value) const; bool attachableOperands(OperationName const& currentOperation, OperandRange operands) const; + enum MODE { + CONST, LOAD, OPCODE + }; + + SLPTree::MODE modeFromOperation(Operation const* operation) const; + void reorderOperands(SLPNode& node); - SLPNode graph; + std::vector graphs; }; } diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp index afcd60d6..405c3a05 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp @@ -11,16 +11,18 @@ using namespace mlir; using namespace mlir::spn; using namespace mlir::spn::slp; -SLPNode::SLPNode(size_t const& width) : width{width}, operations{}, operands{} { -} - -SLPNode::SLPNode(std::vector const& values) : width{values.size()}, operations{values}, operands{} { +SLPNode::SLPNode(std::vector const& values) : width{values.size()}, + operationName{values.front()->getName()}, lanes{}, + operands{} { + lanes.emplace_back(values); } SLPNode& SLPNode::addOperands(std::vector const& values) { - //assert(values.size() == width); + // If the operations are attachable (i.e. same opcode), insert them into this node (forming a multinode). if (attachable(values)) { - operations.insert(std::end(operations), std::begin(values), std::end(values)); + for (size_t lane = 0; lane < values.size(); ++lane) { + lanes.at(lane).emplace_back(values.at(lane)); + } return *this; } operands.emplace_back(SLPNode{values}); @@ -28,24 +30,38 @@ SLPNode& SLPNode::addOperands(std::vector const& values) { } -std::vector const& SLPNode::getOperations() { - return operations; +std::vector& SLPNode::getOperands() { + return operands; +} + +SLPNode& SLPNode::getOperand(size_t index) { + return operands.at(index); +} + +std::vector SLPNode::getLane(size_t index) { + std::vector lane; + for (auto const& operations : lanes) { + lane.emplace_back(operations.at(index)); + } + return lane; } -OperationName SLPNode::operationName() { - return operations.front()->getName(); +Operation* SLPNode::getOperation(size_t lane, size_t index) { + return lanes.at(lane).at(index); +} + +OperationName const& SLPNode::name() { + return operationName; } bool SLPNode::isMultiNode() const { - return (operations.size() / width) > 1; + return lanes.front().size() > 1; } bool SLPNode::attachable(std::vector const& otherOperations) { // TODO operands escape multi-node? - if (operations.empty()) { - return true; - } + assert(lanes.size() == otherOperations.size()); return std::all_of(std::begin(otherOperations), std::end(otherOperations), - [&](auto const& operation) { return operation->getName() == operationName(); }); + [&](auto const& operation) { return operation->getName() == operationName; }); } diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index faecb7c3..80d80143 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -4,6 +4,7 @@ // #include "SPN/Analysis/SLP/SLPTree.h" +#include "SPN/SPNOps.h" #include "SPN/SPNOpTraits.h" #include @@ -13,7 +14,7 @@ using namespace mlir; using namespace mlir::spn; using namespace mlir::spn::slp; -SLPTree::SLPTree(Operation* root, size_t width) : graph{width} { +SLPTree::SLPTree(Operation* root, size_t width) : graphs{} { assert(root); llvm::StringMap> operationsByOpCode; for (auto& op : root->getBlock()->getOperations()) { @@ -21,7 +22,8 @@ SLPTree::SLPTree(Operation* root, size_t width) : graph{width} { } // TODO compute seeds in a proper fashion for (auto const& entry : operationsByOpCode) { - buildGraph(entry.getValue(), graph); + SLPNode rootNode{entry.getValue()}; + buildGraph(entry.getValue(), rootNode); } } @@ -105,6 +107,22 @@ std::vector SLPTree::getOperands(Operation* operation) const { return operands; } +SLPTree::MODE SLPTree::modeFromOperation(Operation const* operation) const { + if (dyn_cast(operation)) { + return MODE::CONST; + } else if (dyn_cast(operation) || dyn_cast(operation)) { + return MODE::LOAD; + } + return MODE::OPCODE; +} + void SLPTree::reorderOperands(SLPNode& node) { - // TODO + std::vector finalOrder; + std::vector mode; + // 1. Strip first lane + for (size_t i = 0; i < node.getOperands().size(); ++i) { + auto& operand = node.getOperand(i); + finalOrder.emplace_back(operand); + //mode.emplace_back(modeFromOperation(node.getOperation(i))); + } } \ No newline at end of file From 178a47726a9cdfacdb445ea5e905b3fb4abc57e2 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Tue, 12 Jan 2021 03:54:45 +0100 Subject: [PATCH 011/385] Operand reordering skeleton. --- .../Dialect/SPN/Analysis/SLP/SLPNode.h | 3 +- .../Dialect/SPN/Analysis/SLP/SLPTree.h | 8 +++-- mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp | 8 +++-- mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 31 ++++++++++++++++--- 4 files changed, 39 insertions(+), 11 deletions(-) diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h index 4b04c2dc..852224dc 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h @@ -29,6 +29,7 @@ namespace mlir { OperationName const& name(); bool isMultiNode() const; + size_t numLanes() const; bool attachable(std::vector const& otherOperations); private: @@ -36,7 +37,7 @@ namespace mlir { size_t const width; OperationName const operationName; - /// List of operations that can be executed in any order/in parallel when width > 1. + /// Stores lists of operations for each lane for multinode use cases. std::vector> lanes; std::vector operands; diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h index 0a0f7688..e2ff14fc 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h @@ -38,18 +38,20 @@ namespace mlir { bool commutative(std::vector const& values) const; std::vector getOperands(std::vector const& values) const; std::vector getOperands(Operation* value) const; - bool attachableOperands(OperationName const& currentOperation, OperandRange operands) const; enum MODE { - CONST, LOAD, OPCODE + CONST, LOAD, OPCODE, FAILED, SPLAT }; SLPTree::MODE modeFromOperation(Operation const* operation) const; - void reorderOperands(SLPNode& node); + void reorderOperands(SLPNode& multinode); std::vector graphs; + std::pair getBest(SLPTree::MODE const& mode, + SLPNode const& last, + std::vector const& candidates) const; }; } } diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp index 405c3a05..e00f2d5f 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp @@ -38,10 +38,10 @@ SLPNode& SLPNode::getOperand(size_t index) { return operands.at(index); } -std::vector SLPNode::getLane(size_t index) { +std::vector SLPNode::getLane(size_t laneIndex) { std::vector lane; for (auto const& operations : lanes) { - lane.emplace_back(operations.at(index)); + lane.emplace_back(operations.at(laneIndex)); } return lane; } @@ -58,6 +58,10 @@ bool SLPNode::isMultiNode() const { return lanes.front().size() > 1; } +size_t SLPNode::numLanes() const { + return width; +} + bool SLPNode::attachable(std::vector const& otherOperations) { // TODO operands escape multi-node? assert(lanes.size() == otherOperations.size()); diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index 80d80143..3f5f307b 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -116,13 +116,34 @@ SLPTree::MODE SLPTree::modeFromOperation(Operation const* operation) const { return MODE::OPCODE; } -void SLPTree::reorderOperands(SLPNode& node) { +void SLPTree::reorderOperands(SLPNode& multinode) { + assert(multinode.isMultiNode()); std::vector finalOrder; std::vector mode; // 1. Strip first lane - for (size_t i = 0; i < node.getOperands().size(); ++i) { - auto& operand = node.getOperand(i); + for (size_t i = 0; i < multinode.getOperands().size(); ++i) { + auto& operand = multinode.getOperand(i); finalOrder.emplace_back(operand); - //mode.emplace_back(modeFromOperation(node.getOperation(i))); + mode.emplace_back(modeFromOperation(multinode.getOperation(0, i))); } -} \ No newline at end of file + + // 2. For all other lanes, find best candidate + for (size_t lane = 1; lane < multinode.numLanes(); ++lane) { + std::vector candidates = multinode.getLane(lane); + // Look for a matching candidate + for (size_t i = 0; i < multinode.getOperands().size(); ++i) { + // Skip if we can't vectorize + if (mode.at(i) == MODE::FAILED) { + continue; + } + auto const& last = finalOrder.at(i); + auto const& bestResult = getBest(mode.at(i), last, candidates); + } + } +} + +std::pair SLPTree::getBest(SLPTree::MODE const& mode, + SLPNode const& last, + std::vector const& candidates) const { + return {last, mode}; +} From 9e362b2387adb792618cd01bc557134c44b8146b Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Tue, 12 Jan 2021 04:04:14 +0100 Subject: [PATCH 012/385] More reordering. --- mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h | 9 +++++++++ mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h | 2 +- mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 14 +++++++++++++- 3 files changed, 23 insertions(+), 2 deletions(-) diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h index 852224dc..dcc8d1c1 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h @@ -32,6 +32,15 @@ namespace mlir { size_t numLanes() const; bool attachable(std::vector const& otherOperations); + friend bool operator==(SLPNode const& lhs, SLPNode const& rhs) { + return std::tie(lhs.width, lhs.operationName, lhs.lanes, lhs.operands) + == std::tie(rhs.width, rhs.operationName, rhs.lanes, rhs.operands); + } + + friend bool operator!=(SLPNode const& lhs, SLPNode const& rhs) { + return !(lhs == rhs); + } + private: size_t const width; diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h index e2ff14fc..44e3150c 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h @@ -45,7 +45,7 @@ namespace mlir { SLPTree::MODE modeFromOperation(Operation const* operation) const; - void reorderOperands(SLPNode& multinode); + std::vector reorderOperands(SLPNode& multinode); std::vector graphs; diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index 3f5f307b..e5ed82d8 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -116,7 +116,7 @@ SLPTree::MODE SLPTree::modeFromOperation(Operation const* operation) const { return MODE::OPCODE; } -void SLPTree::reorderOperands(SLPNode& multinode) { +std::vector SLPTree::reorderOperands(SLPNode& multinode) { assert(multinode.isMultiNode()); std::vector finalOrder; std::vector mode; @@ -138,12 +138,24 @@ void SLPTree::reorderOperands(SLPNode& multinode) { } auto const& last = finalOrder.at(i); auto const& bestResult = getBest(mode.at(i), last, candidates); + + // Update output + // TODO: funky two dimensional stuff + finalOrder.emplace_back(bestResult.first); + + // Detect SPLAT mode + if (i == 1 && bestResult.first == last) { + mode.at(i) = MODE::SPLAT; + } + } } + return finalOrder; } std::pair SLPTree::getBest(SLPTree::MODE const& mode, SLPNode const& last, std::vector const& candidates) const { + // TODO return {last, mode}; } From ce0e94e161179ccc627335b2112ebbb57ba13d52 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Tue, 12 Jan 2021 05:27:19 +0100 Subject: [PATCH 013/385] Some comments. --- mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h index 44e3150c..6da8a82c 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h @@ -40,7 +40,16 @@ namespace mlir { std::vector getOperands(Operation* value) const; enum MODE { - CONST, LOAD, OPCODE, FAILED, SPLAT + // look for a constant + CONST, + // look for a consecutive load to that in the previous lane + LOAD, + // look for an operation of the same opcode + OPCODE, + // look for the exact same operation + SPLAT, + // vectorization has failed, give higher priority to others + FAILED }; SLPTree::MODE modeFromOperation(Operation const* operation) const; From 55f2de996766f1e98f2e2af5090deaddb8d7e889 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Wed, 20 Jan 2021 21:48:52 +0100 Subject: [PATCH 014/385] WIP node refactoring. --- .../Dialect/SPN/Analysis/SLP/SLPNode.h | 22 +++--- .../Dialect/SPN/Analysis/SLP/SLPTree.h | 8 +- mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp | 65 ++++++++++------- mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 73 +++++++++++++------ 4 files changed, 105 insertions(+), 63 deletions(-) diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h index dcc8d1c1..58e91458 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h @@ -20,21 +20,21 @@ namespace mlir { explicit SLPNode(std::vector const& values); - SLPNode& addOperands(std::vector const& values); - std::vector& getOperands(); - SLPNode& getOperand(size_t index); + void addOperands(std::vector> const& operandsPerLane); + void addOperands(std::vector const& operations, size_t const& lane); + std::vector& getOperands(size_t lane); - std::vector getLane(size_t index); + std::vector& getLane(size_t lane); Operation* getOperation(size_t lane, size_t index); OperationName const& name(); bool isMultiNode() const; size_t numLanes() const; - bool attachable(std::vector const& otherOperations); + bool attachable(std::vector> const& operations); friend bool operator==(SLPNode const& lhs, SLPNode const& rhs) { - return std::tie(lhs.width, lhs.operationName, lhs.lanes, lhs.operands) - == std::tie(rhs.width, rhs.operationName, rhs.lanes, rhs.operands); + return std::tie(lhs.operationName, lhs.lanes, lhs.operands) + == std::tie(rhs.operationName, rhs.lanes, rhs.operands); } friend bool operator!=(SLPNode const& lhs, SLPNode const& rhs) { @@ -43,12 +43,14 @@ namespace mlir { private: - size_t const width; OperationName const operationName; - /// Stores lists of operations for each lane for multinode use cases. + /// Stores lanes as lists of operations. An inner vector (i.e. a lane) only contains more than one operation + /// if this node is a multinode. std::vector> lanes; - std::vector operands; + + /// A list of operands for each lane. + std::vector> operands; }; } diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h index 6da8a82c..58ac1cec 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h @@ -32,10 +32,10 @@ namespace mlir { private: - void buildGraph(std::vector const& values, SLPNode& parentNode); + void buildGraph(std::vector const& operations, SLPNode& parentNode); - bool vectorizable(std::vector const& values) const; - bool commutative(std::vector const& values) const; + bool vectorizable(std::vector const& operations) const; + bool commutative(std::vector const& operations) const; std::vector getOperands(std::vector const& values) const; std::vector getOperands(Operation* value) const; @@ -60,7 +60,7 @@ namespace mlir { std::pair getBest(SLPTree::MODE const& mode, SLPNode const& last, - std::vector const& candidates) const; + std::vector const& candidates) const; }; } } diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp index e00f2d5f..3c2e676a 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp @@ -11,39 +11,46 @@ using namespace mlir; using namespace mlir::spn; using namespace mlir::spn::slp; -SLPNode::SLPNode(std::vector const& values) : width{values.size()}, - operationName{values.front()->getName()}, lanes{}, - operands{} { - lanes.emplace_back(values); +SLPNode::SLPNode(std::vector const& operations) : operationName{operations.front()->getName()}, + lanes{operations.size()}, + operands{operations.size()} { + for (size_t i = 0; i < operations.size(); ++i) { + lanes.at(i).emplace_back(operations.at(i)); + } } -SLPNode& SLPNode::addOperands(std::vector const& values) { +void SLPNode::addOperands(std::vector> const& operandsPerLane) { + assert(operandsPerLane.size() == numLanes()); // If the operations are attachable (i.e. same opcode), insert them into this node (forming a multinode). - if (attachable(values)) { - for (size_t lane = 0; lane < values.size(); ++lane) { - lanes.at(lane).emplace_back(values.at(lane)); + if (attachable(operandsPerLane)) { + for (size_t lane = 0; lane < numLanes(); ++lane) { + lanes.at(lane).insert(std::end(lanes.at(lane)), + std::begin(operandsPerLane.at(lane)), + std::end(operandsPerLane.at(lane))); + } + } + // Otherwise, add don't modify this node's operations and add the operands as true operands. + else { + for (size_t lane = 0; lane < numLanes(); ++lane) { + addOperands(operandsPerLane.at(lane), lane); } - return *this; } - operands.emplace_back(SLPNode{values}); - return operands.back(); } -std::vector& SLPNode::getOperands() { - return operands; +void SLPNode::addOperands(std::vector const& operations, size_t const& lane) { + assert(operations.size() == lanes.at(lane).back()->getNumOperands()); + for (auto const& operand : operations) { + operands.at(lane).emplace_back(SLPNode{{operand}}); + } } -SLPNode& SLPNode::getOperand(size_t index) { - return operands.at(index); +std::vector& SLPNode::getOperands(size_t lane) { + return operands.at(lane); } -std::vector SLPNode::getLane(size_t laneIndex) { - std::vector lane; - for (auto const& operations : lanes) { - lane.emplace_back(operations.at(laneIndex)); - } - return lane; +std::vector& SLPNode::getLane(size_t lane) { + return lanes.at(lane); } Operation* SLPNode::getOperation(size_t lane, size_t index) { @@ -59,13 +66,17 @@ bool SLPNode::isMultiNode() const { } size_t SLPNode::numLanes() const { - return width; + return lanes.size(); } -bool SLPNode::attachable(std::vector const& otherOperations) { +bool SLPNode::attachable(std::vector> const& operations) { // TODO operands escape multi-node? - assert(lanes.size() == otherOperations.size()); - return std::all_of(std::begin(otherOperations), - std::end(otherOperations), - [&](auto const& operation) { return operation->getName() == operationName; }); + for (auto const& laneOperands : operations) { + if (std::any_of(std::begin(laneOperands), + std::end(laneOperands), + [&](auto const& operandOperation) { return operandOperation->getName() != operationName; })) { + return false; + } + } + return true; } diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index e5ed82d8..3648950f 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -17,9 +17,18 @@ using namespace mlir::spn::slp; SLPTree::SLPTree(Operation* root, size_t width) : graphs{} { assert(root); llvm::StringMap> operationsByOpCode; + std::set operations; for (auto& op : root->getBlock()->getOperations()) { operationsByOpCode[op.getName().getStringRef().str()].emplace_back(&op); + operations.insert(&op); + for(auto const& operand : op.getOperands()) { + if(operations.find(operand.getDefiningOp()) != std::end(operations)) { + std::cout << "it's not a tree!" << std::endl; + return; + } + } } + // TODO compute seeds in a proper fashion for (auto const& entry : operationsByOpCode) { SLPNode rootNode{entry.getValue()}; @@ -47,44 +56,52 @@ void SLPTree::traverseSubgraph(Operation* root) { }*/ -void SLPTree::buildGraph(std::vector const& values, SLPNode& parentNode) { - for (auto const& op : values) { +void SLPTree::buildGraph(std::vector const& operations, SLPNode& parentNode) { + for (auto const& op : operations) { std::cout << op->getName().getStringRef().str() << std::endl; } // Stop growing graph - if (!vectorizable(values)) { + if (!vectorizable(operations)) { return; } // Create new node for values and add to graph - SLPNode currentNode = parentNode.addOperands(values); + SLPNode* currentNode = &parentNode; // Recursion call to grow graph further // 1. Commutative - if (commutative(values)) { + if (commutative(operations)) { // A. Coarsening Mode - for (auto const& operation : values) { - buildGraph(getOperands(operation), currentNode); + for (auto const& operation : operations) { + buildGraph(getOperands(operation), *currentNode); } // B. Normal Mode: Finished building multi-node - if (currentNode.isMultiNode()) { - reorderOperands(currentNode); - // TODO buildGraph() needed for operands? Currently don't think so because of smarter node.addOperands() handling. + if (currentNode->isMultiNode()) { + reorderOperands(*currentNode); + // TODO buildGraph() } } // 2. Non-Commutative else { - buildGraph(getOperands(values), currentNode); + buildGraph(getOperands(operations), *currentNode); } } -bool SLPTree::vectorizable(std::vector const& values) const { - return std::all_of(std::begin(values), std::end(values), [&](Operation* operation) { - return operation->hasTrait(); - }); +/// Checks if the given operations are vectorizable. Operations are vectorizable iff the SPN dialect says they're +/// vectorizable and they all share the same opcode. +/// \param operations The potentially vectorizable operations. +/// \return True if the operations can be vectorized, otherwise false. +bool SLPTree::vectorizable(std::vector const& operations) const { + for (size_t i = 0; i < operations.size(); ++i) { + if (!operations.at(i)->hasTrait() + || (i > 0 && operations.at(i)->getName() != operations.front()->getName())) { + return false; + } + } + return true; } -bool SLPTree::commutative(std::vector const& values) const { - return std::all_of(std::begin(values), std::end(values), [&](Operation* operation) { +bool SLPTree::commutative(std::vector const& operations) const { + return std::all_of(std::begin(operations), std::end(operations), [&](Operation* operation) { return operation->hasTrait(); }); } @@ -119,7 +136,7 @@ SLPTree::MODE SLPTree::modeFromOperation(Operation const* operation) const { std::vector SLPTree::reorderOperands(SLPNode& multinode) { assert(multinode.isMultiNode()); std::vector finalOrder; - std::vector mode; + /*std::vector mode; // 1. Strip first lane for (size_t i = 0; i < multinode.getOperands().size(); ++i) { auto& operand = multinode.getOperand(i); @@ -129,7 +146,7 @@ std::vector SLPTree::reorderOperands(SLPNode& multinode) { // 2. For all other lanes, find best candidate for (size_t lane = 1; lane < multinode.numLanes(); ++lane) { - std::vector candidates = multinode.getLane(lane); + std::vector candidates = multinode.getLane(lane); // Look for a matching candidate for (size_t i = 0; i < multinode.getOperands().size(); ++i) { // Skip if we can't vectorize @@ -149,13 +166,25 @@ std::vector SLPTree::reorderOperands(SLPNode& multinode) { } } - } + }*/ return finalOrder; } std::pair SLPTree::getBest(SLPTree::MODE const& mode, SLPNode const& last, - std::vector const& candidates) const { - // TODO + std::vector const& candidates) const { + Operation* best; + + if (mode == MODE::FAILED) { + // Don't select now, let others choose first + // ... + } else if (mode == MODE::SPLAT) { + // Look for other splat candidates + for (auto const& value : candidates) { + if (*value == last) { + + } + } + } return {last, mode}; } From b35953cbff374f25e43a3f5df22a52af807b23e8 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Fri, 22 Jan 2021 01:07:04 +0100 Subject: [PATCH 015/385] GetBest() and GetLookAheadScore(). --- .../Dialect/SPN/Analysis/SLP/SLPNode.h | 2 +- .../Dialect/SPN/Analysis/SLP/SLPTree.h | 11 ++- mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp | 6 +- mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 81 ++++++++++++++++--- .../Dialect/SPN/Passes/SPNVectorization.cpp | 2 +- 5 files changed, 83 insertions(+), 19 deletions(-) diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h index 58e91458..734fbcbf 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h @@ -21,7 +21,7 @@ namespace mlir { explicit SLPNode(std::vector const& values); void addOperands(std::vector> const& operandsPerLane); - void addOperands(std::vector const& operations, size_t const& lane); + void addOperandsToLane(std::vector const& operations, size_t const& lane); std::vector& getOperands(size_t lane); std::vector& getLane(size_t lane); diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h index 58ac1cec..69174bc9 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h @@ -28,7 +28,7 @@ namespace mlir { /// Constructor, initialize analysis. /// \param root Root node of a (sub-)graph or query operation. /// \param width The target width of the SLP vectors. - explicit SLPTree(Operation* op, size_t width); + explicit SLPTree(Operation* op, size_t width, size_t maxLookAhead); private: @@ -58,9 +58,12 @@ namespace mlir { std::vector graphs; - std::pair getBest(SLPTree::MODE const& mode, - SLPNode const& last, - std::vector const& candidates) const; + size_t const maxLookAhead; + + std::pair getBest(SLPTree::MODE& mode, + SLPNode& last, + std::vector& candidates) const; + int getLookAheadScore(SLPNode& last, SLPNode& candidate, size_t const& maxLevel) const; }; } } diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp index 3c2e676a..1fec5eb7 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp @@ -29,16 +29,16 @@ void SLPNode::addOperands(std::vector> const& operandsPe std::end(operandsPerLane.at(lane))); } } - // Otherwise, add don't modify this node's operations and add the operands as true operands. + // Otherwise, don't modify this node's operations and add the operands as true operands. else { for (size_t lane = 0; lane < numLanes(); ++lane) { - addOperands(operandsPerLane.at(lane), lane); + addOperandsToLane(operandsPerLane.at(lane), lane); } } } -void SLPNode::addOperands(std::vector const& operations, size_t const& lane) { +void SLPNode::addOperandsToLane(std::vector const& operations, size_t const& lane) { assert(operations.size() == lanes.at(lane).back()->getNumOperands()); for (auto const& operand : operations) { operands.at(lane).emplace_back(SLPNode{{operand}}); diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index 3648950f..779a01ea 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -14,15 +14,15 @@ using namespace mlir; using namespace mlir::spn; using namespace mlir::spn::slp; -SLPTree::SLPTree(Operation* root, size_t width) : graphs{} { +SLPTree::SLPTree(Operation* root, size_t width, size_t maxLookAhead) : graphs{}, maxLookAhead{maxLookAhead} { assert(root); llvm::StringMap> operationsByOpCode; std::set operations; for (auto& op : root->getBlock()->getOperations()) { operationsByOpCode[op.getName().getStringRef().str()].emplace_back(&op); operations.insert(&op); - for(auto const& operand : op.getOperands()) { - if(operations.find(operand.getDefiningOp()) != std::end(operations)) { + for (auto const& operand : op.getOperands()) { + if (operations.find(operand.getDefiningOp()) != std::end(operations)) { std::cout << "it's not a tree!" << std::endl; return; } @@ -170,21 +170,82 @@ std::vector SLPTree::reorderOperands(SLPNode& multinode) { return finalOrder; } -std::pair SLPTree::getBest(SLPTree::MODE const& mode, - SLPNode const& last, - std::vector const& candidates) const { - Operation* best; - +std::pair SLPTree::getBest(SLPTree::MODE& mode, + SLPNode& last, + std::vector& candidates) const { + SLPNode* best = nullptr; + std::vector bestCandidates; if (mode == MODE::FAILED) { // Don't select now, let others choose first - // ... + best = nullptr; } else if (mode == MODE::SPLAT) { // Look for other splat candidates for (auto const& value : candidates) { if (*value == last) { + best = value; + break; + } + } + } else { + // Default value + best = candidates.front(); + for (auto const& candidate : candidates) { + // We don't have consecutive memory accesses, therefore we're only interested in opcode comparisons. + if (candidate->name() == last.name()) { + bestCandidates.emplace_back(candidate); + } + } + // 1. If we have a trivial solution, use it + if (bestCandidates.empty()) { + mode = MODE::FAILED; + } + // Single match + else if (bestCandidates.size() == 1) { + best = bestCandidates.front(); + } + // 2. Look-ahead to choose from best candidates + else { + if (mode == MODE::OPCODE) { + // Look-ahead on various levels + for (size_t level = 1; level <= maxLookAhead; ++level) { + // Best is the candidate with max score + int bestScore = 0; + std::set scores; + for (auto const& candidate : bestCandidates) { + // Get the look-ahead score + auto const& score = getLookAheadScore(last, *candidate, level); + if (scores.empty() || score > bestScore) { + best = candidate; + bestScore = score; + scores.insert(score); + } + } + // If found best at level don't go deeper + if (best != nullptr && scores.size() > 1) { + break; + } + } + } + } + } + // Remove best from candidates + if (best != nullptr) { + candidates.erase(std::remove(std::begin(candidates), std::end(candidates), best), std::end(candidates)); + } + return {best, mode}; +} +int SLPTree::getLookAheadScore(SLPNode& last, SLPNode& candidate, size_t const& maxLevel) const { + if (maxLevel == 0 || last.name() != candidate.name()) { + return 0; + } + auto scoreSum = 0; + for (size_t lane = 0; lane < last.numLanes(); ++lane) { + for (auto& lastOperand : last.getOperands(lane)) { + for (auto& candidateOperand : candidate.getOperands(lane)) { + scoreSum += getLookAheadScore(lastOperand, candidateOperand, maxLevel - 1); } } } - return {last, mode}; + return scoreSum; } diff --git a/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp b/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp index 15c1df0f..f77c7133 100644 --- a/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp +++ b/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp @@ -25,7 +25,7 @@ namespace { func.walk([](Operation* op) { if (auto query = dyn_cast(op)) { for (auto r : query.getRootNodes()) { - slp::SLPTree graph(r, 4); + slp::SLPTree graph(r, 4, 3); } } }); From 4b3e0c55a3343d20b9336fd0a4720f4ee6cac015 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Fri, 22 Jan 2021 03:32:15 +0100 Subject: [PATCH 016/385] C++ refactoring. --- .../Dialect/SPN/Analysis/SLP/SLPMode.h | 30 +++++++++ .../Dialect/SPN/Analysis/SLP/SLPNode.h | 9 +-- .../Dialect/SPN/Analysis/SLP/SLPTree.h | 26 ++------ mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp | 15 +++-- mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 65 ++++++++++--------- 5 files changed, 86 insertions(+), 59 deletions(-) create mode 100644 mlir/include/Dialect/SPN/Analysis/SLP/SLPMode.h diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPMode.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPMode.h new file mode 100644 index 00000000..96a881a6 --- /dev/null +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPMode.h @@ -0,0 +1,30 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#ifndef SPNC_MLIR_DIALECTS_INCLUDE_DIALECT_SPN_ANALYSIS_SLP_SLPMODE_H +#define SPNC_MLIR_DIALECTS_INCLUDE_DIALECT_SPN_ANALYSIS_SLP_SLPMODE_H + +namespace mlir { + namespace spn { + namespace slp { + + enum MODE { + // look for a constant + CONST, + // look for a consecutive load to that in the previous lane + LOAD, + // look for an operation of the same opcode + OPCODE, + // look for the exact same operation + SPLAT, + // vectorization has failed, give higher priority to others + FAILED + }; + + } + } +} + +#endif //SPNC_MLIR_DIALECTS_INCLUDE_DIALECT_SPN_ANALYSIS_SLP_SLPMODE_H diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h index 734fbcbf..9b6386b0 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h @@ -18,15 +18,16 @@ namespace mlir { public: - explicit SLPNode(std::vector const& values); + explicit SLPNode(std::vector const& operations); + explicit SLPNode(Operation* operation); void addOperands(std::vector> const& operandsPerLane); void addOperandsToLane(std::vector const& operations, size_t const& lane); - std::vector& getOperands(size_t lane); + std::vector const& getOperands(size_t const& lane) const; + SLPNode const& getOperand(size_t const& lane, size_t const& index) const; - std::vector& getLane(size_t lane); Operation* getOperation(size_t lane, size_t index); - OperationName const& name(); + OperationName const& name() const; bool isMultiNode() const; size_t numLanes() const; diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h index 69174bc9..0ef98844 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h @@ -11,6 +11,7 @@ #include "llvm/ADT/SmallPtrSet.h" #include "llvm/ADT/StringMap.h" #include "SLPNode.h" +#include "SLPMode.h" #include #include @@ -39,31 +40,18 @@ namespace mlir { std::vector getOperands(std::vector const& values) const; std::vector getOperands(Operation* value) const; - enum MODE { - // look for a constant - CONST, - // look for a consecutive load to that in the previous lane - LOAD, - // look for an operation of the same opcode - OPCODE, - // look for the exact same operation - SPLAT, - // vectorization has failed, give higher priority to others - FAILED - }; + MODE modeFromOperation(Operation const* operation) const; - SLPTree::MODE modeFromOperation(Operation const* operation) const; - - std::vector reorderOperands(SLPNode& multinode); + std::vector> reorderOperands(SLPNode& multinode); std::vector graphs; size_t const maxLookAhead; - std::pair getBest(SLPTree::MODE& mode, - SLPNode& last, - std::vector& candidates) const; - int getLookAheadScore(SLPNode& last, SLPNode& candidate, size_t const& maxLevel) const; + std::pair, MODE> getBest(MODE& mode, + SLPNode const& last, + std::vector& candidates) const; + int getLookAheadScore(SLPNode const& last, SLPNode const& candidate, size_t const& maxLevel) const; }; } } diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp index 1fec5eb7..82c5e3c2 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp @@ -15,10 +15,15 @@ SLPNode::SLPNode(std::vector const& operations) : operationName{oper lanes{operations.size()}, operands{operations.size()} { for (size_t i = 0; i < operations.size(); ++i) { + assert(i == 0 || operations.at(i)->getName() == operationName); lanes.at(i).emplace_back(operations.at(i)); } } +SLPNode::SLPNode(Operation* operation) : operationName{operation->getName()}, + lanes{}, + operands{} {} + void SLPNode::addOperands(std::vector> const& operandsPerLane) { assert(operandsPerLane.size() == numLanes()); // If the operations are attachable (i.e. same opcode), insert them into this node (forming a multinode). @@ -41,23 +46,23 @@ void SLPNode::addOperands(std::vector> const& operandsPe void SLPNode::addOperandsToLane(std::vector const& operations, size_t const& lane) { assert(operations.size() == lanes.at(lane).back()->getNumOperands()); for (auto const& operand : operations) { - operands.at(lane).emplace_back(SLPNode{{operand}}); + operands.at(lane).emplace_back(operand); } } -std::vector& SLPNode::getOperands(size_t lane) { +std::vector const& SLPNode::getOperands(size_t const& lane) const { return operands.at(lane); } -std::vector& SLPNode::getLane(size_t lane) { - return lanes.at(lane); +SLPNode const& SLPNode::getOperand(size_t const& lane, size_t const& index) const { + return operands.at(lane).at(index); } Operation* SLPNode::getOperation(size_t lane, size_t index) { return lanes.at(lane).at(index); } -OperationName const& SLPNode::name() { +OperationName const& SLPNode::name() const { return operationName; } diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index 779a01ea..cd8900c7 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -124,41 +124,44 @@ std::vector SLPTree::getOperands(Operation* operation) const { return operands; } -SLPTree::MODE SLPTree::modeFromOperation(Operation const* operation) const { +MODE SLPTree::modeFromOperation(Operation const* operation) const { if (dyn_cast(operation)) { return MODE::CONST; - } else if (dyn_cast(operation) || dyn_cast(operation)) { + } + // TODO: can this really be considered a load? I doubt it... + else if (dyn_cast(operation) || dyn_cast(operation)) { return MODE::LOAD; } return MODE::OPCODE; } -std::vector SLPTree::reorderOperands(SLPNode& multinode) { +std::vector> SLPTree::reorderOperands(SLPNode& multinode) { assert(multinode.isMultiNode()); - std::vector finalOrder; - /*std::vector mode; + std::vector> finalOrder{multinode.numLanes()}; + std::vector mode; + auto const& numOperands = multinode.getOperands(0).size(); // 1. Strip first lane - for (size_t i = 0; i < multinode.getOperands().size(); ++i) { - auto& operand = multinode.getOperand(i); - finalOrder.emplace_back(operand); - mode.emplace_back(modeFromOperation(multinode.getOperation(0, i))); + for (size_t i = 0; i < numOperands; ++i) { + auto operand = multinode.getOperand(0, i); + finalOrder.at(0).emplace_back(operand); + mode.emplace_back(modeFromOperation(operand.getOperation(0, 0))); } // 2. For all other lanes, find best candidate for (size_t lane = 1; lane < multinode.numLanes(); ++lane) { - std::vector candidates = multinode.getLane(lane); + std::vector candidates{multinode.getOperands(lane)}; // Look for a matching candidate - for (size_t i = 0; i < multinode.getOperands().size(); ++i) { + for (size_t i = 0; i < numOperands; ++i) { // Skip if we can't vectorize if (mode.at(i) == MODE::FAILED) { continue; } - auto const& last = finalOrder.at(i); + auto const& last = finalOrder.at(lane - 1).at(i); auto const& bestResult = getBest(mode.at(i), last, candidates); // Update output // TODO: funky two dimensional stuff - finalOrder.emplace_back(bestResult.first); + finalOrder.at(lane).emplace_back(bestResult.first.getValue()); // Detect SPLAT mode if (i == 1 && bestResult.first == last) { @@ -166,32 +169,32 @@ std::vector SLPTree::reorderOperands(SLPNode& multinode) { } } - }*/ + } return finalOrder; } -std::pair SLPTree::getBest(SLPTree::MODE& mode, - SLPNode& last, - std::vector& candidates) const { - SLPNode* best = nullptr; - std::vector bestCandidates; +std::pair, MODE> SLPTree::getBest(MODE& mode, + SLPNode const& last, + std::vector& candidates) const { + Optional best; + std::vector bestCandidates; if (mode == MODE::FAILED) { // Don't select now, let others choose first - best = nullptr; + best.reset(); } else if (mode == MODE::SPLAT) { // Look for other splat candidates - for (auto const& value : candidates) { - if (*value == last) { - best = value; + for (auto& operand : candidates) { + if (operand == last) { + best = operand; break; } } } else { // Default value best = candidates.front(); - for (auto const& candidate : candidates) { + for (auto& candidate : candidates) { // We don't have consecutive memory accesses, therefore we're only interested in opcode comparisons. - if (candidate->name() == last.name()) { + if (candidate.name() == last.name()) { bestCandidates.emplace_back(candidate); } } @@ -213,7 +216,7 @@ std::pair SLPTree::getBest(SLPTree::MODE& mode, std::set scores; for (auto const& candidate : bestCandidates) { // Get the look-ahead score - auto const& score = getLookAheadScore(last, *candidate, level); + auto const& score = getLookAheadScore(last, candidate, level); if (scores.empty() || score > bestScore) { best = candidate; bestScore = score; @@ -221,7 +224,7 @@ std::pair SLPTree::getBest(SLPTree::MODE& mode, } } // If found best at level don't go deeper - if (best != nullptr && scores.size() > 1) { + if (best.hasValue() && scores.size() > 1) { break; } } @@ -230,19 +233,19 @@ std::pair SLPTree::getBest(SLPTree::MODE& mode, } // Remove best from candidates - if (best != nullptr) { + if (best.hasValue()) { candidates.erase(std::remove(std::begin(candidates), std::end(candidates), best), std::end(candidates)); } return {best, mode}; } -int SLPTree::getLookAheadScore(SLPNode& last, SLPNode& candidate, size_t const& maxLevel) const { +int SLPTree::getLookAheadScore(SLPNode const& last, SLPNode const& candidate, size_t const& maxLevel) const { if (maxLevel == 0 || last.name() != candidate.name()) { return 0; } auto scoreSum = 0; for (size_t lane = 0; lane < last.numLanes(); ++lane) { - for (auto& lastOperand : last.getOperands(lane)) { - for (auto& candidateOperand : candidate.getOperands(lane)) { + for (auto const& lastOperand : last.getOperands(lane)) { + for (auto const& candidateOperand : candidate.getOperands(lane)) { scoreSum += getLookAheadScore(lastOperand, candidateOperand, maxLevel - 1); } } From fe7b697f83a316e2b458707bd984dd1c6df24cc2 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Fri, 22 Jan 2021 04:19:32 +0100 Subject: [PATCH 017/385] LookAhead fix, removed a const qualifier. --- .../Dialect/SPN/Analysis/SLP/SLPNode.h | 2 +- .../Dialect/SPN/Analysis/SLP/SLPTree.h | 2 +- mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 39 ++++++++++--------- 3 files changed, 23 insertions(+), 20 deletions(-) diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h index 9b6386b0..8ee7385e 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h @@ -44,7 +44,7 @@ namespace mlir { private: - OperationName const operationName; + OperationName operationName; /// Stores lanes as lists of operations. An inner vector (i.e. a lane) only contains more than one operation /// if this node is a multinode. diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h index 0ef98844..66ea42a1 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h @@ -48,7 +48,7 @@ namespace mlir { size_t const maxLookAhead; - std::pair, MODE> getBest(MODE& mode, + std::pair, MODE> getBest(MODE const& mode, SLPNode const& last, std::vector& candidates) const; int getLookAheadScore(SLPNode const& last, SLPNode const& candidate, size_t const& maxLevel) const; diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index cd8900c7..584a9dc9 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -126,13 +126,10 @@ std::vector SLPTree::getOperands(Operation* operation) const { MODE SLPTree::modeFromOperation(Operation const* operation) const { if (dyn_cast(operation)) { - return MODE::CONST; + return CONST; } - // TODO: can this really be considered a load? I doubt it... - else if (dyn_cast(operation) || dyn_cast(operation)) { - return MODE::LOAD; - } - return MODE::OPCODE; + // We don't have LOADs. Therefore just return OPCODE. + return OPCODE; } std::vector> SLPTree::reorderOperands(SLPNode& multinode) { @@ -153,19 +150,21 @@ std::vector> SLPTree::reorderOperands(SLPNode& multinode) { // Look for a matching candidate for (size_t i = 0; i < numOperands; ++i) { // Skip if we can't vectorize - if (mode.at(i) == MODE::FAILED) { + if (mode.at(i) == FAILED) { continue; } auto const& last = finalOrder.at(lane - 1).at(i); auto const& bestResult = getBest(mode.at(i), last, candidates); // Update output - // TODO: funky two dimensional stuff finalOrder.at(lane).emplace_back(bestResult.first.getValue()); // Detect SPLAT mode + // TODO: determine whether SPLAT mode is actually relevant (are the same operands used in more than one lane?) if (i == 1 && bestResult.first == last) { - mode.at(i) = MODE::SPLAT; + mode.at(i) = SPLAT; + } else { + mode.at(i) = bestResult.second; } } @@ -173,15 +172,17 @@ std::vector> SLPTree::reorderOperands(SLPNode& multinode) { return finalOrder; } -std::pair, MODE> SLPTree::getBest(MODE& mode, +std::pair, MODE> SLPTree::getBest(MODE const& mode, SLPNode const& last, std::vector& candidates) const { Optional best; + MODE resultMode = mode; std::vector bestCandidates; - if (mode == MODE::FAILED) { + + if (mode == FAILED) { // Don't select now, let others choose first best.reset(); - } else if (mode == MODE::SPLAT) { + } else if (mode == SPLAT) { // Look for other splat candidates for (auto& operand : candidates) { if (operand == last) { @@ -199,8 +200,9 @@ std::pair, MODE> SLPTree::getBest(MODE& mode, } } // 1. If we have a trivial solution, use it + // No matches if (bestCandidates.empty()) { - mode = MODE::FAILED; + resultMode = FAILED; } // Single match else if (bestCandidates.size() == 1) { @@ -208,11 +210,11 @@ std::pair, MODE> SLPTree::getBest(MODE& mode, } // 2. Look-ahead to choose from best candidates else { - if (mode == MODE::OPCODE) { + if (mode == OPCODE) { // Look-ahead on various levels for (size_t level = 1; level <= maxLookAhead; ++level) { // Best is the candidate with max score - int bestScore = 0; + auto bestScore = 0; std::set scores; for (auto const& candidate : bestCandidates) { // Get the look-ahead score @@ -236,11 +238,12 @@ std::pair, MODE> SLPTree::getBest(MODE& mode, if (best.hasValue()) { candidates.erase(std::remove(std::begin(candidates), std::end(candidates), best), std::end(candidates)); } - return {best, mode}; + return {best, resultMode}; } int SLPTree::getLookAheadScore(SLPNode const& last, SLPNode const& candidate, size_t const& maxLevel) const { - if (maxLevel == 0 || last.name() != candidate.name()) { - return 0; + if (maxLevel == 0) { + // No consecutive loads to check, only opcodes. + return last.name() == candidate.name() ? 1 : 0; } auto scoreSum = 0; for (size_t lane = 0; lane < last.numLanes(); ++lane) { From d6281b148ceb4d316b6be9d017e5c961a4f44e2d Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Fri, 22 Jan 2021 04:20:55 +0100 Subject: [PATCH 018/385] Added TODOs. --- mlir/include/Dialect/SPN/Analysis/SLP/SLPMode.h | 2 ++ mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 1 - 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPMode.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPMode.h index 96a881a6..847abdb0 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPMode.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPMode.h @@ -14,10 +14,12 @@ namespace mlir { // look for a constant CONST, // look for a consecutive load to that in the previous lane + // TODO: do we have load operations? or something that can be treated equivalently? LOAD, // look for an operation of the same opcode OPCODE, // look for the exact same operation + // TODO: determine whether SPLAT mode is actually relevant (are the same operands used in more than one lane?) SPLAT, // vectorization has failed, give higher priority to others FAILED diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index 584a9dc9..e2efb9f1 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -160,7 +160,6 @@ std::vector> SLPTree::reorderOperands(SLPNode& multinode) { finalOrder.at(lane).emplace_back(bestResult.first.getValue()); // Detect SPLAT mode - // TODO: determine whether SPLAT mode is actually relevant (are the same operands used in more than one lane?) if (i == 1 && bestResult.first == last) { mode.at(i) = SPLAT; } else { From 49600281eaedd753a84fc66b9c1ce945ba5f9a80 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sat, 23 Jan 2021 03:17:34 +0100 Subject: [PATCH 019/385] Some static refactoring. --- .../Dialect/SPN/Analysis/SLP/SLPMode.h | 10 ++++ .../Dialect/SPN/Analysis/SLP/SLPTree.h | 44 ++++++++++++++--- mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 48 ------------------- 3 files changed, 48 insertions(+), 54 deletions(-) diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPMode.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPMode.h index 847abdb0..45683205 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPMode.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPMode.h @@ -6,6 +6,8 @@ #ifndef SPNC_MLIR_DIALECTS_INCLUDE_DIALECT_SPN_ANALYSIS_SLP_SLPMODE_H #define SPNC_MLIR_DIALECTS_INCLUDE_DIALECT_SPN_ANALYSIS_SLP_SLPMODE_H +#include "SPN/SPNOps.h" + namespace mlir { namespace spn { namespace slp { @@ -25,6 +27,14 @@ namespace mlir { FAILED }; + static MODE modeFromOperation(Operation const* operation) { + if (dyn_cast(operation)) { + return CONST; + } + // We don't have LOADs. Therefore just return OPCODE. + return OPCODE; + } + } } } diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h index 66ea42a1..0077759a 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h @@ -10,6 +10,7 @@ #include "mlir/IR/OpDefinition.h" #include "llvm/ADT/SmallPtrSet.h" #include "llvm/ADT/StringMap.h" +#include "SPN/SPNOpTraits.h" #include "SLPNode.h" #include "SLPMode.h" @@ -35,12 +36,43 @@ namespace mlir { void buildGraph(std::vector const& operations, SLPNode& parentNode); - bool vectorizable(std::vector const& operations) const; - bool commutative(std::vector const& operations) const; - std::vector getOperands(std::vector const& values) const; - std::vector getOperands(Operation* value) const; - - MODE modeFromOperation(Operation const* operation) const; + /// Checks if the given operations are vectorizable. Operations are vectorizable iff the SPN dialect says they're + /// vectorizable and they all share the same opcode. + /// \param operations The potentially vectorizable operations. + /// \return True if the operations can be vectorized, otherwise false. + static bool vectorizable(std::vector const& operations) { + for (size_t i = 0; i < operations.size(); ++i) { + if (!operations.at(i)->hasTrait() + || (i > 0 && operations.at(i)->getName() != operations.front()->getName())) { + return false; + } + } + return true; + } + + static bool commutative(std::vector const& operations) { + return std::all_of(std::begin(operations), std::end(operations), [&](Operation* operation) { + return operation->hasTrait(); + }); + } + + static std::vector getOperands(std::vector const& values) { + std::vector operands; + for (auto const& operation : values) { + for (auto operand : operation->getOperands()) { + operands.emplace_back(operand.getDefiningOp()); + } + } + return operands; + } + + static std::vector getOperands(Operation* operation) { + std::vector operands; + for (auto operand : operation->getOperands()) { + operands.emplace_back(operand.getDefiningOp()); + } + return operands; + } std::vector> reorderOperands(SLPNode& multinode); diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index e2efb9f1..5277a265 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -4,8 +4,6 @@ // #include "SPN/Analysis/SLP/SLPTree.h" -#include "SPN/SPNOps.h" -#include "SPN/SPNOpTraits.h" #include #include @@ -86,52 +84,6 @@ void SLPTree::buildGraph(std::vector const& operations, SLPNode& par } -/// Checks if the given operations are vectorizable. Operations are vectorizable iff the SPN dialect says they're -/// vectorizable and they all share the same opcode. -/// \param operations The potentially vectorizable operations. -/// \return True if the operations can be vectorized, otherwise false. -bool SLPTree::vectorizable(std::vector const& operations) const { - for (size_t i = 0; i < operations.size(); ++i) { - if (!operations.at(i)->hasTrait() - || (i > 0 && operations.at(i)->getName() != operations.front()->getName())) { - return false; - } - } - return true; -} - -bool SLPTree::commutative(std::vector const& operations) const { - return std::all_of(std::begin(operations), std::end(operations), [&](Operation* operation) { - return operation->hasTrait(); - }); -} - -std::vector SLPTree::getOperands(std::vector const& values) const { - std::vector operands; - for (auto const& operation : values) { - for (auto operand : operation->getOperands()) { - operands.emplace_back(operand.getDefiningOp()); - } - } - return operands; -} - -std::vector SLPTree::getOperands(Operation* operation) const { - std::vector operands; - for (auto operand : operation->getOperands()) { - operands.emplace_back(operand.getDefiningOp()); - } - return operands; -} - -MODE SLPTree::modeFromOperation(Operation const* operation) const { - if (dyn_cast(operation)) { - return CONST; - } - // We don't have LOADs. Therefore just return OPCODE. - return OPCODE; -} - std::vector> SLPTree::reorderOperands(SLPNode& multinode) { assert(multinode.isMultiNode()); std::vector> finalOrder{multinode.numLanes()}; From ff4e89416f799a56ab020eb7a32e73d345f0b341 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sat, 23 Jan 2021 18:26:55 +0100 Subject: [PATCH 020/385] Added SPN -> .dot pass. --- mlir/tools/spnc-opt/spnc-opt.cpp | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/mlir/tools/spnc-opt/spnc-opt.cpp b/mlir/tools/spnc-opt/spnc-opt.cpp index 07d9c0be..719a2582 100644 --- a/mlir/tools/spnc-opt/spnc-opt.cpp +++ b/mlir/tools/spnc-opt/spnc-opt.cpp @@ -83,6 +83,10 @@ int main(int argc, char** argv) { return mlir::spn::createSPNtoLLVMConversionPass(); }); + mlir::registerPass("spn-to-dot", "Prints the SPN to a dot file", []() -> std::unique_ptr { + return mlir::createPrintOpGraphPass(llvm::errs(), false, llvm::Twine("test.dot")); + }); + llvm::InitLLVM y(argc, argv); // Register any pass manager command line options. From 35398d9a32b8b06f28db26ecb81910e43635f538 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sat, 23 Jan 2021 18:27:57 +0100 Subject: [PATCH 021/385] Better .dot pass parameters. --- mlir/tools/spnc-opt/spnc-opt.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/mlir/tools/spnc-opt/spnc-opt.cpp b/mlir/tools/spnc-opt/spnc-opt.cpp index 719a2582..8cbf73bd 100644 --- a/mlir/tools/spnc-opt/spnc-opt.cpp +++ b/mlir/tools/spnc-opt/spnc-opt.cpp @@ -83,8 +83,8 @@ int main(int argc, char** argv) { return mlir::spn::createSPNtoLLVMConversionPass(); }); - mlir::registerPass("spn-to-dot", "Prints the SPN to a dot file", []() -> std::unique_ptr { - return mlir::createPrintOpGraphPass(llvm::errs(), false, llvm::Twine("test.dot")); + mlir::registerPass("spn-to-dot", "Prints the SPN to stderr", []() -> std::unique_ptr { + return mlir::createPrintOpGraphPass(); }); llvm::InitLLVM y(argc, argv); From b1f0b12939044efac139fe4d07c8d9dc4df530a9 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sat, 23 Jan 2021 19:13:07 +0100 Subject: [PATCH 022/385] Better tree check. --- mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 20 ++++++++----------- 1 file changed, 8 insertions(+), 12 deletions(-) diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index 5277a265..55156179 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -15,15 +15,11 @@ using namespace mlir::spn::slp; SLPTree::SLPTree(Operation* root, size_t width, size_t maxLookAhead) : graphs{}, maxLookAhead{maxLookAhead} { assert(root); llvm::StringMap> operationsByOpCode; - std::set operations; for (auto& op : root->getBlock()->getOperations()) { operationsByOpCode[op.getName().getStringRef().str()].emplace_back(&op); - operations.insert(&op); - for (auto const& operand : op.getOperands()) { - if (operations.find(operand.getDefiningOp()) != std::end(operations)) { - std::cout << "it's not a tree!" << std::endl; - return; - } + auto const& uses = std::distance(op.getUses().begin(), op.getUses().end()); + if (uses > 1) { + std::cerr << "SPN is not a tree!" << std::endl; } } @@ -63,23 +59,23 @@ void SLPTree::buildGraph(std::vector const& operations, SLPNode& par return; } // Create new node for values and add to graph - SLPNode* currentNode = &parentNode; + SLPNode& currentNode = parentNode; // Recursion call to grow graph further // 1. Commutative if (commutative(operations)) { // A. Coarsening Mode for (auto const& operation : operations) { - buildGraph(getOperands(operation), *currentNode); + buildGraph(getOperands(operation), currentNode); } // B. Normal Mode: Finished building multi-node - if (currentNode->isMultiNode()) { - reorderOperands(*currentNode); + if (currentNode.isMultiNode()) { + reorderOperands(currentNode); // TODO buildGraph() } } // 2. Non-Commutative else { - buildGraph(getOperands(operations), *currentNode); + buildGraph(getOperands(operations), currentNode); } } From 62b2cc202159aebe4ced3a24c2003546c23d9681 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 24 Jan 2021 00:45:57 +0100 Subject: [PATCH 023/385] WIP seed computation. --- .../Dialect/SPN/Analysis/SLP/SLPSeeding.h | 43 +++++++++++++++++++ mlir/include/Dialect/SPN/SPNOpTraits.h | 3 ++ mlir/include/Dialect/SPN/SPNOpTraits.td | 5 +++ mlir/include/Dialect/SPN/SPNOps.td | 2 +- mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 2 + 5 files changed, 54 insertions(+), 1 deletion(-) create mode 100644 mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h new file mode 100644 index 00000000..964e67d7 --- /dev/null +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h @@ -0,0 +1,43 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#ifndef SPNC_MLIR_DIALECTS_INCLUDE_DIALECT_SPN_ANALYSIS_SLP_SLPSEEDING_H +#define SPNC_MLIR_DIALECTS_INCLUDE_DIALECT_SPN_ANALYSIS_SLP_SLPSEEDING_H + +#include "llvm/ADT/StringMap.h" + +#include "SPN/SPNOps.h" + +namespace mlir { + namespace spn { + namespace slp { + namespace seeding { + + typedef llvm::SmallVector seed; + + static std::vector getSeeds(Operation* root) { + llvm::StringMap> operationsByName; + + for (auto& op : root->getBlock()->getOperations()) { + if (op.hasTrait() || op.hasTrait()) { + operationsByName[op.getName().getStringRef()].emplace_back(&op); + } + } + + // Sort operations by their number of operands in descending order to maximize vectorization tree sizes. + for (auto& entry : operationsByName) { + std::sort(std::begin(entry.second), std::end(entry.second), [&](Operation* a, Operation* b) { + return a->getNumOperands() > b->getNumOperands(); + }); + } + + return {}; + } + } + } + } +} + +#endif //SPNC_MLIR_DIALECTS_INCLUDE_DIALECT_SPN_ANALYSIS_SLP_SLPSEEDING_H diff --git a/mlir/include/Dialect/SPN/SPNOpTraits.h b/mlir/include/Dialect/SPN/SPNOpTraits.h index 2e1b6c4a..b0dfc62e 100644 --- a/mlir/include/Dialect/SPN/SPNOpTraits.h +++ b/mlir/include/Dialect/SPN/SPNOpTraits.h @@ -14,6 +14,9 @@ namespace mlir { template class Vectorizable : public TraitBase {}; + + template + class Binarizable : public TraitBase {}; } } } diff --git a/mlir/include/Dialect/SPN/SPNOpTraits.td b/mlir/include/Dialect/SPN/SPNOpTraits.td index ca8b9d6d..0b6fb1c0 100644 --- a/mlir/include/Dialect/SPN/SPNOpTraits.td +++ b/mlir/include/Dialect/SPN/SPNOpTraits.td @@ -8,4 +8,9 @@ include "SPNDialect.td" /// def Vectorizable : NativeOpTrait<"spn::Vectorizable">; +/// +/// Trait which can be attached to operations to mark them as binarizable (such as weighted sums or products). +/// +def Binarizable : NativeOpTrait<"spn::Binarizable">; + #endif // SPNOpTraits \ No newline at end of file diff --git a/mlir/include/Dialect/SPN/SPNOps.td b/mlir/include/Dialect/SPN/SPNOps.td index 4a88e288..2bd1494b 100644 --- a/mlir/include/Dialect/SPN/SPNOps.td +++ b/mlir/include/Dialect/SPN/SPNOps.td @@ -42,7 +42,7 @@ def SumOp : SPN_NAry_Op<"sum"> { /// Weighted N-ary sum operation. /// def WeightedSumOp : SPN_Op<"weighted_sum", - [NoSideEffect, SameOperandsAndResultType, DeclareOpInterfaceMethods]> { + [NoSideEffect, Binarizable, SameOperandsAndResultType, DeclareOpInterfaceMethods]> { let summary = "Weighted, N-ary Sum Operation"; let description = [{ diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index 55156179..03fbc3a9 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -4,6 +4,7 @@ // #include "SPN/Analysis/SLP/SLPTree.h" +#include "SPN/Analysis/SLP/SLPSeeding.h" #include #include @@ -24,6 +25,7 @@ SLPTree::SLPTree(Operation* root, size_t width, size_t maxLookAhead) : graphs{}, } // TODO compute seeds in a proper fashion + auto const& seeds = seeding::getSeeds(root); for (auto const& entry : operationsByOpCode) { SLPNode rootNode{entry.getValue()}; buildGraph(entry.getValue(), rootNode); From ef957d5c485f169d61848a5500ad359e5047288b Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 24 Jan 2021 02:01:46 +0100 Subject: [PATCH 024/385] Basic seeding setup. --- .../Dialect/SPN/Analysis/SLP/SLPMode.h | 10 ++-------- .../Dialect/SPN/Analysis/SLP/SLPSeeding.h | 20 +++++++++++++++---- .../Dialect/SPN/Analysis/SLP/SLPTree.h | 4 ++-- mlir/include/Dialect/SPN/SPNOps.td | 2 +- mlir/lib/Dialect/SPN/Analysis/SLP/SLPMode.cpp | 19 ++++++++++++++++++ mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 8 ++++---- mlir/lib/Dialect/SPN/CMakeLists.txt | 1 + 7 files changed, 45 insertions(+), 19 deletions(-) create mode 100644 mlir/lib/Dialect/SPN/Analysis/SLP/SLPMode.cpp diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPMode.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPMode.h index 45683205..c2b82880 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPMode.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPMode.h @@ -12,7 +12,7 @@ namespace mlir { namespace spn { namespace slp { - enum MODE { + enum Mode { // look for a constant CONST, // look for a consecutive load to that in the previous lane @@ -27,13 +27,7 @@ namespace mlir { FAILED }; - static MODE modeFromOperation(Operation const* operation) { - if (dyn_cast(operation)) { - return CONST; - } - // We don't have LOADs. Therefore just return OPCODE. - return OPCODE; - } + Mode modeFromOperation(Operation const* operation); } } diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h index 964e67d7..8bced896 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h @@ -17,23 +17,35 @@ namespace mlir { typedef llvm::SmallVector seed; - static std::vector getSeeds(Operation* root) { - llvm::StringMap> operationsByName; + enum Mode { + // Look for disjoint subgraphs in the operation tree. + DISJOINT, + // Look for the largest possible subgraph. + SIZE, + // Stop looking for subgraphs. + FAILED + }; + + static std::vector getSeeds(Operation* root, size_t const& width) { + llvm::StringMap> operationsByName; for (auto& op : root->getBlock()->getOperations()) { if (op.hasTrait() || op.hasTrait()) { operationsByName[op.getName().getStringRef()].emplace_back(&op); } } - // Sort operations by their number of operands in descending order to maximize vectorization tree sizes. + std::vector seeds; for (auto& entry : operationsByName) { + // Sort operations by their number of operands in descending order to maximize vectorization tree sizes. std::sort(std::begin(entry.second), std::end(entry.second), [&](Operation* a, Operation* b) { return a->getNumOperands() > b->getNumOperands(); }); + Mode searchMode = DISJOINT; + } - return {}; + return seeds; } } } diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h index 0077759a..ed190374 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h @@ -11,8 +11,8 @@ #include "llvm/ADT/SmallPtrSet.h" #include "llvm/ADT/StringMap.h" #include "SPN/SPNOpTraits.h" -#include "SLPNode.h" #include "SLPMode.h" +#include "SLPNode.h" #include #include @@ -80,7 +80,7 @@ namespace mlir { size_t const maxLookAhead; - std::pair, MODE> getBest(MODE const& mode, + std::pair, Mode> getBest(Mode const& mode, SLPNode const& last, std::vector& candidates) const; int getLookAheadScore(SLPNode const& last, SLPNode const& candidate, size_t const& maxLevel) const; diff --git a/mlir/include/Dialect/SPN/SPNOps.td b/mlir/include/Dialect/SPN/SPNOps.td index 2bd1494b..da5e9240 100644 --- a/mlir/include/Dialect/SPN/SPNOps.td +++ b/mlir/include/Dialect/SPN/SPNOps.td @@ -11,7 +11,7 @@ include "mlir/IR/RegionKindInterface.td" /// Abstract base class of n-ary arithmetic operations /// class SPN_NAry_Op : SPN_Op]> { + [NoSideEffect, Commutative, Binarizable, Vectorizable, SameOperandsAndResultType, DeclareOpInterfaceMethods]> { /// Operands: Child nodes /// opcount: Number of child nodes let arguments = (ins Variadic: $operands); diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPMode.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPMode.cpp new file mode 100644 index 00000000..f53bfd9e --- /dev/null +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPMode.cpp @@ -0,0 +1,19 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#include "SPN/Analysis/SLP/SLPMode.h" + +using namespace mlir; +using namespace mlir::spn; +using namespace mlir::spn::slp; + +Mode mlir::spn::slp::modeFromOperation(Operation const* operation) { + if (dyn_cast(operation)) { + return CONST; + } + // We don't have LOADs. Therefore just return OPCODE. + return OPCODE; +} + diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index 03fbc3a9..0e2bbb0c 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -25,7 +25,7 @@ SLPTree::SLPTree(Operation* root, size_t width, size_t maxLookAhead) : graphs{}, } // TODO compute seeds in a proper fashion - auto const& seeds = seeding::getSeeds(root); + auto const& seeds = seeding::getSeeds(root, 4); for (auto const& entry : operationsByOpCode) { SLPNode rootNode{entry.getValue()}; buildGraph(entry.getValue(), rootNode); @@ -85,7 +85,7 @@ void SLPTree::buildGraph(std::vector const& operations, SLPNode& par std::vector> SLPTree::reorderOperands(SLPNode& multinode) { assert(multinode.isMultiNode()); std::vector> finalOrder{multinode.numLanes()}; - std::vector mode; + std::vector mode; auto const& numOperands = multinode.getOperands(0).size(); // 1. Strip first lane for (size_t i = 0; i < numOperands; ++i) { @@ -121,11 +121,11 @@ std::vector> SLPTree::reorderOperands(SLPNode& multinode) { return finalOrder; } -std::pair, MODE> SLPTree::getBest(MODE const& mode, +std::pair, Mode> SLPTree::getBest(Mode const& mode, SLPNode const& last, std::vector& candidates) const { Optional best; - MODE resultMode = mode; + Mode resultMode = mode; std::vector bestCandidates; if (mode == FAILED) { diff --git a/mlir/lib/Dialect/SPN/CMakeLists.txt b/mlir/lib/Dialect/SPN/CMakeLists.txt index d940db7e..0706efc6 100644 --- a/mlir/lib/Dialect/SPN/CMakeLists.txt +++ b/mlir/lib/Dialect/SPN/CMakeLists.txt @@ -6,6 +6,7 @@ add_mlir_dialect_library(MLIRSPN Analysis/SPNNodeLevel.cpp Analysis/SPNGraphStatistics.cpp Analysis/SLP/SLPTree.cpp + Analysis/SLP/SLPMode.cpp Analysis/SLP/SLPNode.cpp Canonicalization/CanonicalizationPatterns.cpp Simplification/SimplificationPatterns.cpp From b0e8b623fc2ad7d43849d7c40ed2288a12d86273 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 24 Jan 2021 04:38:28 +0100 Subject: [PATCH 025/385] Seeding test. --- .../Dialect/SPN/Analysis/SLP/SLPSeeding.h | 34 ++------- mlir/lib/Dialect/SPN/Analysis/SLP/SLPMode.cpp | 2 +- .../Dialect/SPN/Analysis/SLP/SLPSeeding.cpp | 72 +++++++++++++++++++ mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 30 ++------ mlir/lib/Dialect/SPN/CMakeLists.txt | 3 +- 5 files changed, 87 insertions(+), 54 deletions(-) create mode 100644 mlir/lib/Dialect/SPN/Analysis/SLP/SLPSeeding.cpp diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h index 8bced896..091a5573 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h @@ -6,8 +6,6 @@ #ifndef SPNC_MLIR_DIALECTS_INCLUDE_DIALECT_SPN_ANALYSIS_SLP_SLPSEEDING_H #define SPNC_MLIR_DIALECTS_INCLUDE_DIALECT_SPN_ANALYSIS_SLP_SLPSEEDING_H -#include "llvm/ADT/StringMap.h" - #include "SPN/SPNOps.h" namespace mlir { @@ -15,38 +13,18 @@ namespace mlir { namespace slp { namespace seeding { - typedef llvm::SmallVector seed; + typedef std::vector seed_t; - enum Mode { - // Look for disjoint subgraphs in the operation tree. + enum SearchMode { + /// Look for disjoint subgraphs in the operation tree. DISJOINT, - // Look for the largest possible subgraph. + /// Look for the largest possible subgraph. SIZE, - // Stop looking for subgraphs. + /// Stop looking for subgraphs. FAILED }; - static std::vector getSeeds(Operation* root, size_t const& width) { - - llvm::StringMap> operationsByName; - for (auto& op : root->getBlock()->getOperations()) { - if (op.hasTrait() || op.hasTrait()) { - operationsByName[op.getName().getStringRef()].emplace_back(&op); - } - } - - std::vector seeds; - for (auto& entry : operationsByName) { - // Sort operations by their number of operands in descending order to maximize vectorization tree sizes. - std::sort(std::begin(entry.second), std::end(entry.second), [&](Operation* a, Operation* b) { - return a->getNumOperands() > b->getNumOperands(); - }); - Mode searchMode = DISJOINT; - - } - - return seeds; - } + std::vector getSeeds(Operation* root, size_t const& width); } } } diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPMode.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPMode.cpp index f53bfd9e..47cdd5d1 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPMode.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPMode.cpp @@ -9,7 +9,7 @@ using namespace mlir; using namespace mlir::spn; using namespace mlir::spn::slp; -Mode mlir::spn::slp::modeFromOperation(Operation const* operation) { +Mode slp::modeFromOperation(Operation const* operation) { if (dyn_cast(operation)) { return CONST; } diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPSeeding.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPSeeding.cpp new file mode 100644 index 00000000..14a124b9 --- /dev/null +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPSeeding.cpp @@ -0,0 +1,72 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#include "SPN/Analysis/SLP/SLPSeeding.h" +#include "llvm/ADT/StringMap.h" + +using namespace mlir; +using namespace mlir::spn; +using namespace mlir::spn::slp; +using namespace mlir::spn::slp::seeding; + +std::vector seeding::getSeeds(Operation* root, size_t const& width) { + + llvm::StringMap> operationsByName; + for (auto& op : root->getBlock()->getOperations()) { + if (op.hasTrait() || op.hasTrait()) { + operationsByName[op.getName().getStringRef()].emplace_back(&op); + } + } + + std::vector seeds; + for (auto& entry : operationsByName) { + auto& operations = entry.second; + // Sort operations by their number of operands in descending order to maximize vectorization tree sizes. + std::sort(std::begin(operations), std::end(operations), [&](Operation* a, Operation* b) { + return a->getNumOperands() > b->getNumOperands(); + }); + SearchMode searchMode = SIZE; + std::vector assignedOps(operations.size()); + for (size_t i = 0; i < operations.size() - width; ++i) { + + if (assignedOps.at(i)) { + continue; + } + + // Begin a new seed. + seed_t seed; + auto& firstOp = operations.at(i); + seed.emplace_back(firstOp); + assignedOps.at(i) = true; + + for (size_t j = i + 1; j < operations.size() && seed.size() < width; ++j) { + + if (assignedOps.at(j)) { + continue; + } + + auto& nextOp = operations.at(j); + // When in SIZE mode, simply add the next biggest operation to the seed. + if (searchMode == SIZE) { + seed.emplace_back(nextOp); + assignedOps.at(j) = true; + } + + if (seed.size() == width) { + break; + } + } + if (seed.size() == width) { + seeds.emplace_back(seed); + } else { + searchMode = FAILED; + } + } + + } + + return seeds; + +} diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index 0e2bbb0c..4f848118 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -24,38 +24,20 @@ SLPTree::SLPTree(Operation* root, size_t width, size_t maxLookAhead) : graphs{}, } } - // TODO compute seeds in a proper fashion auto const& seeds = seeding::getSeeds(root, 4); - for (auto const& entry : operationsByOpCode) { - SLPNode rootNode{entry.getValue()}; - buildGraph(entry.getValue(), rootNode); + for (auto const& seed : seeds) { + SLPNode rootNode{seed}; + buildGraph(seed, rootNode); } + std::cout << "seeds computed" << std::endl; } -/* -void SLPTree::analyzeGraph(Operation* root) { - traverseSubgraph(root); -} - -void SLPTree::traverseSubgraph(Operation* root) { - std::cout << root->getName().getStringRef().str() << std::endl; - if (auto leaf = dyn_cast(root)) { - std::cout << "\tis a leaf." << std::endl; - } else { - std::cout << "\tis an inner node." << std::endl; - for (auto op : root->getOperands()) { - traverseSubgraph(op.getDefiningOp()); - } - } - -}*/ - - void SLPTree::buildGraph(std::vector const& operations, SLPNode& parentNode) { for (auto const& op : operations) { - std::cout << op->getName().getStringRef().str() << std::endl; + op->dump(); } + // TODO: handle binarizable > multinode conversion: if binarizable node, binarize it before going through here // Stop growing graph if (!vectorizable(operations)) { return; diff --git a/mlir/lib/Dialect/SPN/CMakeLists.txt b/mlir/lib/Dialect/SPN/CMakeLists.txt index 0706efc6..464dd953 100644 --- a/mlir/lib/Dialect/SPN/CMakeLists.txt +++ b/mlir/lib/Dialect/SPN/CMakeLists.txt @@ -5,9 +5,10 @@ add_mlir_dialect_library(MLIRSPN SPNEnums.cpp Analysis/SPNNodeLevel.cpp Analysis/SPNGraphStatistics.cpp - Analysis/SLP/SLPTree.cpp Analysis/SLP/SLPMode.cpp Analysis/SLP/SLPNode.cpp + Analysis/SLP/SLPTree.cpp + Analysis/SLP/SLPSeeding.cpp Canonicalization/CanonicalizationPatterns.cpp Simplification/SimplificationPatterns.cpp TypeAnalysis/TypePinningPatterns.cpp From 15111526c484dd598c7cf7f6066c123a24353686 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 24 Jan 2021 21:33:57 +0100 Subject: [PATCH 026/385] Converted seeding into proper MLIR analysis. --- .../Dialect/SPN/Analysis/SLP/SLPSeeding.h | 21 ++++++++--- .../Dialect/SPN/Analysis/SLP/SLPSeeding.cpp | 34 ++++++++++------- mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 4 +- .../Dialect/SPN/Passes/SPNVectorization.cpp | 37 +++++++++++++++++-- mlir/tools/spnc-opt/spnc-opt.cpp | 3 -- 5 files changed, 72 insertions(+), 27 deletions(-) diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h index 091a5573..6dcec9da 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h @@ -7,25 +7,36 @@ #define SPNC_MLIR_DIALECTS_INCLUDE_DIALECT_SPN_ANALYSIS_SLP_SLPSEEDING_H #include "SPN/SPNOps.h" +#include "llvm/ADT/StringMap.h" namespace mlir { namespace spn { namespace slp { - namespace seeding { - typedef std::vector seed_t; + typedef std::vector seed_t; + + class SeedAnalysis { + + public: + + explicit SeedAnalysis(Operation* module); + + std::vector getSeeds(size_t const& width) const; + + private: enum SearchMode { /// Look for disjoint subgraphs in the operation tree. DISJOINT, /// Look for the largest possible subgraph. - SIZE, + GREEDY, /// Stop looking for subgraphs. FAILED }; - std::vector getSeeds(Operation* root, size_t const& width); - } + llvm::StringMap> operationsByName; + + }; } } } diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPSeeding.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPSeeding.cpp index 14a124b9..7e610854 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPSeeding.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPSeeding.cpp @@ -4,30 +4,38 @@ // #include "SPN/Analysis/SLP/SLPSeeding.h" -#include "llvm/ADT/StringMap.h" +#include using namespace mlir; using namespace mlir::spn; using namespace mlir::spn::slp; -using namespace mlir::spn::slp::seeding; -std::vector seeding::getSeeds(Operation* root, size_t const& width) { +SeedAnalysis::SeedAnalysis(Operation* module) : operationsByName{} { - llvm::StringMap> operationsByName; - for (auto& op : root->getBlock()->getOperations()) { - if (op.hasTrait() || op.hasTrait()) { - operationsByName[op.getName().getStringRef()].emplace_back(&op); + module->walk([&](Operation* op) { + if (op->hasTrait() || op->hasTrait()) { + operationsByName[op->getName().getStringRef()].emplace_back(op); } + }); + + // Sort operations by their number of operands in descending order to maximize vectorization tree sizes. + for (auto& entry : operationsByName) { + std::sort(std::begin(entry.second), std::end(entry.second), [&](Operation* a, Operation* b) { + return a->getNumOperands() > b->getNumOperands(); + }); } +} + +std::vector SeedAnalysis::getSeeds(size_t const& width) const { + + std::cout << "Starting seed computation!" << std::endl; std::vector seeds; + for (auto& entry : operationsByName) { + auto& operations = entry.second; - // Sort operations by their number of operands in descending order to maximize vectorization tree sizes. - std::sort(std::begin(operations), std::end(operations), [&](Operation* a, Operation* b) { - return a->getNumOperands() > b->getNumOperands(); - }); - SearchMode searchMode = SIZE; + SearchMode searchMode = GREEDY; std::vector assignedOps(operations.size()); for (size_t i = 0; i < operations.size() - width; ++i) { @@ -49,7 +57,7 @@ std::vector seeding::getSeeds(Operation* root, size_t const& width) { auto& nextOp = operations.at(j); // When in SIZE mode, simply add the next biggest operation to the seed. - if (searchMode == SIZE) { + if (searchMode == GREEDY) { seed.emplace_back(nextOp); assignedOps.at(j) = true; } diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index 4f848118..1d14e91c 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -23,14 +23,14 @@ SLPTree::SLPTree(Operation* root, size_t width, size_t maxLookAhead) : graphs{}, std::cerr << "SPN is not a tree!" << std::endl; } } - +/* auto const& seeds = seeding::getSeeds(root, 4); for (auto const& seed : seeds) { SLPNode rootNode{seed}; buildGraph(seed, rootNode); } std::cout << "seeds computed" << std::endl; - +*/ } void SLPTree::buildGraph(std::vector const& operations, SLPNode& parentNode) { diff --git a/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp b/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp index f77c7133..7c8f4176 100644 --- a/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp +++ b/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp @@ -9,7 +9,10 @@ #include "SPNPassDetails.h" #include #include +#include #include "SPN/Analysis/SLP/SLPTree.h" +#include "SPN/Analysis/SLP/SLPSeeding.h" + using namespace mlir; using namespace mlir::spn; @@ -22,10 +25,36 @@ namespace { std::cout << "Starting SPN vectorization..." << std::endl; auto func = getOperation(); - func.walk([](Operation* op) { - if (auto query = dyn_cast(op)) { - for (auto r : query.getRootNodes()) { - slp::SLPTree graph(r, 4, 3); + func.walk([&](Operation* topLevelOp) { + if (auto query = dyn_cast(topLevelOp)) { + for (auto root : query.getRootNodes()) { + + // ============ TREE CHECK ============ + llvm::StringMap> operationsByOpCode; + for (auto& op : root->getBlock()->getOperations()) { + operationsByOpCode[op.getName().getStringRef().str()].emplace_back(&op); + auto const& uses = std::distance(op.getUses().begin(), op.getUses().end()); + if (uses > 1) { + std::cerr << "SPN is not a tree!" << std::endl; + } + } + // ==================================== + + auto& seedAnalysis = getAnalysis(); + + auto const& seeds = seedAnalysis.getSeeds(4); + + if (!seeds.empty()) { + // Run simplification on seeds to binarize them into trees. + OpPassManager binarizer("operation", mlir::OpPassManager::Nesting::Explicit); + binarizer.addPass(createSPNOpSimplifierPass()); + runPipeline(binarizer, seeds.front().front()); + std::cout << "binarized a seed!" << std::endl; + } + + std::cout << "seeds computed" << std::endl; + + slp::SLPTree graph(root, 4, 3); } } }); diff --git a/mlir/tools/spnc-opt/spnc-opt.cpp b/mlir/tools/spnc-opt/spnc-opt.cpp index 8cbf73bd..482ef220 100644 --- a/mlir/tools/spnc-opt/spnc-opt.cpp +++ b/mlir/tools/spnc-opt/spnc-opt.cpp @@ -4,14 +4,11 @@ // #include "mlir/IR/Dialect.h" -#include "mlir/IR/MLIRContext.h" #include "mlir/InitAllDialects.h" #include "mlir/InitAllPasses.h" -#include "mlir/Pass/Pass.h" #include "mlir/Pass/PassManager.h" #include "mlir/Support/FileUtilities.h" #include "mlir/Support/MlirOptMain.h" -#include "llvm/Support/CommandLine.h" #include "llvm/Support/InitLLVM.h" #include "llvm/Support/ToolOutputFile.h" From fbe708528f677b64eb72918972913ec1628f9ae3 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 31 Jan 2021 21:31:37 +0100 Subject: [PATCH 027/385] Included node level analysis in seeding computation. --- .../Dialect/SPN/Analysis/SLP/SLPSeeding.h | 6 +- mlir/include/Dialect/SPN/SPNOpTraits.h | 3 - mlir/include/Dialect/SPN/SPNOpTraits.td | 5 -- mlir/include/Dialect/SPN/SPNOps.td | 4 +- .../Dialect/SPN/Analysis/SLP/SLPSeeding.cpp | 82 +++++++------------ .../Dialect/SPN/Passes/SPNVectorization.cpp | 29 +++---- 6 files changed, 46 insertions(+), 83 deletions(-) diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h index 6dcec9da..0abc84c5 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h @@ -6,8 +6,8 @@ #ifndef SPNC_MLIR_DIALECTS_INCLUDE_DIALECT_SPN_ANALYSIS_SLP_SLPSEEDING_H #define SPNC_MLIR_DIALECTS_INCLUDE_DIALECT_SPN_ANALYSIS_SLP_SLPSEEDING_H +#include #include "SPN/SPNOps.h" -#include "llvm/ADT/StringMap.h" namespace mlir { namespace spn { @@ -21,7 +21,7 @@ namespace mlir { explicit SeedAnalysis(Operation* module); - std::vector getSeeds(size_t const& width) const; + std::vector getSeeds(size_t const& op, SPNNodeLevel const& nodeLevels) const; private: @@ -34,7 +34,7 @@ namespace mlir { FAILED }; - llvm::StringMap> operationsByName; + Operation* module; }; } diff --git a/mlir/include/Dialect/SPN/SPNOpTraits.h b/mlir/include/Dialect/SPN/SPNOpTraits.h index b0dfc62e..2e1b6c4a 100644 --- a/mlir/include/Dialect/SPN/SPNOpTraits.h +++ b/mlir/include/Dialect/SPN/SPNOpTraits.h @@ -14,9 +14,6 @@ namespace mlir { template class Vectorizable : public TraitBase {}; - - template - class Binarizable : public TraitBase {}; } } } diff --git a/mlir/include/Dialect/SPN/SPNOpTraits.td b/mlir/include/Dialect/SPN/SPNOpTraits.td index 0b6fb1c0..ca8b9d6d 100644 --- a/mlir/include/Dialect/SPN/SPNOpTraits.td +++ b/mlir/include/Dialect/SPN/SPNOpTraits.td @@ -8,9 +8,4 @@ include "SPNDialect.td" /// def Vectorizable : NativeOpTrait<"spn::Vectorizable">; -/// -/// Trait which can be attached to operations to mark them as binarizable (such as weighted sums or products). -/// -def Binarizable : NativeOpTrait<"spn::Binarizable">; - #endif // SPNOpTraits \ No newline at end of file diff --git a/mlir/include/Dialect/SPN/SPNOps.td b/mlir/include/Dialect/SPN/SPNOps.td index da5e9240..4a88e288 100644 --- a/mlir/include/Dialect/SPN/SPNOps.td +++ b/mlir/include/Dialect/SPN/SPNOps.td @@ -11,7 +11,7 @@ include "mlir/IR/RegionKindInterface.td" /// Abstract base class of n-ary arithmetic operations /// class SPN_NAry_Op : SPN_Op]> { + [NoSideEffect, Commutative, Vectorizable, SameOperandsAndResultType, DeclareOpInterfaceMethods]> { /// Operands: Child nodes /// opcount: Number of child nodes let arguments = (ins Variadic: $operands); @@ -42,7 +42,7 @@ def SumOp : SPN_NAry_Op<"sum"> { /// Weighted N-ary sum operation. /// def WeightedSumOp : SPN_Op<"weighted_sum", - [NoSideEffect, Binarizable, SameOperandsAndResultType, DeclareOpInterfaceMethods]> { + [NoSideEffect, SameOperandsAndResultType, DeclareOpInterfaceMethods]> { let summary = "Weighted, N-ary Sum Operation"; let description = [{ diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPSeeding.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPSeeding.cpp index 7e610854..af34f8c8 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPSeeding.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPSeeding.cpp @@ -5,76 +5,50 @@ #include "SPN/Analysis/SLP/SLPSeeding.h" #include +#include using namespace mlir; using namespace mlir::spn; using namespace mlir::spn::slp; -SeedAnalysis::SeedAnalysis(Operation* module) : operationsByName{} { +SeedAnalysis::SeedAnalysis(Operation* module) : module{module} {} - module->walk([&](Operation* op) { - if (op->hasTrait() || op->hasTrait()) { - operationsByName[op->getName().getStringRef()].emplace_back(op); - } - }); - - // Sort operations by their number of operands in descending order to maximize vectorization tree sizes. - for (auto& entry : operationsByName) { - std::sort(std::begin(entry.second), std::end(entry.second), [&](Operation* a, Operation* b) { - return a->getNumOperands() > b->getNumOperands(); - }); - } -} - -std::vector SeedAnalysis::getSeeds(size_t const& width) const { +std::vector SeedAnalysis::getSeeds(size_t const& width, SPNNodeLevel const& nodeLevels) const { std::cout << "Starting seed computation!" << std::endl; - std::vector seeds; - - for (auto& entry : operationsByName) { + llvm::StringMap> seedsByOpName; - auto& operations = entry.second; - SearchMode searchMode = GREEDY; - std::vector assignedOps(operations.size()); - for (size_t i = 0; i < operations.size() - width; ++i) { - - if (assignedOps.at(i)) { - continue; + module->walk([&](Operation* op) { + auto depth = nodeLevels.getOperationDepth(op); + if (!op->hasTrait() || depth < log2(width)) { + return; + } + bool needsNewSeed = true; + for (auto& seed : seedsByOpName[op->getName().getStringRef()]) { + if (seed.size() < width) { + seed.emplace_back(op); + needsNewSeed = false; + break; } + } + if (needsNewSeed) { + seed_t seed{op}; + seedsByOpName[op->getName().getStringRef()].emplace_back(seed); + } - // Begin a new seed. - seed_t seed; - auto& firstOp = operations.at(i); - seed.emplace_back(firstOp); - assignedOps.at(i) = true; - - for (size_t j = i + 1; j < operations.size() && seed.size() < width; ++j) { - - if (assignedOps.at(j)) { - continue; - } - - auto& nextOp = operations.at(j); - // When in SIZE mode, simply add the next biggest operation to the seed. - if (searchMode == GREEDY) { - seed.emplace_back(nextOp); - assignedOps.at(j) = true; - } + }); - if (seed.size() == width) { - break; - } - } - if (seed.size() == width) { - seeds.emplace_back(seed); - } else { - searchMode = FAILED; + std::vector seeds; + // Flatten the map that maps opcode to seeds. + for (auto const& entry : seedsByOpName) { + for (auto const& seed : entry.second) { + if (seed.size() != width) { + continue; } + seeds.emplace_back(seed); } - } - return seeds; } diff --git a/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp b/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp index 7c8f4176..3c7377bf 100644 --- a/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp +++ b/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp @@ -12,6 +12,7 @@ #include #include "SPN/Analysis/SLP/SLPTree.h" #include "SPN/Analysis/SLP/SLPSeeding.h" +#include "SPN/Analysis/SPNNodeLevel.h" using namespace mlir; using namespace mlir::spn; @@ -23,9 +24,18 @@ namespace { protected: void runOnOperation() override { std::cout << "Starting SPN vectorization..." << std::endl; - auto func = getOperation(); + auto module = getOperation(); - func.walk([&](Operation* topLevelOp) { + auto& depthAnalysis = getAnalysis(); + auto& seedAnalysis = getAnalysis(); + auto seeds = seedAnalysis.getSeeds(4, depthAnalysis); + std::cout << "seeds computed" << std::endl; + + if (!seeds.empty()) { + std::cout << "binarized spn!" << std::endl; + } + + module.walk([&](Operation* topLevelOp) { if (auto query = dyn_cast(topLevelOp)) { for (auto root : query.getRootNodes()) { @@ -40,25 +50,12 @@ namespace { } // ==================================== - auto& seedAnalysis = getAnalysis(); - - auto const& seeds = seedAnalysis.getSeeds(4); - - if (!seeds.empty()) { - // Run simplification on seeds to binarize them into trees. - OpPassManager binarizer("operation", mlir::OpPassManager::Nesting::Explicit); - binarizer.addPass(createSPNOpSimplifierPass()); - runPipeline(binarizer, seeds.front().front()); - std::cout << "binarized a seed!" << std::endl; - } - - std::cout << "seeds computed" << std::endl; - slp::SLPTree graph(root, 4, 3); } } }); } + }; } From 4295622b829497bfa3359e360a4245043223ae0c Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 31 Jan 2021 22:31:19 +0100 Subject: [PATCH 028/385] Marked Gaussian leaves as vectorizable. --- mlir/include/Dialect/SPN/SPNOps.td | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlir/include/Dialect/SPN/SPNOps.td b/mlir/include/Dialect/SPN/SPNOps.td index 4a88e288..beb2c073 100644 --- a/mlir/include/Dialect/SPN/SPNOps.td +++ b/mlir/include/Dialect/SPN/SPNOps.td @@ -133,7 +133,7 @@ def CategoricalOp : SPN_Op<"categorical", /// Gaussian distribution as SPN leaf node. /// def GaussianOp : SPN_Op<"gaussian", - [NoSideEffect, DeclareOpInterfaceMethods]> { + [NoSideEffect, Vectorizable, DeclareOpInterfaceMethods]> { let summary = "Gaussian leaf distribution"; From ae77bfccfa8e1d29fe61525e5a7346c17bb67b62 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 31 Jan 2021 22:31:52 +0100 Subject: [PATCH 029/385] Changed vectorization pass to run on JointQueries instead of ModuleOps. --- mlir/include/Dialect/SPN/SPNPasses.h | 3 ++- mlir/include/Dialect/SPN/SPNPasses.td | 3 ++- mlir/lib/Dialect/SPN/Analysis/SLP/SLPSeeding.cpp | 1 + mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp | 6 +----- 4 files changed, 6 insertions(+), 7 deletions(-) diff --git a/mlir/include/Dialect/SPN/SPNPasses.h b/mlir/include/Dialect/SPN/SPNPasses.h index 0ae66fbe..4163b61c 100644 --- a/mlir/include/Dialect/SPN/SPNPasses.h +++ b/mlir/include/Dialect/SPN/SPNPasses.h @@ -7,6 +7,7 @@ #define SPNC_MLIR_DIALECTS_INCLUDE_SPN_SPNPASSES_H #include "mlir/Pass/Pass.h" +#include "SPNOps.h" namespace mlir { namespace spn { @@ -23,7 +24,7 @@ namespace mlir { /// Instantiate the SPNVectorization pass vectorizing the computation of the SPN. /// \return Pass instance. - std::unique_ptr> createSPNVectorizationPass(); + std::unique_ptr> createSPNVectorizationPass(); #define GEN_PASS_REGISTRATION #include "SPN/SPNPasses.h.inc" diff --git a/mlir/include/Dialect/SPN/SPNPasses.td b/mlir/include/Dialect/SPN/SPNPasses.td index bbbd7ac3..829a07e2 100644 --- a/mlir/include/Dialect/SPN/SPNPasses.td +++ b/mlir/include/Dialect/SPN/SPNPasses.td @@ -2,6 +2,7 @@ #define MLIR_DIALECT_SPN_PASSES include "mlir/Pass/PassBase.td" +include "SPNOps.td" def SPNOpSimplifier : Pass<"spn-op-simplifier", "ModuleOp"> { let summary = "Simplify SPN operations"; @@ -13,7 +14,7 @@ def SPNTypePinning : Pass<"spn-type-pinning", "ModuleOp"> { let constructor = "mlir::spn::createSPNTypePinningPass()"; } -def SPNVectorization : Pass<"spn-vectorization", "ModuleOp"> { +def SPNVectorization : Pass<"spn-vectorization", "JointQuery"> { let summary = "Vectorize SPN execution"; let constructor = "mlir::spn::createSPNVectorizationPass()"; } diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPSeeding.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPSeeding.cpp index af34f8c8..f1f5d9ff 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPSeeding.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPSeeding.cpp @@ -20,6 +20,7 @@ std::vector SeedAnalysis::getSeeds(size_t const& width, SPNNodeLevel con llvm::StringMap> seedsByOpName; module->walk([&](Operation* op) { + op->getLoc().dump(); auto depth = nodeLevels.getOperationDepth(op); if (!op->hasTrait() || depth < log2(width)) { return; diff --git a/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp b/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp index 3c7377bf..2c41b9dd 100644 --- a/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp +++ b/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp @@ -3,13 +3,9 @@ // Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. // -#include "mlir/IR/Matchers.h" -#include "mlir/IR/PatternMatch.h" #include "SPN/SPNPasses.h" #include "SPNPassDetails.h" #include -#include -#include #include "SPN/Analysis/SLP/SLPTree.h" #include "SPN/Analysis/SLP/SLPSeeding.h" #include "SPN/Analysis/SPNNodeLevel.h" @@ -60,7 +56,7 @@ namespace { } -std::unique_ptr> mlir::spn::createSPNVectorizationPass() { +std::unique_ptr> mlir::spn::createSPNVectorizationPass() { return std::make_unique(); } From 0ef4766b3af48e3d0fbb91cc45f77a110e356135 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Mon, 1 Feb 2021 02:48:01 +0100 Subject: [PATCH 030/385] Added seed sorting. --- .../Dialect/SPN/Analysis/SLP/SLPSeeding.h | 22 ++++++++++--------- .../Dialect/SPN/Analysis/SLP/SLPSeeding.cpp | 19 +++++++++++++--- 2 files changed, 28 insertions(+), 13 deletions(-) diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h index 0abc84c5..92349881 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h @@ -15,25 +15,27 @@ namespace mlir { typedef std::vector seed_t; + enum SearchMode { + /// + RootToLeaf, + /// + LeafToRoot, + /// TODO? + Chain + }; + class SeedAnalysis { public: explicit SeedAnalysis(Operation* module); - std::vector getSeeds(size_t const& op, SPNNodeLevel const& nodeLevels) const; + std::vector getSeeds(size_t const& op, + SPNNodeLevel const& nodeLevels, + SearchMode const& mode = RootToLeaf) const; private: - enum SearchMode { - /// Look for disjoint subgraphs in the operation tree. - DISJOINT, - /// Look for the largest possible subgraph. - GREEDY, - /// Stop looking for subgraphs. - FAILED - }; - Operation* module; }; diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPSeeding.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPSeeding.cpp index f1f5d9ff..7ebbfc9e 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPSeeding.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPSeeding.cpp @@ -13,7 +13,9 @@ using namespace mlir::spn::slp; SeedAnalysis::SeedAnalysis(Operation* module) : module{module} {} -std::vector SeedAnalysis::getSeeds(size_t const& width, SPNNodeLevel const& nodeLevels) const { +std::vector SeedAnalysis::getSeeds(size_t const& width, + SPNNodeLevel const& nodeLevels, + SearchMode const& mode) const { std::cout << "Starting seed computation!" << std::endl; @@ -27,7 +29,7 @@ std::vector SeedAnalysis::getSeeds(size_t const& width, SPNNodeLevel con } bool needsNewSeed = true; for (auto& seed : seedsByOpName[op->getName().getStringRef()]) { - if (seed.size() < width) { + if (seed.size() < width && nodeLevels.getOperationDepth(seed.front()) == depth) { seed.emplace_back(op); needsNewSeed = false; break; @@ -37,7 +39,6 @@ std::vector SeedAnalysis::getSeeds(size_t const& width, SPNNodeLevel con seed_t seed{op}; seedsByOpName[op->getName().getStringRef()].emplace_back(seed); } - }); std::vector seeds; @@ -50,6 +51,18 @@ std::vector SeedAnalysis::getSeeds(size_t const& width, SPNNodeLevel con seeds.emplace_back(seed); } } + // Sort the seeds such that either the seeds closest to the root come first (RootToLeaf), + // or those closest to the leaves (LeafToRoot). + std::sort(seeds.begin(), seeds.end(), [&](seed_t const& seed1, seed_t const& seed2) { + if (mode == RootToLeaf) { + return nodeLevels.getOperationDepth(seed1.front()) < nodeLevels.getOperationDepth(seed2.front()); + } else if (mode == LeafToRoot) { + return nodeLevels.getOperationDepth(seed1.front()) > nodeLevels.getOperationDepth(seed2.front()); + } else { + // Unused so far. + assert(false); + } + }); return seeds; } From 66f9f1ab84c37badbdd2465431ae1d8c1dd660e1 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Mon, 1 Feb 2021 03:03:40 +0100 Subject: [PATCH 031/385] Updated graph building to new seed analysis. --- .../Dialect/SPN/Analysis/SLP/SLPSeeding.h | 4 +- .../Dialect/SPN/Analysis/SLP/SLPTree.h | 8 +--- .../Dialect/SPN/Analysis/SLP/SLPSeeding.cpp | 8 +--- mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 23 ++-------- .../Dialect/SPN/Passes/SPNVectorization.cpp | 46 +++++++++---------- 5 files changed, 32 insertions(+), 57 deletions(-) diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h index 92349881..3f784237 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPSeeding.h @@ -28,7 +28,7 @@ namespace mlir { public: - explicit SeedAnalysis(Operation* module); + explicit SeedAnalysis(Operation* jointQuery); std::vector getSeeds(size_t const& op, SPNNodeLevel const& nodeLevels, @@ -36,7 +36,7 @@ namespace mlir { private: - Operation* module; + Operation* jointQuery; }; } diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h index ed190374..35df6081 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h @@ -13,6 +13,7 @@ #include "SPN/SPNOpTraits.h" #include "SLPMode.h" #include "SLPNode.h" +#include "SLPSeeding.h" #include #include @@ -21,16 +22,11 @@ namespace mlir { namespace spn { namespace slp { - /// - /// Graph class storing Use-Def chains of an SPN. class SLPTree { public: - /// Constructor, initialize analysis. - /// \param root Root node of a (sub-)graph or query operation. - /// \param width The target width of the SLP vectors. - explicit SLPTree(Operation* op, size_t width, size_t maxLookAhead); + explicit SLPTree(seed_t const& seed, size_t maxLookAhead); private: diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPSeeding.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPSeeding.cpp index 7ebbfc9e..db529f46 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPSeeding.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPSeeding.cpp @@ -4,25 +4,21 @@ // #include "SPN/Analysis/SLP/SLPSeeding.h" -#include #include using namespace mlir; using namespace mlir::spn; using namespace mlir::spn::slp; -SeedAnalysis::SeedAnalysis(Operation* module) : module{module} {} +SeedAnalysis::SeedAnalysis(Operation* jointQuery) : jointQuery{jointQuery} {} std::vector SeedAnalysis::getSeeds(size_t const& width, SPNNodeLevel const& nodeLevels, SearchMode const& mode) const { - std::cout << "Starting seed computation!" << std::endl; - llvm::StringMap> seedsByOpName; - module->walk([&](Operation* op) { - op->getLoc().dump(); + jointQuery->walk([&](Operation* op) { auto depth = nodeLevels.getOperationDepth(op); if (!op->hasTrait() || depth < log2(width)) { return; diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index 1d14e91c..251885ff 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -4,7 +4,6 @@ // #include "SPN/Analysis/SLP/SLPTree.h" -#include "SPN/Analysis/SLP/SLPSeeding.h" #include #include @@ -13,31 +12,15 @@ using namespace mlir; using namespace mlir::spn; using namespace mlir::spn::slp; -SLPTree::SLPTree(Operation* root, size_t width, size_t maxLookAhead) : graphs{}, maxLookAhead{maxLookAhead} { - assert(root); - llvm::StringMap> operationsByOpCode; - for (auto& op : root->getBlock()->getOperations()) { - operationsByOpCode[op.getName().getStringRef().str()].emplace_back(&op); - auto const& uses = std::distance(op.getUses().begin(), op.getUses().end()); - if (uses > 1) { - std::cerr << "SPN is not a tree!" << std::endl; - } - } -/* - auto const& seeds = seeding::getSeeds(root, 4); - for (auto const& seed : seeds) { - SLPNode rootNode{seed}; - buildGraph(seed, rootNode); - } - std::cout << "seeds computed" << std::endl; -*/ +SLPTree::SLPTree(seed_t const& seed, size_t maxLookAhead) : graphs{}, maxLookAhead{maxLookAhead} { + SLPNode rootNode{seed}; + buildGraph(seed, rootNode); } void SLPTree::buildGraph(std::vector const& operations, SLPNode& parentNode) { for (auto const& op : operations) { op->dump(); } - // TODO: handle binarizable > multinode conversion: if binarizable node, binarize it before going through here // Stop growing graph if (!vectorizable(operations)) { return; diff --git a/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp b/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp index 2c41b9dd..53d702f2 100644 --- a/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp +++ b/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp @@ -19,37 +19,37 @@ namespace { protected: void runOnOperation() override { + std::cout << "Starting SPN vectorization..." << std::endl; - auto module = getOperation(); + auto query = getOperation(); + + // ============ TREE CHECK ============ // + query.walk([&](Operation* topLevelOp) { + for (auto root : query.getRootNodes()) { + + llvm::StringMap> operationsByOpCode; + for (auto& op : root->getBlock()->getOperations()) { + operationsByOpCode[op.getName().getStringRef().str()].emplace_back(&op); + auto const& uses = std::distance(op.getUses().begin(), op.getUses().end()); + if (uses > 1) { + std::cerr << "SPN is not a tree!" << std::endl; + // TODO: how to handle such cases? and is special handling required at all? + assert(false); + } + } + } + }); + // ==================================== // auto& depthAnalysis = getAnalysis(); auto& seedAnalysis = getAnalysis(); + std::cout << "Starting seed computation!" << std::endl; auto seeds = seedAnalysis.getSeeds(4, depthAnalysis); - std::cout << "seeds computed" << std::endl; + std::cout << "Seeds computed" << std::endl; if (!seeds.empty()) { - std::cout << "binarized spn!" << std::endl; + slp::SLPTree graph(seeds.front(), 3); } - - module.walk([&](Operation* topLevelOp) { - if (auto query = dyn_cast(topLevelOp)) { - for (auto root : query.getRootNodes()) { - - // ============ TREE CHECK ============ - llvm::StringMap> operationsByOpCode; - for (auto& op : root->getBlock()->getOperations()) { - operationsByOpCode[op.getName().getStringRef().str()].emplace_back(&op); - auto const& uses = std::distance(op.getUses().begin(), op.getUses().end()); - if (uses > 1) { - std::cerr << "SPN is not a tree!" << std::endl; - } - } - // ==================================== - - slp::SLPTree graph(root, 4, 3); - } - } - }); } }; From 9001da6587264fe76ab75ebac554958c0fccdcbb Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Mon, 1 Feb 2021 04:38:26 +0100 Subject: [PATCH 032/385] Added subgraph printing method for debugging purposes. --- .../Dialect/SPN/Analysis/SLP/SLPTree.h | 42 +++++++++++++++++++ mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 1 + 2 files changed, 43 insertions(+) diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h index 35df6081..9f6f560d 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h @@ -17,6 +17,8 @@ #include #include +#include +#include namespace mlir { namespace spn { @@ -79,7 +81,47 @@ namespace mlir { std::pair, Mode> getBest(Mode const& mode, SLPNode const& last, std::vector& candidates) const; + int getLookAheadScore(SLPNode const& last, SLPNode const& candidate, size_t const& maxLevel) const; + + /// For debugging purposes. + static void printSubgraph(std::vector const& operations) { + std::set nodes; + std::vector> edges; + + std::stack worklist; + for (auto& op : operations) { + worklist.push(op); + } + + while (!worklist.empty()) { + + auto op = worklist.top(); + worklist.pop(); + + if (nodes.find(op) == nodes.end()) { + nodes.insert(op); + for (auto operand : op->getOperands()) { + if (operand.getDefiningOp() != nullptr) { + edges.emplace_back(std::make_pair(op, operand.getDefiningOp())); + worklist.push(operand.getDefiningOp()); + } + } + } + } + + llvm::errs() << "digraph debug_graph {\n"; + llvm::errs() << "rankdir = BT;\n"; + llvm::errs() << "node[shape=box];\n"; + for (auto& op : nodes) { + llvm::errs() << "node_" << op << "[label=\"" << op->getName().getStringRef() << "\\n" << op << "\"];\n"; + } + for (auto& edge : edges) { + llvm::errs() << "node_" << edge.first << " -> node_" << edge.second << ";\n"; + } + llvm::errs() << "}\n"; + } + }; } } diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index 251885ff..c37e0d38 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -20,6 +20,7 @@ SLPTree::SLPTree(seed_t const& seed, size_t maxLookAhead) : graphs{}, maxLookAhe void SLPTree::buildGraph(std::vector const& operations, SLPNode& parentNode) { for (auto const& op : operations) { op->dump(); + op->getLoc()->dump(); } // Stop growing graph if (!vectorizable(operations)) { From 4ae217790228bb2a8bb461f0d56d495e6337f147 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Tue, 2 Feb 2021 04:44:37 +0100 Subject: [PATCH 033/385] Refactoring of buildGraph(). --- .../Dialect/SPN/Analysis/SLP/SLPNode.h | 21 ++--- .../Dialect/SPN/Analysis/SLP/SLPTree.h | 79 ++++++++++------ mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp | 60 ++---------- mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 93 ++++++++++++------- 4 files changed, 124 insertions(+), 129 deletions(-) diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h index 8ee7385e..eeed2cb1 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h @@ -19,23 +19,17 @@ namespace mlir { public: explicit SLPNode(std::vector const& operations); - explicit SLPNode(Operation* operation); - - void addOperands(std::vector> const& operandsPerLane); - void addOperandsToLane(std::vector const& operations, size_t const& lane); - std::vector const& getOperands(size_t const& lane) const; - SLPNode const& getOperand(size_t const& lane, size_t const& index) const; + void addOperationToLane(Operation* operation, size_t const& lane); + std::vector getLastOperations() const; Operation* getOperation(size_t lane, size_t index); - OperationName const& name() const; bool isMultiNode() const; size_t numLanes() const; - bool attachable(std::vector> const& operations); friend bool operator==(SLPNode const& lhs, SLPNode const& rhs) { - return std::tie(lhs.operationName, lhs.lanes, lhs.operands) - == std::tie(rhs.operationName, rhs.lanes, rhs.operands); + return std::tie(lhs.lanes, lhs.operands) + == std::tie(rhs.lanes, rhs.operands); } friend bool operator!=(SLPNode const& lhs, SLPNode const& rhs) { @@ -44,16 +38,15 @@ namespace mlir { private: - OperationName operationName; - /// Stores lanes as lists of operations. An inner vector (i.e. a lane) only contains more than one operation /// if this node is a multinode. std::vector> lanes; - /// A list of operands for each lane. - std::vector> operands; + /// A list of operands in the form of other SLPNodes. + std::vector operands; }; + } } } diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h index 9f6f560d..68a59887 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h @@ -7,32 +7,36 @@ #define SPNC_MLIR_DIALECTS_INCLUDE_DIALECT_SPN_ANALYSIS_SLP_SLPGRAPH_H #include "mlir/IR/Operation.h" -#include "mlir/IR/OpDefinition.h" -#include "llvm/ADT/SmallPtrSet.h" -#include "llvm/ADT/StringMap.h" #include "SPN/SPNOpTraits.h" #include "SLPMode.h" #include "SLPNode.h" #include "SLPSeeding.h" -#include -#include -#include #include namespace mlir { namespace spn { namespace slp { + typedef std::shared_ptr node_t; + class SLPTree { public: - explicit SLPTree(seed_t const& seed, size_t maxLookAhead); + SLPTree(seed_t const& seed, size_t const& maxLookAhead); private: - void buildGraph(std::vector const& operations, SLPNode& parentNode); + void buildGraph(std::vector const& operations, node_t const& currentNode); + + std::vector> reorderOperands(node_t const& multinode); + + std::pair, Mode> getBest(Mode const& mode, + node_t const& last, + std::vector& candidates) const; + + int getLookAheadScore(node_t const& last, node_t const& candidate, size_t const& maxLevel) const; /// Checks if the given operations are vectorizable. Operations are vectorizable iff the SPN dialect says they're /// vectorizable and they all share the same opcode. @@ -54,35 +58,50 @@ namespace mlir { }); } - static std::vector getOperands(std::vector const& values) { - std::vector operands; - for (auto const& operation : values) { + static bool escapesMultinode(Operation* operation) { + // TODO: check if some intermediate, temporary value of a multinode is used outside of it + return false; + } + + static std::vector> getOperands(std::vector const& operations) { + std::vector> allOperands; + for (auto* operation : operations) { + std::vector operands; + operands.reserve(operation->getNumOperands()); for (auto operand : operation->getOperands()) { operands.emplace_back(operand.getDefiningOp()); } + allOperands.emplace_back(operands); } - return operands; + return allOperands; } - static std::vector getOperands(Operation* operation) { - std::vector operands; - for (auto operand : operation->getOperands()) { - operands.emplace_back(operand.getDefiningOp()); + static std::vector> getOperandsTransposed(std::vector const& operations) { + std::vector> allOperands; + for (size_t i = 0; i < operations.front()->getNumOperands(); ++i) { + std::vector operand; + operand.reserve(operations.size()); + for (auto* operation : operations) { + operand.emplace_back(operation->getOperand(i).getDefiningOp()); + } + allOperands.emplace_back(operand); } - return operands; + return allOperands; } - std::vector> reorderOperands(SLPNode& multinode); - - std::vector graphs; - - size_t const maxLookAhead; - - std::pair, Mode> getBest(Mode const& mode, - SLPNode const& last, - std::vector& candidates) const; - - int getLookAheadScore(SLPNode const& last, SLPNode const& candidate, size_t const& maxLevel) const; + static void sortByOpcode(std::vector& operations, + Optional const& smallestOpcode) { + std::sort(std::begin(operations), std::end(operations), [&](Operation* lhs, Operation* rhs) { + if (smallestOpcode.hasValue()) { + if (lhs->getName() == *smallestOpcode) { + return rhs->getName() != *smallestOpcode; + } else if (rhs->getName() == *smallestOpcode) { + return false; + } + } + return lhs->getName().getStringRef() < rhs->getName().getStringRef(); + }); + } /// For debugging purposes. static void printSubgraph(std::vector const& operations) { @@ -122,6 +141,10 @@ namespace mlir { llvm::errs() << "}\n"; } + std::map> operandsOf; + + size_t const maxLookAhead; + }; } } diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp index 82c5e3c2..efb790a8 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp @@ -11,61 +11,29 @@ using namespace mlir; using namespace mlir::spn; using namespace mlir::spn::slp; -SLPNode::SLPNode(std::vector const& operations) : operationName{operations.front()->getName()}, - lanes{operations.size()}, +SLPNode::SLPNode(std::vector const& operations) : lanes{operations.size()}, operands{operations.size()} { for (size_t i = 0; i < operations.size(); ++i) { - assert(i == 0 || operations.at(i)->getName() == operationName); lanes.at(i).emplace_back(operations.at(i)); } } -SLPNode::SLPNode(Operation* operation) : operationName{operation->getName()}, - lanes{}, - operands{} {} - -void SLPNode::addOperands(std::vector> const& operandsPerLane) { - assert(operandsPerLane.size() == numLanes()); - // If the operations are attachable (i.e. same opcode), insert them into this node (forming a multinode). - if (attachable(operandsPerLane)) { - for (size_t lane = 0; lane < numLanes(); ++lane) { - lanes.at(lane).insert(std::end(lanes.at(lane)), - std::begin(operandsPerLane.at(lane)), - std::end(operandsPerLane.at(lane))); - } - } - // Otherwise, don't modify this node's operations and add the operands as true operands. - else { - for (size_t lane = 0; lane < numLanes(); ++lane) { - addOperandsToLane(operandsPerLane.at(lane), lane); - } - } - +void SLPNode::addOperationToLane(Operation* operation, size_t const& lane) { + lanes.at(lane).emplace_back(operation); } -void SLPNode::addOperandsToLane(std::vector const& operations, size_t const& lane) { - assert(operations.size() == lanes.at(lane).back()->getNumOperands()); - for (auto const& operand : operations) { - operands.at(lane).emplace_back(operand); +std::vector SLPNode::getLastOperations() const { + std::vector lastOperations; + for (size_t lane = 0; lane < numLanes(); ++lane) { + lastOperations.emplace_back(lanes.at(lane).back()); } -} - -std::vector const& SLPNode::getOperands(size_t const& lane) const { - return operands.at(lane); -} - -SLPNode const& SLPNode::getOperand(size_t const& lane, size_t const& index) const { - return operands.at(lane).at(index); + return lastOperations; } Operation* SLPNode::getOperation(size_t lane, size_t index) { return lanes.at(lane).at(index); } -OperationName const& SLPNode::name() const { - return operationName; -} - bool SLPNode::isMultiNode() const { return lanes.front().size() > 1; } @@ -73,15 +41,3 @@ bool SLPNode::isMultiNode() const { size_t SLPNode::numLanes() const { return lanes.size(); } - -bool SLPNode::attachable(std::vector> const& operations) { - // TODO operands escape multi-node? - for (auto const& laneOperands : operations) { - if (std::any_of(std::begin(laneOperands), - std::end(laneOperands), - [&](auto const& operandOperation) { return operandOperation->getName() != operationName; })) { - return false; - } - } - return true; -} diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index c37e0d38..9ce23f58 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -5,64 +5,86 @@ #include "SPN/Analysis/SLP/SLPTree.h" -#include #include using namespace mlir; using namespace mlir::spn; using namespace mlir::spn::slp; -SLPTree::SLPTree(seed_t const& seed, size_t maxLookAhead) : graphs{}, maxLookAhead{maxLookAhead} { - SLPNode rootNode{seed}; - buildGraph(seed, rootNode); +SLPTree::SLPTree(seed_t const& seed, size_t const& maxLookAhead) : operandsOf{}, maxLookAhead{maxLookAhead} { + auto const& currentNode = std::make_shared(seed); + operandsOf[currentNode] = {}; + buildGraph(seed, currentNode); } -void SLPTree::buildGraph(std::vector const& operations, SLPNode& parentNode) { - for (auto const& op : operations) { +void SLPTree::buildGraph(std::vector const& operations, node_t const& currentNode) { + llvm::errs() << "Building graph with:\n"; + for (auto* op : operations) { + llvm::errs() << op << ": "; op->dump(); - op->getLoc()->dump(); } // Stop growing graph if (!vectorizable(operations)) { return; } - // Create new node for values and add to graph - SLPNode& currentNode = parentNode; + auto const& currentOpCode = operations.front()->getName(); // Recursion call to grow graph further // 1. Commutative if (commutative(operations)) { // A. Coarsening Mode - for (auto const& operation : operations) { - buildGraph(getOperands(operation), currentNode); + auto allOperands = getOperands(operations); + for (auto& operands : allOperands) { + sortByOpcode(operands, currentOpCode); } + for (size_t i = 0; i < operations.front()->getNumOperands(); ++i) { + auto const& operand = allOperands.at(i); + if (std::all_of(std::begin(operand), std::end(operand), [&](auto* operation) { + return operation->getName() == currentOpCode && !escapesMultinode(operation); + })) { + for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { + currentNode->addOperationToLane(operand.at(lane), lane); + } + buildGraph(currentNode->getLastOperations(), currentNode); + } else { + operandsOf[currentNode].emplace_back(std::make_shared(operand)); + } + } + // B. Normal Mode: Finished building multi-node - if (currentNode.isMultiNode()) { + if (currentNode->isMultiNode()) { reorderOperands(currentNode); - // TODO buildGraph() + for (auto& operandNode : operandsOf.at(currentNode)) { + buildGraph(operandNode->getLastOperations(), currentNode); + } } } // 2. Non-Commutative else { - buildGraph(getOperands(operations), currentNode); + std::vector> operands; + for (auto const& operandOperations : getOperandsTransposed(operations)) { + auto const& operandNode = std::make_shared(operandOperations); + operandsOf[currentNode].emplace_back(operandNode); + buildGraph(operandOperations, operandNode); + } } } -std::vector> SLPTree::reorderOperands(SLPNode& multinode) { - assert(multinode.isMultiNode()); - std::vector> finalOrder{multinode.numLanes()}; +std::vector> SLPTree::reorderOperands(node_t const& multinode) { + assert(multinode->isMultiNode()); + std::vector> finalOrder{multinode->numLanes()}; std::vector mode; - auto const& numOperands = multinode.getOperands(0).size(); + auto const& numOperands = operandsOf.at(multinode).size(); // 1. Strip first lane for (size_t i = 0; i < numOperands; ++i) { - auto operand = multinode.getOperand(0, i); + auto operand = operandsOf.at(multinode).at(i); finalOrder.at(0).emplace_back(operand); - mode.emplace_back(modeFromOperation(operand.getOperation(0, 0))); + mode.emplace_back(modeFromOperation(operand->getOperation(0, 0))); } // 2. For all other lanes, find best candidate - for (size_t lane = 1; lane < multinode.numLanes(); ++lane) { - std::vector candidates{multinode.getOperands(lane)}; + for (size_t lane = 1; lane < multinode->numLanes(); ++lane) { + std::vector candidates{operandsOf.at(multinode)}; // Look for a matching candidate for (size_t i = 0; i < numOperands; ++i) { // Skip if we can't vectorize @@ -87,12 +109,12 @@ std::vector> SLPTree::reorderOperands(SLPNode& multinode) { return finalOrder; } -std::pair, Mode> SLPTree::getBest(Mode const& mode, - SLPNode const& last, - std::vector& candidates) const { - Optional best; +std::pair, Mode> SLPTree::getBest(Mode const& mode, + node_t const& last, + std::vector& candidates) const { + Optional best; Mode resultMode = mode; - std::vector bestCandidates; + std::vector bestCandidates; if (mode == FAILED) { // Don't select now, let others choose first @@ -110,9 +132,9 @@ std::pair, Mode> SLPTree::getBest(Mode const& mode, best = candidates.front(); for (auto& candidate : candidates) { // We don't have consecutive memory accesses, therefore we're only interested in opcode comparisons. - if (candidate.name() == last.name()) { - bestCandidates.emplace_back(candidate); - } + //if (candidate.name() == last.name()) { + bestCandidates.emplace_back(candidate); + //} } // 1. If we have a trivial solution, use it // No matches @@ -155,15 +177,16 @@ std::pair, Mode> SLPTree::getBest(Mode const& mode, } return {best, resultMode}; } -int SLPTree::getLookAheadScore(SLPNode const& last, SLPNode const& candidate, size_t const& maxLevel) const { + +int SLPTree::getLookAheadScore(node_t const& last, node_t const& candidate, size_t const& maxLevel) const { if (maxLevel == 0) { // No consecutive loads to check, only opcodes. - return last.name() == candidate.name() ? 1 : 0; + return /*last.name() == candidate.name() ? 1 :*/ 0; } auto scoreSum = 0; - for (size_t lane = 0; lane < last.numLanes(); ++lane) { - for (auto const& lastOperand : last.getOperands(lane)) { - for (auto const& candidateOperand : candidate.getOperands(lane)) { + for (size_t lane = 0; lane < last->numLanes(); ++lane) { + for (auto const& lastOperand : operandsOf.at(last)) { + for (auto const& candidateOperand : operandsOf.at(candidate)) { scoreSum += getLookAheadScore(lastOperand, candidateOperand, maxLevel - 1); } } From d68e83c812e712ee3558ce248cd0296bdd10a349 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Tue, 2 Feb 2021 05:08:16 +0100 Subject: [PATCH 034/385] Fixed operand construction. --- mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index 9ce23f58..04585d06 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -37,16 +37,19 @@ void SLPTree::buildGraph(std::vector const& operations, node_t const sortByOpcode(operands, currentOpCode); } for (size_t i = 0; i < operations.front()->getNumOperands(); ++i) { - auto const& operand = allOperands.at(i); - if (std::all_of(std::begin(operand), std::end(operand), [&](auto* operation) { - return operation->getName() == currentOpCode && !escapesMultinode(operation); + if (std::all_of(std::begin(allOperands), std::end(allOperands), [&](auto const& operandOperations) { + return operandOperations.at(i)->getName() == currentOpCode && !escapesMultinode(operandOperations.at(i)); })) { for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { - currentNode->addOperationToLane(operand.at(lane), lane); + currentNode->addOperationToLane(allOperands.at(lane).at(i), lane); } buildGraph(currentNode->getLastOperations(), currentNode); } else { - operandsOf[currentNode].emplace_back(std::make_shared(operand)); + std::vector operandOperations; + for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { + operandOperations.emplace_back(allOperands.at(lane).at(i)); + } + operandsOf[currentNode].emplace_back(std::make_shared(operandOperations)); } } From a28654cdcf5c747c9916a29121d109ed8e0d73cb Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Tue, 2 Feb 2021 05:53:43 +0100 Subject: [PATCH 035/385] Added rootNode() check, better typedef encapsulation. --- mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h | 8 +++----- mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h | 4 ++-- mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp | 12 ++++++++++-- mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 13 ++++++++----- 4 files changed, 23 insertions(+), 14 deletions(-) diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h index eeed2cb1..1c99135f 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h @@ -25,11 +25,12 @@ namespace mlir { Operation* getOperation(size_t lane, size_t index); bool isMultiNode() const; + bool areRootOfNode(std::vector const& operations) const; size_t numLanes() const; friend bool operator==(SLPNode const& lhs, SLPNode const& rhs) { - return std::tie(lhs.lanes, lhs.operands) - == std::tie(rhs.lanes, rhs.operands); + return std::tie(lhs.lanes) + == std::tie(rhs.lanes); } friend bool operator!=(SLPNode const& lhs, SLPNode const& rhs) { @@ -42,9 +43,6 @@ namespace mlir { /// if this node is a multinode. std::vector> lanes; - /// A list of operands in the form of other SLPNodes. - std::vector operands; - }; } diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h index 68a59887..090399ad 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h @@ -18,10 +18,10 @@ namespace mlir { namespace spn { namespace slp { - typedef std::shared_ptr node_t; - class SLPTree { + typedef std::shared_ptr node_t; + public: SLPTree(seed_t const& seed, size_t const& maxLookAhead); diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp index efb790a8..47660bf3 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp @@ -11,8 +11,7 @@ using namespace mlir; using namespace mlir::spn; using namespace mlir::spn::slp; -SLPNode::SLPNode(std::vector const& operations) : lanes{operations.size()}, - operands{operations.size()} { +SLPNode::SLPNode(std::vector const& operations) : lanes{operations.size()} { for (size_t i = 0; i < operations.size(); ++i) { lanes.at(i).emplace_back(operations.at(i)); } @@ -38,6 +37,15 @@ bool SLPNode::isMultiNode() const { return lanes.front().size() > 1; } +bool SLPNode::areRootOfNode(std::vector const& operations) const { + for (size_t lane = 0; lane < numLanes(); ++lane) { + if (operations.at(lane) != lanes.at(lane).front()) { + return false; + } + } + return true; +} + size_t SLPNode::numLanes() const { return lanes.size(); } diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index 04585d06..3d94937e 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -49,12 +49,15 @@ void SLPTree::buildGraph(std::vector const& operations, node_t const for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { operandOperations.emplace_back(allOperands.at(lane).at(i)); } + for (auto const& tmp : operandOperations) { + tmp->dump(); + } operandsOf[currentNode].emplace_back(std::make_shared(operandOperations)); } } // B. Normal Mode: Finished building multi-node - if (currentNode->isMultiNode()) { + if (currentNode->isMultiNode() && currentNode->areRootOfNode(operations)) { reorderOperands(currentNode); for (auto& operandNode : operandsOf.at(currentNode)) { buildGraph(operandNode->getLastOperations(), currentNode); @@ -73,7 +76,7 @@ void SLPTree::buildGraph(std::vector const& operations, node_t const } -std::vector> SLPTree::reorderOperands(node_t const& multinode) { +std::vector> SLPTree::reorderOperands(node_t const& multinode) { assert(multinode->isMultiNode()); std::vector> finalOrder{multinode->numLanes()}; std::vector mode; @@ -112,9 +115,9 @@ std::vector> SLPTree::reorderOperands(node_t const& multinod return finalOrder; } -std::pair, Mode> SLPTree::getBest(Mode const& mode, - node_t const& last, - std::vector& candidates) const { +std::pair, Mode> SLPTree::getBest(Mode const& mode, + node_t const& last, + std::vector& candidates) const { Optional best; Mode resultMode = mode; std::vector bestCandidates; From c41ca5b24c1cf3928edec713104807c82ad0442f Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Tue, 9 Feb 2021 20:45:24 +0100 Subject: [PATCH 036/385] Vectorization progress: operand reordering. --- .../Dialect/SPN/Analysis/SLP/SLPTree.h | 34 +++++---- mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 69 ++++++++++--------- 2 files changed, 57 insertions(+), 46 deletions(-) diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h index 090399ad..fb0af69e 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h @@ -30,13 +30,13 @@ namespace mlir { void buildGraph(std::vector const& operations, node_t const& currentNode); - std::vector> reorderOperands(node_t const& multinode); + std::vector> reorderOperands(node_t const& multinode); - std::pair, Mode> getBest(Mode const& mode, - node_t const& last, - std::vector& candidates) const; + std::pair getBest(Mode const& mode, + Operation* last, + std::vector& candidates) const; - int getLookAheadScore(node_t const& last, node_t const& candidate, size_t const& maxLevel) const; + int getLookAheadScore(Operation* last, Operation* candidate, size_t const& maxLevel) const; /// Checks if the given operations are vectorizable. Operations are vectorizable iff the SPN dialect says they're /// vectorizable and they all share the same opcode. @@ -60,18 +60,24 @@ namespace mlir { static bool escapesMultinode(Operation* operation) { // TODO: check if some intermediate, temporary value of a multinode is used outside of it + assert(operation); return false; } + static std::vector getOperands(Operation* operation) { + std::vector operands; + operands.reserve(operation->getNumOperands()); + for (auto operand : operation->getOperands()) { + operands.emplace_back(operand.getDefiningOp()); + } + return operands; + } + static std::vector> getOperands(std::vector const& operations) { std::vector> allOperands; + allOperands.reserve(operations.size()); for (auto* operation : operations) { - std::vector operands; - operands.reserve(operation->getNumOperands()); - for (auto operand : operation->getOperands()) { - operands.emplace_back(operand.getDefiningOp()); - } - allOperands.emplace_back(operands); + allOperands.emplace_back(getOperands(operation)); } return allOperands; } @@ -93,9 +99,9 @@ namespace mlir { Optional const& smallestOpcode) { std::sort(std::begin(operations), std::end(operations), [&](Operation* lhs, Operation* rhs) { if (smallestOpcode.hasValue()) { - if (lhs->getName() == *smallestOpcode) { - return rhs->getName() != *smallestOpcode; - } else if (rhs->getName() == *smallestOpcode) { + if (lhs->getName() == smallestOpcode.getValue()) { + return rhs->getName() != smallestOpcode.getValue(); + } else if (rhs->getName() == smallestOpcode.getValue()) { return false; } } diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index 3d94937e..2cbf8025 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -45,6 +45,7 @@ void SLPTree::buildGraph(std::vector const& operations, node_t const } buildGraph(currentNode->getLastOperations(), currentNode); } else { + // TODO: here might be a good place to implement variable vector width std::vector operandOperations; for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { operandOperations.emplace_back(allOperands.at(lane).at(i)); @@ -58,7 +59,8 @@ void SLPTree::buildGraph(std::vector const& operations, node_t const // B. Normal Mode: Finished building multi-node if (currentNode->isMultiNode() && currentNode->areRootOfNode(operations)) { - reorderOperands(currentNode); + auto const& order = reorderOperands(currentNode); + // TODO: reorder based on reordering results for (auto& operandNode : operandsOf.at(currentNode)) { buildGraph(operandNode->getLastOperations(), currentNode); } @@ -76,38 +78,41 @@ void SLPTree::buildGraph(std::vector const& operations, node_t const } -std::vector> SLPTree::reorderOperands(node_t const& multinode) { +std::vector> SLPTree::reorderOperands(node_t const& multinode) { assert(multinode->isMultiNode()); - std::vector> finalOrder{multinode->numLanes()}; - std::vector mode; auto const& numOperands = operandsOf.at(multinode).size(); + std::vector> finalOrder{multinode->numLanes()}; + std::vector> mode{multinode->numLanes()}; // 1. Strip first lane for (size_t i = 0; i < numOperands; ++i) { - auto operand = operandsOf.at(multinode).at(i); - finalOrder.at(0).emplace_back(operand); - mode.emplace_back(modeFromOperation(operand->getOperation(0, 0))); + auto operation = operandsOf.at(multinode).at(i)->getOperation(0, 0); + finalOrder.at(0).emplace_back(operation); + mode.at(0).emplace_back(modeFromOperation(operation)); } // 2. For all other lanes, find best candidate for (size_t lane = 1; lane < multinode->numLanes(); ++lane) { - std::vector candidates{operandsOf.at(multinode)}; + std::vector candidates; + for (auto const& operand : operandsOf.at(multinode)) { + candidates.emplace_back(operand->getOperation(lane, 0)); + } // Look for a matching candidate for (size_t i = 0; i < numOperands; ++i) { // Skip if we can't vectorize - if (mode.at(i) == FAILED) { + if (mode.at(lane - 1).at(i) == FAILED) { continue; } - auto const& last = finalOrder.at(lane - 1).at(i); - auto const& bestResult = getBest(mode.at(i), last, candidates); + auto* last = finalOrder.at(lane - 1).at(i); + auto const& bestResult = getBest(mode.at(lane - 1).at(i), last, candidates); // Update output - finalOrder.at(lane).emplace_back(bestResult.first.getValue()); + finalOrder.at(lane).emplace_back(bestResult.first); // Detect SPLAT mode if (i == 1 && bestResult.first == last) { - mode.at(i) = SPLAT; + mode.at(lane).at(i) = SPLAT; } else { - mode.at(i) = bestResult.second; + mode.at(lane).at(i) = bestResult.second; } } @@ -115,16 +120,16 @@ std::vector> SLPTree::reorderOperands(node_t const& return finalOrder; } -std::pair, Mode> SLPTree::getBest(Mode const& mode, - node_t const& last, - std::vector& candidates) const { - Optional best; +std::pair SLPTree::getBest(Mode const& mode, + Operation* last, + std::vector& candidates) const { + Operation* best = nullptr; Mode resultMode = mode; - std::vector bestCandidates; + std::vector bestCandidates; if (mode == FAILED) { // Don't select now, let others choose first - best.reset(); + best = nullptr; } else if (mode == SPLAT) { // Look for other splat candidates for (auto& operand : candidates) { @@ -138,9 +143,9 @@ std::pair, Mode> SLPTree::getBest(Mode const& mode, best = candidates.front(); for (auto& candidate : candidates) { // We don't have consecutive memory accesses, therefore we're only interested in opcode comparisons. - //if (candidate.name() == last.name()) { - bestCandidates.emplace_back(candidate); - //} + if (candidate->getName() == last->getName()) { + bestCandidates.emplace_back(candidate); + } } // 1. If we have a trivial solution, use it // No matches @@ -169,7 +174,7 @@ std::pair, Mode> SLPTree::getBest(Mode const& mode, } } // If found best at level don't go deeper - if (best.hasValue() && scores.size() > 1) { + if (best != nullptr && scores.size() > 1) { break; } } @@ -178,23 +183,23 @@ std::pair, Mode> SLPTree::getBest(Mode const& mode, } // Remove best from candidates - if (best.hasValue()) { + if (best != nullptr) { candidates.erase(std::remove(std::begin(candidates), std::end(candidates), best), std::end(candidates)); } return {best, resultMode}; } -int SLPTree::getLookAheadScore(node_t const& last, node_t const& candidate, size_t const& maxLevel) const { +int SLPTree::getLookAheadScore(Operation* last, Operation* candidate, size_t const& maxLevel) const { if (maxLevel == 0) { // No consecutive loads to check, only opcodes. - return /*last.name() == candidate.name() ? 1 :*/ 0; + return last->getName() == candidate->getName() ? 1 : 0; } auto scoreSum = 0; - for (size_t lane = 0; lane < last->numLanes(); ++lane) { - for (auto const& lastOperand : operandsOf.at(last)) { - for (auto const& candidateOperand : operandsOf.at(candidate)) { - scoreSum += getLookAheadScore(lastOperand, candidateOperand, maxLevel - 1); - } + for (auto const& lastOperand : getOperands(last)) { + for (auto const& candidateOperand : getOperands(candidate)) { + // TODO: if operands are commutative, sort operands + scoreSum += getLookAheadScore(lastOperand, candidateOperand, maxLevel - 1); + } } return scoreSum; From 97653250415733f02b3b4a4468aeefd423c640e8 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Wed, 10 Feb 2021 03:27:41 +0100 Subject: [PATCH 037/385] Fixed segmentation fault, added TODOs. --- mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index 2cbf8025..9fa04012 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -100,6 +100,7 @@ std::vector> SLPTree::reorderOperands(node_t const& mult for (size_t i = 0; i < numOperands; ++i) { // Skip if we can't vectorize if (mode.at(lane - 1).at(i) == FAILED) { + mode.at(lane).emplace_back(FAILED); continue; } auto* last = finalOrder.at(lane - 1).at(i); @@ -112,7 +113,7 @@ std::vector> SLPTree::reorderOperands(node_t const& mult if (i == 1 && bestResult.first == last) { mode.at(lane).at(i) = SPLAT; } else { - mode.at(lane).at(i) = bestResult.second; + mode.at(lane).emplace_back(bestResult.second); } } @@ -160,6 +161,7 @@ std::pair SLPTree::getBest(Mode const& mode, else { if (mode == OPCODE) { // Look-ahead on various levels + // TODO: when the level is increased, we recompute everything from the level before. change that maybe? for (size_t level = 1; level <= maxLookAhead; ++level) { // Best is the candidate with max score auto bestScore = 0; @@ -195,9 +197,9 @@ int SLPTree::getLookAheadScore(Operation* last, Operation* candidate, size_t con return last->getName() == candidate->getName() ? 1 : 0; } auto scoreSum = 0; + // TODO: if operands are commutative, sort operands to speed up lookahead? for (auto const& lastOperand : getOperands(last)) { for (auto const& candidateOperand : getOperands(candidate)) { - // TODO: if operands are commutative, sort operands scoreSum += getLookAheadScore(lastOperand, candidateOperand, maxLevel - 1); } From 429a94b7d0bbfe5d7658192de700eeaa5859b7fd Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Thu, 11 Feb 2021 03:39:05 +0100 Subject: [PATCH 038/385] Fix: final order not containing every operand. --- mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index 9fa04012..fcb77640 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -99,7 +99,9 @@ std::vector> SLPTree::reorderOperands(node_t const& mult // Look for a matching candidate for (size_t i = 0; i < numOperands; ++i) { // Skip if we can't vectorize + // TODO: here might also be a good place to start looking for variable-width if (mode.at(lane - 1).at(i) == FAILED) { + finalOrder.at(lane).emplace_back(nullptr); mode.at(lane).emplace_back(FAILED); continue; } @@ -117,6 +119,15 @@ std::vector> SLPTree::reorderOperands(node_t const& mult } } + // Distribute remaining candidates in case we encountered a FAILED. + for (auto* candidate : candidates) { + for (size_t i = 0; i < numOperands; ++i) { + if (finalOrder.at(lane).at(i) == nullptr) { + finalOrder.at(lane).at(i) = candidate; + break; + } + } + } } return finalOrder; } From 433a75db53eeb386619c618112f044fea4ed34ce Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Thu, 11 Feb 2021 04:05:07 +0100 Subject: [PATCH 039/385] Fix: reordering now actually reorders operations. --- mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h | 3 ++- mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp | 6 +++++- mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 6 +++++- 3 files changed, 12 insertions(+), 3 deletions(-) diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h index 1c99135f..85553029 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h @@ -22,7 +22,8 @@ namespace mlir { void addOperationToLane(Operation* operation, size_t const& lane); std::vector getLastOperations() const; - Operation* getOperation(size_t lane, size_t index); + Operation* getOperation(size_t lane, size_t index) const; + void setOperation(size_t lane, size_t index, Operation* operation); bool isMultiNode() const; bool areRootOfNode(std::vector const& operations) const; diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp index 47660bf3..deba5205 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp @@ -29,10 +29,14 @@ std::vector SLPNode::getLastOperations() const { return lastOperations; } -Operation* SLPNode::getOperation(size_t lane, size_t index) { +Operation* SLPNode::getOperation(size_t lane, size_t index) const { return lanes.at(lane).at(index); } +void SLPNode::setOperation(size_t lane, size_t index, Operation* operation) { + lanes.at(lane).at(index) = operation; +} + bool SLPNode::isMultiNode() const { return lanes.front().size() > 1; } diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index fcb77640..2bc4e1b4 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -60,7 +60,11 @@ void SLPTree::buildGraph(std::vector const& operations, node_t const // B. Normal Mode: Finished building multi-node if (currentNode->isMultiNode() && currentNode->areRootOfNode(operations)) { auto const& order = reorderOperands(currentNode); - // TODO: reorder based on reordering results + for (size_t operandIndex = 0; operandIndex < operandsOf.at(currentNode).size(); ++operandIndex) { + for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { + operandsOf.at(currentNode).at(operandIndex)->setOperation(lane, 0, order.at(lane).at(operandIndex)); + } + } for (auto& operandNode : operandsOf.at(currentNode)) { buildGraph(operandNode->getLastOperations(), currentNode); } From 1d9ec5fc130b9ad9f24766480b2049de4940d917 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Thu, 11 Feb 2021 04:32:39 +0100 Subject: [PATCH 040/385] Better subgraph printing. --- mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h | 3 ++- mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h | 10 ++++++---- mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp | 2 -- mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 4 +--- 4 files changed, 9 insertions(+), 10 deletions(-) diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h index 85553029..c7ebb343 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h @@ -41,7 +41,8 @@ namespace mlir { private: /// Stores lanes as lists of operations. An inner vector (i.e. a lane) only contains more than one operation - /// if this node is a multinode. + /// if this node is a multinode. Operations with smaller indices inside a lane are executed "after" the higher + /// ones in the source code. std::vector> lanes; }; diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h index fb0af69e..7591941c 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h @@ -112,7 +112,7 @@ namespace mlir { /// For debugging purposes. static void printSubgraph(std::vector const& operations) { std::set nodes; - std::vector> edges; + std::vector> edges; std::stack worklist; for (auto& op : operations) { @@ -126,9 +126,10 @@ namespace mlir { if (nodes.find(op) == nodes.end()) { nodes.insert(op); - for (auto operand : op->getOperands()) { + for (size_t i = 0; i < op->getNumOperands(); ++i) { + auto const& operand = op->getOperand(i); if (operand.getDefiningOp() != nullptr) { - edges.emplace_back(std::make_pair(op, operand.getDefiningOp())); + edges.emplace_back(std::make_tuple(op, operand.getDefiningOp(), i)); worklist.push(operand.getDefiningOp()); } } @@ -142,7 +143,8 @@ namespace mlir { llvm::errs() << "node_" << op << "[label=\"" << op->getName().getStringRef() << "\\n" << op << "\"];\n"; } for (auto& edge : edges) { - llvm::errs() << "node_" << edge.first << " -> node_" << edge.second << ";\n"; + llvm::errs() << "node_" << std::get<0>(edge) << " -> node_" << std::get<1>(edge) << "[label=\"" + << std::get<2>(edge) << "\"];\n"; } llvm::errs() << "}\n"; } diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp index deba5205..0f6c4648 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp @@ -5,8 +5,6 @@ #include "SPN/Analysis/SLP/SLPNode.h" -#include - using namespace mlir; using namespace mlir::spn; using namespace mlir::spn::slp; diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index 2bc4e1b4..c3635369 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -5,8 +5,6 @@ #include "SPN/Analysis/SLP/SLPTree.h" -#include - using namespace mlir; using namespace mlir::spn; using namespace mlir::spn::slp; @@ -66,7 +64,7 @@ void SLPTree::buildGraph(std::vector const& operations, node_t const } } for (auto& operandNode : operandsOf.at(currentNode)) { - buildGraph(operandNode->getLastOperations(), currentNode); + buildGraph(operandNode->getLastOperations(), operandNode); } } } From 332e561c4a42e04c4ce62613d01e664c98fdb638 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 7 Mar 2021 16:53:23 +0100 Subject: [PATCH 041/385] Better feature index comparison for vectorizing leaf operations. --- .../Dialect/SPN/Analysis/SLP/SLPTree.h | 22 ++++++++++++++++++- mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 21 +++++++++++++----- 2 files changed, 36 insertions(+), 7 deletions(-) diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h index 7591941c..1a91a39e 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h @@ -38,6 +38,15 @@ namespace mlir { int getLookAheadScore(Operation* last, Operation* candidate, size_t const& maxLevel) const; + static bool areConsecutive(Operation* op1, Operation* op2) { + if (auto leaf1 = dyn_cast(op1)) { + if (auto leaf2 = dyn_cast(op2)) { + return leaf2.getFeatureIndex() == leaf1.getFeatureIndex() + 1; + } + } + return false; + } + /// Checks if the given operations are vectorizable. Operations are vectorizable iff the SPN dialect says they're /// vectorizable and they all share the same opcode. /// \param operations The potentially vectorizable operations. @@ -49,6 +58,13 @@ namespace mlir { return false; } } + if (dyn_cast(operations.front())) { + for (size_t i = 1; i < operations.size(); ++i) { + if (!areConsecutive(operations.at(i - 1), operations.at(i))) { + return false; + } + } + } return true; } @@ -140,7 +156,11 @@ namespace mlir { llvm::errs() << "rankdir = BT;\n"; llvm::errs() << "node[shape=box];\n"; for (auto& op : nodes) { - llvm::errs() << "node_" << op << "[label=\"" << op->getName().getStringRef() << "\\n" << op << "\"];\n"; + llvm::errs() << "node_" << op << "[label=\"" << op->getName().getStringRef() << "\\n" << op; + if (auto leaf = dyn_cast(op)) { + llvm::errs() << "\\narg: " << leaf.getFeatureIndex() << "\\n"; + } + llvm::errs() << "\", fillcolor=\"#a0522d\"];\n"; } for (auto& edge : edges) { llvm::errs() << "node_" << std::get<0>(edge) << " -> node_" << std::get<1>(edge) << "[label=\"" diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index c3635369..87cabc07 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -206,15 +206,24 @@ std::pair SLPTree::getBest(Mode const& mode, int SLPTree::getLookAheadScore(Operation* last, Operation* candidate, size_t const& maxLevel) const { if (maxLevel == 0) { - // No consecutive loads to check, only opcodes. - return last->getName() == candidate->getName() ? 1 : 0; + if (last->getName() != candidate->getName()) { + return 0; + } + if (dyn_cast(last)) { + return areConsecutive(last, candidate) ? 1 : 0; + } + return 1; } auto scoreSum = 0; // TODO: if operands are commutative, sort operands to speed up lookahead? - for (auto const& lastOperand : getOperands(last)) { - for (auto const& candidateOperand : getOperands(candidate)) { - scoreSum += getLookAheadScore(lastOperand, candidateOperand, maxLevel - 1); - + for (auto& lastOperand : getOperands(last)) { + for (auto& candidateOperand : getOperands(candidate)) { + // Can be null if the operand is a block argument. + if (!lastOperand || !candidateOperand) { + scoreSum += getLookAheadScore(last, candidate, 0); + } else { + scoreSum += getLookAheadScore(lastOperand, candidateOperand, maxLevel - 1); + } } } return scoreSum; From 86a631e2daa4fa3c3a894a524c6d6f84d962cbda Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 7 Mar 2021 17:01:04 +0100 Subject: [PATCH 042/385] Fixed Segfault when regarding leaf node operands. --- mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index 87cabc07..8b3588a6 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -69,15 +69,14 @@ void SLPTree::buildGraph(std::vector const& operations, node_t const } } // 2. Non-Commutative - else { - std::vector> operands; + // Only consider operands further when the current operations aren't leaf nodes. + else if (!dyn_cast(operations.front())) { for (auto const& operandOperations : getOperandsTransposed(operations)) { auto const& operandNode = std::make_shared(operandOperations); operandsOf[currentNode].emplace_back(operandNode); buildGraph(operandOperations, operandNode); } } - } std::vector> SLPTree::reorderOperands(node_t const& multinode) { From 6c0e0f4cea696e05c398707fc898b6359115105e Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Tue, 9 Mar 2021 03:29:19 +0100 Subject: [PATCH 043/385] Fixed vectorization pass looping endlessly. --- .../SPNtoStandard/SPNtoStandardPatterns.h | 4 +- mlir/include/Dialect/SPN/SPNOps.td | 22 ++++++++++ .../SPNtoStandard/SPNtoStandardPatterns.cpp | 2 +- .../SPN/Analysis/SLP/VectorPatterns.cpp | 21 ++++++++++ .../Dialect/SPN/Analysis/SLP/VectorPatterns.h | 40 +++++++++++++++++++ mlir/lib/Dialect/SPN/CMakeLists.txt | 1 + .../Dialect/SPN/Passes/SPNVectorization.cpp | 13 +++++- mlir/lib/Dialect/SPN/SPNOps.cpp | 21 ++++++++++ 8 files changed, 119 insertions(+), 5 deletions(-) create mode 100644 mlir/lib/Dialect/SPN/Analysis/SLP/VectorPatterns.cpp create mode 100644 mlir/lib/Dialect/SPN/Analysis/SLP/VectorPatterns.h diff --git a/mlir/include/Conversion/SPNtoStandard/SPNtoStandardPatterns.h b/mlir/include/Conversion/SPNtoStandard/SPNtoStandardPatterns.h index f0d7543a..39dacecf 100644 --- a/mlir/include/Conversion/SPNtoStandard/SPNtoStandardPatterns.h +++ b/mlir/include/Conversion/SPNtoStandard/SPNtoStandardPatterns.h @@ -40,7 +40,7 @@ namespace mlir { /// /// Pattern for lowering SPN Gaussian leaf to actual computation of /// Gaussian distribution in the Standard dialect. - struct GaussionOpLowering : public OpConversionPattern { + struct GaussianOpLowering : public OpConversionPattern { using OpConversionPattern::OpConversionPattern; @@ -96,7 +96,7 @@ namespace mlir { static void populateSPNtoStandardConversionPatterns(OwningRewritePatternList& patterns, MLIRContext* context, TypeConverter& typeConverter) { patterns.insert(context); - patterns.insert(context); + patterns.insert(context); patterns.insert(typeConverter, context); } diff --git a/mlir/include/Dialect/SPN/SPNOps.td b/mlir/include/Dialect/SPN/SPNOps.td index beb2c073..484dcff7 100644 --- a/mlir/include/Dialect/SPN/SPNOps.td +++ b/mlir/include/Dialect/SPN/SPNOps.td @@ -151,6 +151,28 @@ def GaussianOp : SPN_Op<"gaussian", } +/// +/// Vectorized Gaussian distribution as SPN leaf node. +/// +def GaussianVectorOp : SPN_Op<"vgaussian", + [NoSideEffect, DeclareOpInterfaceMethods]> { + + let summary = "Vectorized Gaussian leaf distribution"; + + let description = [{ + A vector of univariate Gaussian leaf distributions. + }]; + + let arguments = (ins AnyFloat:$index, F64ArrayAttr:$means, F64ArrayAttr:$stddevs); + + let results = (outs Variadic); + + let builders = [ + OpBuilderDAG<(ins "Value":$indexVal, "llvm::ArrayRef":$means, "llvm::ArrayRef":$stddevs)> + ]; + +} + /// /// Return operation, returning 0 or 1 probability values. /// diff --git a/mlir/lib/Conversion/SPNtoStandard/SPNtoStandardPatterns.cpp b/mlir/lib/Conversion/SPNtoStandard/SPNtoStandardPatterns.cpp index 76e7a034..63aa4a4f 100644 --- a/mlir/lib/Conversion/SPNtoStandard/SPNtoStandardPatterns.cpp +++ b/mlir/lib/Conversion/SPNtoStandard/SPNtoStandardPatterns.cpp @@ -26,7 +26,7 @@ mlir::LogicalResult mlir::spn::ReturnOpLowering::matchAndRewrite(mlir::spn::Retu return failure(); } -mlir::LogicalResult mlir::spn::GaussionOpLowering::matchAndRewrite(mlir::spn::GaussianOp op, +mlir::LogicalResult mlir::spn::GaussianOpLowering::matchAndRewrite(mlir::spn::GaussianOp op, llvm::ArrayRef operands, mlir::ConversionPatternRewriter& rewriter) const { assert(operands.size() == 1); diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/VectorPatterns.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/VectorPatterns.cpp new file mode 100644 index 00000000..0107770b --- /dev/null +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/VectorPatterns.cpp @@ -0,0 +1,21 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#include +#include "mlir/IR/Module.h" +#include "VectorPatterns.h" +#include "mlir/IR/BlockAndValueMapping.h" + +mlir::LogicalResult mlir::spn::slp::GaussianOpVectorization::matchAndRewrite(mlir::spn::GaussianOp op, + mlir::PatternRewriter& rewriter) const { + if (!op.mean().isExactlyValue(-7.0)) { + rewriter.replaceOpWithNewOp(op, + op.getType(), + op.index(), + llvm::APFloat(-7.0), /*op.mean(),*/ + op.stddev()); + } + return success(); +} \ No newline at end of file diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/VectorPatterns.h b/mlir/lib/Dialect/SPN/Analysis/SLP/VectorPatterns.h new file mode 100644 index 00000000..edd7ddc4 --- /dev/null +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/VectorPatterns.h @@ -0,0 +1,40 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#ifndef SPNC_MLIR_DIALECTS_LIB_DIALECT_SPN_ANALYSIS_SLP_VECTOR_PATTERNS_H +#define SPNC_MLIR_DIALECTS_LIB_DIALECT_SPN_ANALYSIS_SLP_VECTOR_PATTERNS_H + +#include "mlir/Transforms/DialectConversion.h" +#include "mlir/IR/StandardTypes.h" +#include "mlir/Dialect/StandardOps/IR/Ops.h" +#include "SPN/SPNOps.h" +#include "SPN/SPNDialect.h" + +namespace mlir { + namespace spn { + namespace slp { + + /// Pattern for transforming SPN Gaussian ops to a vectorized version. + struct GaussianOpVectorization : public OpRewritePattern { + + using OpRewritePattern::OpRewritePattern; + + LogicalResult matchAndRewrite(GaussianOp op, PatternRewriter& rewriter) const override; + + }; + + /// Populate list with all patterns required to transform SPN operations into vectorized operations. + /// \param patterns Pattern list to fill. + /// \param context MLIR context. + /// \param typeConverter Type converter. + static void populateVectorizationPatterns(OwningRewritePatternList& patterns, MLIRContext* context, + TypeConverter& typeConverter) { + patterns.insert(context); + } + } + } +} + +#endif //SPNC_MLIR_DIALECTS_LIB_DIALECT_SPN_ANALYSIS_SLP_VECTOR_PATTERNS_H diff --git a/mlir/lib/Dialect/SPN/CMakeLists.txt b/mlir/lib/Dialect/SPN/CMakeLists.txt index 464dd953..db4ecb31 100644 --- a/mlir/lib/Dialect/SPN/CMakeLists.txt +++ b/mlir/lib/Dialect/SPN/CMakeLists.txt @@ -9,6 +9,7 @@ add_mlir_dialect_library(MLIRSPN Analysis/SLP/SLPNode.cpp Analysis/SLP/SLPTree.cpp Analysis/SLP/SLPSeeding.cpp + Analysis/SLP/VectorPatterns.cpp Canonicalization/CanonicalizationPatterns.cpp Simplification/SimplificationPatterns.cpp TypeAnalysis/TypePinningPatterns.cpp diff --git a/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp b/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp index 53d702f2..c8636f8d 100644 --- a/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp +++ b/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp @@ -6,9 +6,10 @@ #include "SPN/SPNPasses.h" #include "SPNPassDetails.h" #include +#include +#include "../Analysis/SLP/VectorPatterns.h" #include "SPN/Analysis/SLP/SLPTree.h" -#include "SPN/Analysis/SLP/SLPSeeding.h" -#include "SPN/Analysis/SPNNodeLevel.h" +#include "mlir/Transforms/GreedyPatternRewriteDriver.h" using namespace mlir; using namespace mlir::spn; @@ -50,6 +51,14 @@ namespace { if (!seeds.empty()) { slp::SLPTree graph(seeds.front(), 3); } + + OwningRewritePatternList patterns; + TypeConverter converter; + slp::populateVectorizationPatterns(patterns, &getContext(), converter); + auto op = getOperation(); + FrozenRewritePatternList frozenPatterns(std::move(patterns)); + applyPatternsAndFoldGreedily(op, frozenPatterns); + } }; diff --git a/mlir/lib/Dialect/SPN/SPNOps.cpp b/mlir/lib/Dialect/SPN/SPNOps.cpp index 0fe32a87..78821656 100644 --- a/mlir/lib/Dialect/SPN/SPNOps.cpp +++ b/mlir/lib/Dialect/SPN/SPNOps.cpp @@ -251,6 +251,27 @@ unsigned int mlir::spn::GaussianOp::getFeatureIndex() { assert(false); } +//===----------------------------------------------------------------------===// +// GaussianVectorOp +//===----------------------------------------------------------------------===// + +void mlir::spn::GaussianVectorOp::build(::mlir::OpBuilder& odsBuilder, + ::mlir::OperationState& odsState, + Value indexVal, + llvm::ArrayRef means, + llvm::ArrayRef stddevs) { + build(odsBuilder, odsState, ProbabilityType::get(odsBuilder.getContext()), indexVal, + odsBuilder.getF64ArrayAttr(means), odsBuilder.getF64ArrayAttr(stddevs)); +} + +unsigned int mlir::spn::GaussianVectorOp::getFeatureIndex() { + if (auto blockArg = index().dyn_cast()) { + return blockArg.getArgNumber(); + } + // Expecting the index to be a block argument. + assert(false); +} + //===----------------------------------------------------------------------===// // ReturnOp //===----------------------------------------------------------------------===// From 98d26a9641d45929700cbfefb830ce672f77d2f7 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Wed, 10 Mar 2021 02:32:35 +0100 Subject: [PATCH 044/385] More memory-efficient pattern matching. --- .../Dialect/SPN/Analysis/SLP/SLPNode.h | 5 +- .../Dialect/SPN/Analysis/SLP/SLPTree.h | 2 + mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp | 30 ++++++---- mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 47 +++++++++------ .../SPN/Analysis/SLP/VectorPatterns.cpp | 21 ------- .../Dialect/SPN/Analysis/SLP/VectorPatterns.h | 40 ------------- .../Analysis/SLP/VectorizationPatterns.cpp | 52 ++++++++++++++++ .../SPN/Analysis/SLP/VectorizationPatterns.h | 59 +++++++++++++++++++ mlir/lib/Dialect/SPN/CMakeLists.txt | 2 +- .../Dialect/SPN/Passes/SPNVectorization.cpp | 20 ++++--- mlir/test/CMakeLists.txt | 8 +-- 11 files changed, 179 insertions(+), 107 deletions(-) delete mode 100644 mlir/lib/Dialect/SPN/Analysis/SLP/VectorPatterns.cpp delete mode 100644 mlir/lib/Dialect/SPN/Analysis/SLP/VectorPatterns.h create mode 100644 mlir/lib/Dialect/SPN/Analysis/SLP/VectorizationPatterns.cpp create mode 100644 mlir/lib/Dialect/SPN/Analysis/SLP/VectorizationPatterns.h diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h index c7ebb343..d18aea5f 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPNode.h @@ -21,13 +21,16 @@ namespace mlir { explicit SLPNode(std::vector const& operations); void addOperationToLane(Operation* operation, size_t const& lane); - std::vector getLastOperations() const; Operation* getOperation(size_t lane, size_t index) const; void setOperation(size_t lane, size_t index, Operation* operation); bool isMultiNode() const; bool areRootOfNode(std::vector const& operations) const; + size_t numLanes() const; + size_t numVectors() const; + + std::vector getVector(size_t index) const; friend bool operator==(SLPNode const& lhs, SLPNode const& rhs) { return std::tie(lhs.lanes) diff --git a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h index 1a91a39e..28816400 100644 --- a/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h +++ b/mlir/include/Dialect/SPN/Analysis/SLP/SLPTree.h @@ -26,6 +26,8 @@ namespace mlir { SLPTree(seed_t const& seed, size_t const& maxLookAhead); + std::vector> getNodes() const; + private: void buildGraph(std::vector const& operations, node_t const& currentNode); diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp index 0f6c4648..52494c7a 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPNode.cpp @@ -11,28 +11,20 @@ using namespace mlir::spn::slp; SLPNode::SLPNode(std::vector const& operations) : lanes{operations.size()} { for (size_t i = 0; i < operations.size(); ++i) { - lanes.at(i).emplace_back(operations.at(i)); + lanes[i].emplace_back(operations[i]); } } void SLPNode::addOperationToLane(Operation* operation, size_t const& lane) { - lanes.at(lane).emplace_back(operation); -} - -std::vector SLPNode::getLastOperations() const { - std::vector lastOperations; - for (size_t lane = 0; lane < numLanes(); ++lane) { - lastOperations.emplace_back(lanes.at(lane).back()); - } - return lastOperations; + lanes[lane].emplace_back(operation); } Operation* SLPNode::getOperation(size_t lane, size_t index) const { - return lanes.at(lane).at(index); + return lanes[lane][index]; } void SLPNode::setOperation(size_t lane, size_t index, Operation* operation) { - lanes.at(lane).at(index) = operation; + lanes[lane][index] = operation; } bool SLPNode::isMultiNode() const { @@ -41,7 +33,7 @@ bool SLPNode::isMultiNode() const { bool SLPNode::areRootOfNode(std::vector const& operations) const { for (size_t lane = 0; lane < numLanes(); ++lane) { - if (operations.at(lane) != lanes.at(lane).front()) { + if (operations[lane] != lanes[lane].front()) { return false; } } @@ -51,3 +43,15 @@ bool SLPNode::areRootOfNode(std::vector const& operations) const { size_t SLPNode::numLanes() const { return lanes.size(); } + +size_t SLPNode::numVectors() const { + return lanes.front().size(); +} + +std::vector SLPNode::getVector(size_t index) const { + std::vector vector; + for (size_t lane = 0; lane < numLanes(); ++lane) { + vector.emplace_back(lanes[lane][index]); + } + return vector; +} diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index 8b3588a6..9a5cc3fd 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -12,9 +12,18 @@ using namespace mlir::spn::slp; SLPTree::SLPTree(seed_t const& seed, size_t const& maxLookAhead) : operandsOf{}, maxLookAhead{maxLookAhead} { auto const& currentNode = std::make_shared(seed); operandsOf[currentNode] = {}; + printSubgraph(seed); buildGraph(seed, currentNode); } +std::vector> SLPTree::getNodes() const { + std::vector> nodes; + for (auto const& entry : operandsOf) { + nodes.emplace_back(entry.first); + } + return nodes; +} + void SLPTree::buildGraph(std::vector const& operations, node_t const& currentNode) { llvm::errs() << "Building graph with:\n"; for (auto* op : operations) { @@ -36,17 +45,17 @@ void SLPTree::buildGraph(std::vector const& operations, node_t const } for (size_t i = 0; i < operations.front()->getNumOperands(); ++i) { if (std::all_of(std::begin(allOperands), std::end(allOperands), [&](auto const& operandOperations) { - return operandOperations.at(i)->getName() == currentOpCode && !escapesMultinode(operandOperations.at(i)); + return operandOperations[i]->getName() == currentOpCode && !escapesMultinode(operandOperations[i]); })) { for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { - currentNode->addOperationToLane(allOperands.at(lane).at(i), lane); + currentNode->addOperationToLane(allOperands[lane][i], lane); } - buildGraph(currentNode->getLastOperations(), currentNode); + buildGraph(currentNode->getVector(currentNode->numVectors() - 1), currentNode); } else { // TODO: here might be a good place to implement variable vector width std::vector operandOperations; for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { - operandOperations.emplace_back(allOperands.at(lane).at(i)); + operandOperations.emplace_back(allOperands[lane][i]); } for (auto const& tmp : operandOperations) { tmp->dump(); @@ -60,11 +69,11 @@ void SLPTree::buildGraph(std::vector const& operations, node_t const auto const& order = reorderOperands(currentNode); for (size_t operandIndex = 0; operandIndex < operandsOf.at(currentNode).size(); ++operandIndex) { for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { - operandsOf.at(currentNode).at(operandIndex)->setOperation(lane, 0, order.at(lane).at(operandIndex)); + operandsOf.at(currentNode).at(operandIndex)->setOperation(lane, 0, order[lane][operandIndex]); } } for (auto& operandNode : operandsOf.at(currentNode)) { - buildGraph(operandNode->getLastOperations(), operandNode); + buildGraph(operandNode->getVector(operandNode->numVectors() - 1), operandNode); } } } @@ -86,9 +95,9 @@ std::vector> SLPTree::reorderOperands(node_t const& mult std::vector> mode{multinode->numLanes()}; // 1. Strip first lane for (size_t i = 0; i < numOperands; ++i) { - auto operation = operandsOf.at(multinode).at(i)->getOperation(0, 0); - finalOrder.at(0).emplace_back(operation); - mode.at(0).emplace_back(modeFromOperation(operation)); + auto operation = operandsOf.at(multinode)[i]->getOperation(0, 0); + finalOrder[0].emplace_back(operation); + mode[0].emplace_back(modeFromOperation(operation)); } // 2. For all other lanes, find best candidate @@ -101,30 +110,30 @@ std::vector> SLPTree::reorderOperands(node_t const& mult for (size_t i = 0; i < numOperands; ++i) { // Skip if we can't vectorize // TODO: here might also be a good place to start looking for variable-width - if (mode.at(lane - 1).at(i) == FAILED) { - finalOrder.at(lane).emplace_back(nullptr); - mode.at(lane).emplace_back(FAILED); + if (mode[lane - 1][i] == FAILED) { + finalOrder[lane].emplace_back(nullptr); + mode[lane].emplace_back(FAILED); continue; } - auto* last = finalOrder.at(lane - 1).at(i); - auto const& bestResult = getBest(mode.at(lane - 1).at(i), last, candidates); + auto* last = finalOrder[lane - 1][i]; + auto const& bestResult = getBest(mode[lane - 1][i], last, candidates); // Update output - finalOrder.at(lane).emplace_back(bestResult.first); + finalOrder[lane].emplace_back(bestResult.first); // Detect SPLAT mode if (i == 1 && bestResult.first == last) { - mode.at(lane).at(i) = SPLAT; + mode[lane][i] = SPLAT; } else { - mode.at(lane).emplace_back(bestResult.second); + mode[lane].emplace_back(bestResult.second); } } // Distribute remaining candidates in case we encountered a FAILED. for (auto* candidate : candidates) { for (size_t i = 0; i < numOperands; ++i) { - if (finalOrder.at(lane).at(i) == nullptr) { - finalOrder.at(lane).at(i) = candidate; + if (finalOrder[lane][i] == nullptr) { + finalOrder[lane][i] = candidate; break; } } diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/VectorPatterns.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/VectorPatterns.cpp deleted file mode 100644 index 0107770b..00000000 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/VectorPatterns.cpp +++ /dev/null @@ -1,21 +0,0 @@ -// -// This file is part of the SPNC project. -// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. -// - -#include -#include "mlir/IR/Module.h" -#include "VectorPatterns.h" -#include "mlir/IR/BlockAndValueMapping.h" - -mlir::LogicalResult mlir::spn::slp::GaussianOpVectorization::matchAndRewrite(mlir::spn::GaussianOp op, - mlir::PatternRewriter& rewriter) const { - if (!op.mean().isExactlyValue(-7.0)) { - rewriter.replaceOpWithNewOp(op, - op.getType(), - op.index(), - llvm::APFloat(-7.0), /*op.mean(),*/ - op.stddev()); - } - return success(); -} \ No newline at end of file diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/VectorPatterns.h b/mlir/lib/Dialect/SPN/Analysis/SLP/VectorPatterns.h deleted file mode 100644 index edd7ddc4..00000000 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/VectorPatterns.h +++ /dev/null @@ -1,40 +0,0 @@ -// -// This file is part of the SPNC project. -// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. -// - -#ifndef SPNC_MLIR_DIALECTS_LIB_DIALECT_SPN_ANALYSIS_SLP_VECTOR_PATTERNS_H -#define SPNC_MLIR_DIALECTS_LIB_DIALECT_SPN_ANALYSIS_SLP_VECTOR_PATTERNS_H - -#include "mlir/Transforms/DialectConversion.h" -#include "mlir/IR/StandardTypes.h" -#include "mlir/Dialect/StandardOps/IR/Ops.h" -#include "SPN/SPNOps.h" -#include "SPN/SPNDialect.h" - -namespace mlir { - namespace spn { - namespace slp { - - /// Pattern for transforming SPN Gaussian ops to a vectorized version. - struct GaussianOpVectorization : public OpRewritePattern { - - using OpRewritePattern::OpRewritePattern; - - LogicalResult matchAndRewrite(GaussianOp op, PatternRewriter& rewriter) const override; - - }; - - /// Populate list with all patterns required to transform SPN operations into vectorized operations. - /// \param patterns Pattern list to fill. - /// \param context MLIR context. - /// \param typeConverter Type converter. - static void populateVectorizationPatterns(OwningRewritePatternList& patterns, MLIRContext* context, - TypeConverter& typeConverter) { - patterns.insert(context); - } - } - } -} - -#endif //SPNC_MLIR_DIALECTS_LIB_DIALECT_SPN_ANALYSIS_SLP_VECTOR_PATTERNS_H diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/VectorizationPatterns.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/VectorizationPatterns.cpp new file mode 100644 index 00000000..baa67982 --- /dev/null +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/VectorizationPatterns.cpp @@ -0,0 +1,52 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#include +#include "mlir/IR/Module.h" +#include "VectorizationPatterns.h" +#include "mlir/IR/BlockAndValueMapping.h" +#include "mlir/Dialect/Vector/VectorOps.h" + +mlir::LogicalResult mlir::spn::slp::GaussianOpVectorization::matchAndRewrite(mlir::spn::GaussianOp op, + mlir::PatternRewriter& rewriter) const { + + if (!assignedToVector(op.getOperation())) { + return failure(); + } + + // llvm::ArrayRef means, llvm::ArrayRef stddevs + auto const& vector = vectors[3]; + auto const& n = vector.size(); + Value indices[n]; + double means[n]; + double stddevs[n]; + + for (size_t i = 0; i < n; ++i) { + indices[i] = dyn_cast(vector[i]).index(); + means[i] = dyn_cast(vector[i]).mean().convertToDouble(); + stddevs[i] = dyn_cast(vector[i]).stddev().convertToDouble(); + } + + rewriter.create(op.getLoc(), + op.index(), + llvm::ArrayRef(means, n), + llvm::ArrayRef(stddevs, n)); + + for (auto const& gaussianOp : vector) { + rewriter.eraseOp(gaussianOp); + } + + if (false) { + if (!op.mean().isExactlyValue(-7.0)) { + rewriter.replaceOpWithNewOp(op, + op.getType(), + op.index(), llvm::APFloat(-7.0)/*op.mean()*/, + op.stddev()); + + } + } + + return success(); +} \ No newline at end of file diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/VectorizationPatterns.h b/mlir/lib/Dialect/SPN/Analysis/SLP/VectorizationPatterns.h new file mode 100644 index 00000000..bff0c417 --- /dev/null +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/VectorizationPatterns.h @@ -0,0 +1,59 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#ifndef SPNC_MLIR_DIALECTS_LIB_DIALECT_SPN_ANALYSIS_SLP_VECTOR_PATTERNS_H +#define SPNC_MLIR_DIALECTS_LIB_DIALECT_SPN_ANALYSIS_SLP_VECTOR_PATTERNS_H + +#include + +#include "mlir/Transforms/DialectConversion.h" +#include "mlir/IR/StandardTypes.h" +#include "mlir/Dialect/StandardOps/IR/Ops.h" +#include "SPN/SPNOps.h" +#include "SPN/SPNDialect.h" + +namespace mlir { + namespace spn { + namespace slp { + + template + class VectorizationPattern : public mlir::OpRewritePattern { + public: + VectorizationPattern(MLIRContext* context, std::vector> const& vectors) + : OpRewritePattern(context, 1), vectors{vectors} { + for (size_t i = 0; i < vectors.size(); ++i) { + for (auto* op : vectors[i]) { + vectorIndices[op].emplace_back(i); + } + } + } + protected: + + bool assignedToVector(Operation* op) const { + return vectorIndices.count(op); + } + + std::map> vectorIndices; + std::vector> const& vectors; + }; + + /// Pattern for transforming SPN Gaussian ops to a vectorized version. + struct GaussianOpVectorization : public VectorizationPattern { + + using VectorizationPattern::VectorizationPattern; + + LogicalResult matchAndRewrite(GaussianOp op, PatternRewriter& rewriter) const override; + + }; + + static void populateVectorizationPatterns(OwningRewritePatternList& patterns, MLIRContext* context, + std::vector> const& vectors) { + patterns.insert(context, vectors); + } + } + } +} + +#endif //SPNC_MLIR_DIALECTS_LIB_DIALECT_SPN_ANALYSIS_SLP_VECTOR_PATTERNS_H diff --git a/mlir/lib/Dialect/SPN/CMakeLists.txt b/mlir/lib/Dialect/SPN/CMakeLists.txt index db4ecb31..fcd05075 100644 --- a/mlir/lib/Dialect/SPN/CMakeLists.txt +++ b/mlir/lib/Dialect/SPN/CMakeLists.txt @@ -9,7 +9,7 @@ add_mlir_dialect_library(MLIRSPN Analysis/SLP/SLPNode.cpp Analysis/SLP/SLPTree.cpp Analysis/SLP/SLPSeeding.cpp - Analysis/SLP/VectorPatterns.cpp + Analysis/SLP/VectorizationPatterns.cpp Canonicalization/CanonicalizationPatterns.cpp Simplification/SimplificationPatterns.cpp TypeAnalysis/TypePinningPatterns.cpp diff --git a/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp b/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp index c8636f8d..1f551792 100644 --- a/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp +++ b/mlir/lib/Dialect/SPN/Passes/SPNVectorization.cpp @@ -7,7 +7,7 @@ #include "SPNPassDetails.h" #include #include -#include "../Analysis/SLP/VectorPatterns.h" +#include "../Analysis/SLP/VectorizationPatterns.h" #include "SPN/Analysis/SLP/SLPTree.h" #include "mlir/Transforms/GreedyPatternRewriteDriver.h" @@ -50,15 +50,19 @@ namespace { if (!seeds.empty()) { slp::SLPTree graph(seeds.front(), 3); + OwningRewritePatternList patterns; + std::vector> vectors; + for (auto const& node_ptr : graph.getNodes()) { + for (size_t i = 0; i < node_ptr->numVectors(); ++i) { + vectors.emplace_back(node_ptr->getVector(i)); + } + } + slp::populateVectorizationPatterns(patterns, &getContext(), vectors); + auto op = getOperation(); + FrozenRewritePatternList frozenPatterns(std::move(patterns)); + applyPatternsAndFoldGreedily(op, frozenPatterns); } - OwningRewritePatternList patterns; - TypeConverter converter; - slp::populateVectorizationPatterns(patterns, &getContext(), converter); - auto op = getOperation(); - FrozenRewritePatternList frozenPatterns(std::move(patterns)); - applyPatternsAndFoldGreedily(op, frozenPatterns); - } }; diff --git a/mlir/test/CMakeLists.txt b/mlir/test/CMakeLists.txt index a52434e3..4e6a3f16 100644 --- a/mlir/test/CMakeLists.txt +++ b/mlir/test/CMakeLists.txt @@ -13,10 +13,10 @@ configure_lit_site_cfg( set(CHECK_SPNC_LIT_ARGS "") # Add verbose flag if requested -if(${CHECK_SPNC_VERBOSE}) - message(STATUS "Running llvm-lit in verbose mode") - set(CHECK_SPNC_LIT_ARGS "${CHECK_SPNC_LIT_ARGS};-v") -endif() +if (${CHECK_SPNC_VERBOSE}) + message(STATUS "Running llvm-lit in verbose mode") + set(CHECK_SPNC_LIT_ARGS "${CHECK_SPNC_LIT_ARGS};-v") +endif () # Run regression tests add_lit_testsuite(check-spnc-mlir-tests "Running spnc-mlir regression tests" From 901e223ca2175a11da13b432ce95a707f7c10dee Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Tue, 16 Mar 2021 23:33:45 +0100 Subject: [PATCH 045/385] Merge preparations. --- mlir/include/Dialect/SPN/SPNDialect.td | 1 + mlir/include/Dialect/SPN/SPNOps.td | 10 +- mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp | 5 + .../Analysis/SLP/VectorizationPatterns.cpp | 127 +++++++++++++++--- .../SPN/Analysis/SLP/VectorizationPatterns.h | 18 ++- mlir/lib/Dialect/SPN/SPNOps.cpp | 10 +- 6 files changed, 142 insertions(+), 29 deletions(-) diff --git a/mlir/include/Dialect/SPN/SPNDialect.td b/mlir/include/Dialect/SPN/SPNDialect.td index eaebe8cd..706818b1 100644 --- a/mlir/include/Dialect/SPN/SPNDialect.td +++ b/mlir/include/Dialect/SPN/SPNDialect.td @@ -37,4 +37,5 @@ def SPNProbabilityType : DialectType; + #endif // SPN_DIALECT \ No newline at end of file diff --git a/mlir/include/Dialect/SPN/SPNOps.td b/mlir/include/Dialect/SPN/SPNOps.td index 484dcff7..2c185d26 100644 --- a/mlir/include/Dialect/SPN/SPNOps.td +++ b/mlir/include/Dialect/SPN/SPNOps.td @@ -163,13 +163,13 @@ def GaussianVectorOp : SPN_Op<"vgaussian", A vector of univariate Gaussian leaf distributions. }]; - let arguments = (ins AnyFloat:$index, F64ArrayAttr:$means, F64ArrayAttr:$stddevs); + let arguments = (ins VectorOf<[AnyFloat]>:$indices, F64ArrayAttr:$means, F64ArrayAttr:$stddevs); - let results = (outs Variadic); + let results = (outs VectorOf<[AnyProbabilityType]>); - let builders = [ - OpBuilderDAG<(ins "Value":$indexVal, "llvm::ArrayRef":$means, "llvm::ArrayRef":$stddevs)> - ]; +/// let builders = [ +/// OpBuilderDAG<(ins "llvm::ArrayRef":$indices, "llvm::ArrayRef":$means, "llvm::ArrayRef":$stddevs)> +/// ]; } diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp index 9a5cc3fd..7d77e499 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/SLPTree.cpp @@ -20,6 +20,11 @@ std::vector> SLPTree::getNodes() const { std::vector> nodes; for (auto const& entry : operandsOf) { nodes.emplace_back(entry.first); + for (auto const& operand : entry.second) { + if (std::find(std::begin(nodes), std::end(nodes), operand) == std::end(nodes)) { + nodes.emplace_back(operand); + } + } } return nodes; } diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/VectorizationPatterns.cpp b/mlir/lib/Dialect/SPN/Analysis/SLP/VectorizationPatterns.cpp index baa67982..d3542739 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/VectorizationPatterns.cpp +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/VectorizationPatterns.cpp @@ -8,34 +8,53 @@ #include "VectorizationPatterns.h" #include "mlir/IR/BlockAndValueMapping.h" #include "mlir/Dialect/Vector/VectorOps.h" +#include "mlir/Dialect/Vector/VectorTransforms.h" mlir::LogicalResult mlir::spn::slp::GaussianOpVectorization::matchAndRewrite(mlir::spn::GaussianOp op, mlir::PatternRewriter& rewriter) const { - if (!assignedToVector(op.getOperation())) { + return failure(); + + if (!isAssignedToVector(op.getOperation())) { return failure(); } - // llvm::ArrayRef means, llvm::ArrayRef stddevs - auto const& vector = vectors[3]; - auto const& n = vector.size(); - Value indices[n]; - double means[n]; - double stddevs[n]; + auto a = op.getOperation(); - for (size_t i = 0; i < n; ++i) { - indices[i] = dyn_cast(vector[i]).index(); - means[i] = dyn_cast(vector[i]).mean().convertToDouble(); - stddevs[i] = dyn_cast(vector[i]).stddev().convertToDouble(); - } + for (auto const& index : vectorIndices.at(op.getOperation())) { + auto const& vector = vectors[index]; + + if (!isVectorMixed(vector)) { + auto const& n = vector.size(); + Value values[n]; + Value indices[n]; + double means[n]; + double stddevs[n]; - rewriter.create(op.getLoc(), - op.index(), - llvm::ArrayRef(means, n), - llvm::ArrayRef(stddevs, n)); + for (size_t i = 0; i < n; ++i) { + indices[i] = dyn_cast(vector[i]).index(); + means[i] = dyn_cast(vector[i]).mean().convertToDouble(); + stddevs[i] = dyn_cast(vector[i]).stddev().convertToDouble(); + indices[i].dump(); + values[i] = dyn_cast(vector[i]).getOperand(); + } +/* + auto t = rewriter.create(op.getLoc(), + op.index(), + llvm::ArrayRef(means, n), + llvm::ArrayRef(stddevs, n)); + ValueRange valueRange{}; + rewriter.replaceOp(t.getOperation(), {}); + t.getOperation()->dump(); + auto x = dyn_cast(t.getOperation()).getFeatureIndex(); - for (auto const& gaussianOp : vector) { - rewriter.eraseOp(gaussianOp); + x = x - 1;*/ + } else { + + } + for (auto const& gaussianOp : vector) { + rewriter.eraseOp(gaussianOp); + } } if (false) { @@ -49,4 +68,76 @@ mlir::LogicalResult mlir::spn::slp::GaussianOpVectorization::matchAndRewrite(mli } return success(); +} + +mlir::LogicalResult mlir::spn::slp::SumOpVectorization::matchAndRewrite(mlir::spn::SumOp op, + mlir::PatternRewriter& rewriter) const { + + if (!isAssignedToVector(op.getOperation())) { + return failure(); + } + + auto a = op.getOperation(); + + for (auto const& index : vectorIndices.at(op.getOperation())) { + auto const& vector = vectors[index]; + Type vectorType = VectorType::get(vector.size(), op.getType()); + Type memrefType = MemRefType::get(vector.size(), op.getType()); + ValueRange range; + rewriter.create(op.getLoc(), memrefType, range); + op.getOperation()->getBlock()->dump(); + } + /* + if (isVectorMixed(vector)) { + OpBuilder builder(op.getContext()); + builder.setInsertionPoint(op); + llvm::SmallVector values; + values.reserve(vector.size()); + for (size_t i = 0; i < vector.size(); ++i) { + values[i] = vector[i]->getResult(0); + } + //DenseElementsAttr::get(vectorType, llvm::ArrayRef{values}); + } + + for (size_t i = 0; i < n; ++i) { + indices[i] = dyn_cast(vector[i]).index(); + means[i] = dyn_cast(vector[i]).mean().convertToDouble(); + stddevs[i] = dyn_cast(vector[i]).stddev().convertToDouble(); + indices[i].dump(); + values[i] = dyn_cast(vector[i]).getOperand(); + } + + auto t = rewriter.create(op.getLoc(), + op.index(), + llvm::ArrayRef(means, n), + llvm::ArrayRef(stddevs, n)); + ValueRange valueRange{}; + rewriter.replaceOp(t.getOperation(), {}); + t.getOperation()->dump(); + for (size_t i = 0; i < vector.size(); ++i) { + values[i] = vector[i]->getResult(0); + } + //DenseElementsAttr::get(vectorType, llvm::ArrayRef{values}); + } + + for (size_t i = 0; i < n; ++i) { + indices[i] = dyn_cast(vector[i]).index(); + means[i] = dyn_cast(vector[i]).mean().convertToDouble(); + stddevs[i] = dyn_cast(vector[i]).stddev().convertToDouble(); + indices[i].dump(); + values[i] = dyn_cast(vector[i]).getOperand(); + } + + x = x - 1; +} else { + +} +for (auto const& gaussianOp : vector) { + rewriter.eraseOp(gaussianOp); +} +} + +} + */ + return success(); } \ No newline at end of file diff --git a/mlir/lib/Dialect/SPN/Analysis/SLP/VectorizationPatterns.h b/mlir/lib/Dialect/SPN/Analysis/SLP/VectorizationPatterns.h index bff0c417..76b59286 100644 --- a/mlir/lib/Dialect/SPN/Analysis/SLP/VectorizationPatterns.h +++ b/mlir/lib/Dialect/SPN/Analysis/SLP/VectorizationPatterns.h @@ -31,10 +31,16 @@ namespace mlir { } protected: - bool assignedToVector(Operation* op) const { + bool isAssignedToVector(Operation* op) const { return vectorIndices.count(op); } + bool isVectorMixed(std::vector vector) const { + return std::any_of(std::begin(vector), std::end(vector), [&](Operation* op) { + return op->getName() != vector.front()->getName(); + }); + } + std::map> vectorIndices; std::vector> const& vectors; }; @@ -48,9 +54,19 @@ namespace mlir { }; + /// Pattern for transforming SPN sum ops to a vectorized version. + struct SumOpVectorization : public VectorizationPattern { + + using VectorizationPattern::VectorizationPattern; + + LogicalResult matchAndRewrite(SumOp op, PatternRewriter& rewriter) const override; + + }; + static void populateVectorizationPatterns(OwningRewritePatternList& patterns, MLIRContext* context, std::vector> const& vectors) { patterns.insert(context, vectors); + patterns.insert(context, vectors); } } } diff --git a/mlir/lib/Dialect/SPN/SPNOps.cpp b/mlir/lib/Dialect/SPN/SPNOps.cpp index 78821656..ff874f8d 100644 --- a/mlir/lib/Dialect/SPN/SPNOps.cpp +++ b/mlir/lib/Dialect/SPN/SPNOps.cpp @@ -254,18 +254,18 @@ unsigned int mlir::spn::GaussianOp::getFeatureIndex() { //===----------------------------------------------------------------------===// // GaussianVectorOp //===----------------------------------------------------------------------===// - +/* void mlir::spn::GaussianVectorOp::build(::mlir::OpBuilder& odsBuilder, ::mlir::OperationState& odsState, - Value indexVal, + llvm::ArrayRef indices, llvm::ArrayRef means, llvm::ArrayRef stddevs) { - build(odsBuilder, odsState, ProbabilityType::get(odsBuilder.getContext()), indexVal, + build(odsBuilder, odsState, ProbabilityType::get(odsBuilder.getContext()), VectorType::get(indices, indices.front().getType()), odsBuilder.getF64ArrayAttr(means), odsBuilder.getF64ArrayAttr(stddevs)); } - +*/ unsigned int mlir::spn::GaussianVectorOp::getFeatureIndex() { - if (auto blockArg = index().dyn_cast()) { + if (auto blockArg = indices().dyn_cast()) { return blockArg.getArgNumber(); } // Expecting the index to be a block argument. From 828c27e377f3861f984e4fe03b9e6801df423362 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Wed, 17 Mar 2021 17:37:04 +0100 Subject: [PATCH 046/385] Fixed some dependencies. --- .../HiSPNtoLoSPNConversionPasses.h | 12 ++-- .../LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h | 5 +- .../LoSPNtoCPU/Vectorization/SLP/SLPTree.h | 28 ++++---- .../SLP/SLPVectorizationPatterns.h | 66 ++++++++++--------- mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt | 10 +-- .../LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp | 8 +-- .../Vectorization/SLP/SLPSeeding.cpp | 12 ++-- .../LoSPNtoCPU/Vectorization/SLP/SLPTree.cpp | 11 +++- .../SLP/SLPVectorizationPatterns.cpp | 4 +- 9 files changed, 80 insertions(+), 76 deletions(-) diff --git a/mlir/include/Conversion/HiSPNtoLoSPN/HiSPNtoLoSPNConversionPasses.h b/mlir/include/Conversion/HiSPNtoLoSPN/HiSPNtoLoSPNConversionPasses.h index 8025fbd1..9b51faf1 100644 --- a/mlir/include/Conversion/HiSPNtoLoSPN/HiSPNtoLoSPNConversionPasses.h +++ b/mlir/include/Conversion/HiSPNtoLoSPN/HiSPNtoLoSPNConversionPasses.h @@ -11,38 +11,34 @@ namespace mlir { namespace spn { - struct HiSPNtoLoSPNNodeConversionPass : - public PassWrapper> { + struct HiSPNtoLoSPNNodeConversionPass : public PassWrapper> { public: explicit HiSPNtoLoSPNNodeConversionPass(bool useLogSpaceComputation) : computeLogSpace{useLogSpaceComputation} {} protected: - void runOnOperation() override; private: - bool computeLogSpace; }; std::unique_ptr createHiSPNtoLoSPNNodeConversionPass(bool useLogSpaceComputation); - struct HiSPNtoLoSPNQueryConversionPass : - public PassWrapper> { + struct HiSPNtoLoSPNQueryConversionPass : public PassWrapper> { public: explicit HiSPNtoLoSPNQueryConversionPass(bool useLogSpaceComputation) : computeLogSpace{useLogSpaceComputation} {} protected: - void runOnOperation() override; private: - bool computeLogSpace; }; diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h index b802f607..a72a3105 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h @@ -6,8 +6,7 @@ #ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPSEEDING_H #define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPSEEDING_H -#include "SPN/Analysis/SPNNodeLevel.h" -#include "SPN/SPNOps.h" +#include "LoSPN/LoSPNOps.h" namespace mlir { namespace spn { @@ -31,7 +30,7 @@ namespace mlir { explicit SeedAnalysis(Operation* jointQuery); std::vector getSeeds(size_t const& op, - SPNNodeLevel const& nodeLevels, + std::map const& nodeLevels, SearchMode const& mode = RootToLeaf) const; private: diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPTree.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPTree.h index 5314f37b..38c7899c 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPTree.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPTree.h @@ -7,7 +7,7 @@ #define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPTREE_H #include "mlir/IR/Operation.h" -#include "SPN/SPNOpTraits.h" +#include "LoSPN/LoSPNTraits.h" #include "SLPMode.h" #include "SLPNode.h" #include "SLPSeeding.h" @@ -41,11 +41,7 @@ namespace mlir { int getLookAheadScore(Operation* last, Operation* candidate, size_t const& maxLevel) const; static bool areConsecutive(Operation* op1, Operation* op2) { - if (auto leaf1 = dyn_cast(op1)) { - if (auto leaf2 = dyn_cast(op2)) { - return leaf2.getFeatureIndex() == leaf1.getFeatureIndex() + 1; - } - } + assert(false); return false; } @@ -55,11 +51,12 @@ namespace mlir { /// \return True if the operations can be vectorized, otherwise false. static bool vectorizable(std::vector const& operations) { for (size_t i = 0; i < operations.size(); ++i) { - if (!operations.at(i)->hasTrait() + if (!operations.at(i)->hasTrait() || (i > 0 && operations.at(i)->getName() != operations.front()->getName())) { return false; } } + /* if (dyn_cast(operations.front())) { for (size_t i = 1; i < operations.size(); ++i) { if (!areConsecutive(operations.at(i - 1), operations.at(i))) { @@ -67,6 +64,8 @@ namespace mlir { } } } + */ + assert(false); return true; } @@ -129,7 +128,7 @@ namespace mlir { /// For debugging purposes. static void printSubgraph(std::vector const& operations) { - std::set nodes; + std::vector nodes; std::vector> edges; std::stack worklist; @@ -142,8 +141,8 @@ namespace mlir { auto op = worklist.top(); worklist.pop(); - if (nodes.find(op) == nodes.end()) { - nodes.insert(op); + if (std::find(std::begin(nodes), std::end(nodes), op) == nodes.end()) { + nodes.emplace_back(op); for (size_t i = 0; i < op->getNumOperands(); ++i) { auto const& operand = op->getOperand(i); if (operand.getDefiningOp() != nullptr) { @@ -159,9 +158,12 @@ namespace mlir { llvm::errs() << "node[shape=box];\n"; for (auto& op : nodes) { llvm::errs() << "node_" << op << "[label=\"" << op->getName().getStringRef() << "\\n" << op; - if (auto leaf = dyn_cast(op)) { - llvm::errs() << "\\narg: " << leaf.getFeatureIndex() << "\\n"; - } + assert(false); + /* + if (auto leaf = dyn_cast(op)) { + llvm::errs() << "\\narg: " << leaf.getFeatureIndex() << "\\n"; + } + */ llvm::errs() << "\", fillcolor=\"#a0522d\"];\n"; } for (auto& edge : edges) { diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h index 5663dd34..dc439242 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h @@ -10,52 +10,54 @@ #include "mlir/Transforms/DialectConversion.h" #include "mlir/Dialect/StandardOps/IR/Ops.h" -#include "SPN/SPNOps.h" -#include "SPN/SPNDialect.h" +#include "LoSPN/LoSPNOps.h" +#include "LoSPN/LoSPNDialect.h" namespace mlir { namespace spn { - namespace slp { + namespace low { + namespace slp { - template - class VectorizationPattern : public mlir::OpRewritePattern { - public: - VectorizationPattern(MLIRContext* context, std::vector> const& vectors) - : OpRewritePattern(context, 1), vectors{vectors} { - for (size_t i = 0; i < vectors.size(); ++i) { - for (auto* op : vectors[i]) { - vectorIndices[op].emplace_back(i); + template + class VectorizationPattern : public mlir::OpRewritePattern { + public: + VectorizationPattern(MLIRContext* context, std::vector> const& vectors) + : OpRewritePattern(context, 1), vectors{vectors} { + for (size_t i = 0; i < vectors.size(); ++i) { + for (auto* op : vectors[i]) { + vectorIndices[op].emplace_back(i); + } } } - } - protected: + protected: - bool isAssignedToVector(Operation* op) const { - return vectorIndices.count(op); - } + bool isAssignedToVector(Operation* op) const { + return vectorIndices.count(op); + } - bool isVectorMixed(std::vector vector) const { - return std::any_of(std::begin(vector), std::end(vector), [&](Operation* op) { - return op->getName() != vector.front()->getName(); - }); - } + bool isVectorMixed(std::vector vector) const { + return std::any_of(std::begin(vector), std::end(vector), [&](Operation* op) { + return op->getName() != vector.front()->getName(); + }); + } - std::map> vectorIndices; - std::vector> const& vectors; - }; + std::map> vectorIndices; + std::vector> const& vectors; + }; - /// Pattern for transforming SPN sum ops to a vectorized version. - struct SumOpVectorization : public VectorizationPattern { + /// Pattern for transforming SPN sum ops to a vectorized version. + struct SumOpVectorization : public VectorizationPattern { - using VectorizationPattern::VectorizationPattern; + using VectorizationPattern::VectorizationPattern; - LogicalResult matchAndRewrite(SumOp op, PatternRewriter& rewriter) const override; + LogicalResult matchAndRewrite(SPNAdd op, PatternRewriter& rewriter) const override; - }; + }; - static void populateVectorizationPatterns(OwningRewritePatternList& patterns, MLIRContext* context, - std::vector> const& vectors) { - patterns.insert(context, vectors); + static void populateVectorizationPatterns(OwningRewritePatternList& patterns, MLIRContext* context, + std::vector> const& vectors) { + patterns.insert(context, vectors); + } } } } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt b/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt index c39f41ab..9f792047 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt +++ b/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt @@ -5,11 +5,11 @@ add_mlir_conversion_library(MLIRLoSPNtoCPUTransforms Vectorization/VectorizeNodePatterns.cpp Vectorization/TargetInformation.cpp Vectorization/ReplaceGatherWithShuffle.cpp - #Vectorization/SLP/SLPMode.cpp - #Vectorization/SLP/SLPNode.cpp - #Vectorization/SLP/SLPSeeding.cpp - #Vectorization/SLP/SLPTree.cpp - #Vectorization/SLP/SLPVectorizationPatterns.cpp + Vectorization/SLP/SLPMode.cpp + Vectorization/SLP/SLPNode.cpp + Vectorization/SLP/SLPSeeding.cpp + Vectorization/SLP/SLPTree.cpp + Vectorization/SLP/SLPVectorizationPatterns.cpp LoSPNtoCPUConversionPasses.cpp ADDITIONAL_HEADER_DIRS diff --git a/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp b/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp index df73b523..1d0c941d 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp @@ -15,7 +15,6 @@ #include "mlir/Dialect/Vector/VectorOps.h" #include "mlir/Dialect/Math/IR/Math.h" #include "mlir/Dialect/Linalg/IR/LinalgOps.h" -#include "SPN/Analysis/SPNNodeLevel.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h" void mlir::spn::LoSPNtoCPUStructureConversionPass::runOnOperation() { @@ -35,6 +34,7 @@ void mlir::spn::LoSPNtoCPUStructureConversionPass::runOnOperation() { OwningRewritePatternList patterns; if (vectorize) { + // auto& depthAnalysis = getAnalysis(); // Try to vectorize tasks if vectorization was requested. mlir::spn::populateLoSPNCPUVectorizationStructurePatterns(patterns, &getContext(), typeConverter); } @@ -56,13 +56,13 @@ void mlir::spn::LoSPNtoCPUStructureConversionPass::runOnOperation() { auto const& uses = std::distance(op->getUses().begin(), op->getUses().end()); if (uses > 1) { // TODO: how to handle such cases? and is special handling required at all? - assert(false && "SPN is not a tree!"); + //assert(false && "SPN is not a tree!"); } }); // ==================================== // - //auto& depthAnalysis = getAnalysis(); - //auto& seedAnalysis = getAnalysis(); + // auto& depthAnalysis = getAnalysis(); + auto& seedAnalysis = getAnalysis(); /*auto seeds = seedAnalysis.getSeeds(4, depthAnalysis); if (!seeds.empty()) { diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp index c046c3da..79d7c0ed 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp @@ -13,19 +13,19 @@ using namespace mlir::spn::slp; SeedAnalysis::SeedAnalysis(Operation* jointQuery) : jointQuery{jointQuery} {} std::vector SeedAnalysis::getSeeds(size_t const& width, - SPNNodeLevel const& nodeLevels, + std::map const& nodeLevels, SearchMode const& mode) const { llvm::StringMap> seedsByOpName; jointQuery->walk([&](Operation* op) { - auto depth = nodeLevels.getOperationDepth(op); - if (!op->hasTrait() || depth < log2(width)) { + auto depth = nodeLevels.at(op); + if (!op->hasTrait() || depth < log2(width)) { return; } bool needsNewSeed = true; for (auto& seed : seedsByOpName[op->getName().getStringRef()]) { - if (seed.size() < width && nodeLevels.getOperationDepth(seed.front()) == depth) { + if (seed.size() < width && nodeLevels.at(seed.front()) == depth) { seed.emplace_back(op); needsNewSeed = false; break; @@ -51,9 +51,9 @@ std::vector SeedAnalysis::getSeeds(size_t const& width, // or those closest to the leaves (LeafToRoot). std::sort(seeds.begin(), seeds.end(), [&](seed_t const& seed1, seed_t const& seed2) { if (mode == RootToLeaf) { - return nodeLevels.getOperationDepth(seed1.front()) < nodeLevels.getOperationDepth(seed2.front()); + return nodeLevels.at(seed1.front()) < nodeLevels.at(seed2.front()); } else if (mode == LeafToRoot) { - return nodeLevels.getOperationDepth(seed1.front()) > nodeLevels.getOperationDepth(seed2.front()); + return nodeLevels.at(seed1.front()) > nodeLevels.at(seed2.front()); } else { // Unused so far. assert(false); diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPTree.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPTree.cpp index a265debc..bf54345f 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPTree.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPTree.cpp @@ -4,6 +4,7 @@ // #include "LoSPNtoCPU/Vectorization/SLP/SLPTree.h" +#include using namespace mlir; using namespace mlir::spn; @@ -84,6 +85,8 @@ void SLPTree::buildGraph(std::vector const& operations, node_t const } // 2. Non-Commutative // Only consider operands further when the current operations aren't leaf nodes. + assert(false); + /* else if (!dyn_cast(operations.front())) { for (auto const& operandOperations : getOperandsTransposed(operations)) { auto const& operandNode = std::make_shared(operandOperations); @@ -91,6 +94,7 @@ void SLPTree::buildGraph(std::vector const& operations, node_t const buildGraph(operandOperations, operandNode); } } + */ } std::vector> SLPTree::reorderOperands(node_t const& multinode) { @@ -222,9 +226,10 @@ int SLPTree::getLookAheadScore(Operation* last, Operation* candidate, size_t con if (last->getName() != candidate->getName()) { return 0; } - if (dyn_cast(last)) { - return areConsecutive(last, candidate) ? 1 : 0; - } + assert(false); + /* if (dyn_cast(last)) { + return areConsecutive(last, candidate) ? 1 : 0; + }*/ return 1; } auto scoreSum = 0; diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp index 2cfc38f8..278e5a3d 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp @@ -8,8 +8,8 @@ #include "mlir/Dialect/Vector/VectorOps.h" #include "mlir/Dialect/Vector/VectorTransforms.h" -mlir::LogicalResult mlir::spn::slp::SumOpVectorization::matchAndRewrite(mlir::spn::SumOp op, - mlir::PatternRewriter& rewriter) const { +mlir::LogicalResult mlir::spn::low::slp::SumOpVectorization::matchAndRewrite(mlir::spn::low::SPNAdd op, + mlir::PatternRewriter& rewriter) const { if (!isAssignedToVector(op.getOperation())) { return failure(); From 9bb66e84888938eeb469ee5f81c800df907b6a4d Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Thu, 18 Mar 2021 17:46:40 +0100 Subject: [PATCH 047/385] Adapted seed analysis to LoSPN Dialect. --- .../LoSPNtoCPU/LoSPNtoCPUConversionPasses.h | 40 +++++------ .../LoSPNtoCPU/Vectorization/SLP/SLPNode.h | 3 +- .../LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h | 8 ++- .../Vectorization/SLP/SLPVectorizationPass.h | 31 +++++++++ .../Vectorization/TargetInformation.h | 0 .../Vectorization/VectorOptimizationPasses.h | 11 ++-- .../Vectorization/VectorizationPatterns.h | 28 ++++---- mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt | 1 + .../LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp | 41 ------------ .../Vectorization/SLP/SLPSeeding.cpp | 35 +++++++--- .../LoSPNtoCPU/Vectorization/SLP/SLPTree.cpp | 28 ++++---- .../SLP/SLPVectorizationPass.cpp | 66 +++++++++++++++++++ .../Vectorization/TargetInformation.cpp | 2 +- .../Vectorization/VectorizeNodePatterns.cpp | 2 +- .../VectorizeStructurePatterns.cpp | 2 +- mlir/tools/spnc-opt/spnc-opt.cpp | 6 ++ 16 files changed, 190 insertions(+), 114 deletions(-) create mode 100644 mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h rename mlir/{lib => include}/Conversion/LoSPNtoCPU/Vectorization/TargetInformation.h (100%) create mode 100644 mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp diff --git a/mlir/include/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.h b/mlir/include/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.h index 3406b9c8..886dbb1c 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.h +++ b/mlir/include/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.h @@ -11,47 +11,43 @@ namespace mlir { namespace spn { - struct LoSPNtoCPUStructureConversionPass : - public PassWrapper> { + struct LoSPNtoCPUStructureConversionPass : public PassWrapper> { public: - explicit LoSPNtoCPUStructureConversionPass(bool enableVectorization) : vectorize{enableVectorization} {} + explicit LoSPNtoCPUStructureConversionPass(bool enableVectorization) : vectorize{enableVectorization} {} protected: - void runOnOperation() - override; + void runOnOperation() override; private: - bool vectorize; + bool vectorize; - }; + }; - std::unique_ptr createLoSPNtoCPUStructureConversionPass(bool enableVectorization); + std::unique_ptr createLoSPNtoCPUStructureConversionPass(bool enableVectorization); - struct LoSPNtoCPUNodeConversionPass : - public PassWrapper> { + struct LoSPNtoCPUNodeConversionPass : public PassWrapper> { - protected: - void runOnOperation() - override; + protected: + void runOnOperation() override; -}; + }; -std::unique_ptr createLoSPNtoCPUNodeConversionPass(); + std::unique_ptr createLoSPNtoCPUNodeConversionPass(); -struct LoSPNNodeVectorizationPass : public PassWrapper> { + struct LoSPNNodeVectorizationPass : public PassWrapper> { -protected: -void runOnOperation() -override; + protected: + void runOnOperation() override; -}; + }; -std::unique_ptr createLoSPNNodeVectorizationPass(); + std::unique_ptr createLoSPNNodeVectorizationPass(); -} + } } #endif //SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_LOSPNTOCPUCONVERSIONPASSES_H diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h index 7a925769..f93396d6 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h @@ -33,8 +33,7 @@ namespace mlir { std::vector getVector(size_t index) const; friend bool operator==(SLPNode const& lhs, SLPNode const& rhs) { - return std::tie(lhs.lanes) - == std::tie(rhs.lanes); + return std::tie(lhs.lanes) == std::tie(rhs.lanes); } friend bool operator!=(SLPNode const& lhs, SLPNode const& rhs) { diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h index a72a3105..37de9fb3 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h @@ -27,15 +27,17 @@ namespace mlir { public: - explicit SeedAnalysis(Operation* jointQuery); + explicit SeedAnalysis(Operation* rootOp); + + std::map getOpDepths() const; std::vector getSeeds(size_t const& op, - std::map const& nodeLevels, + std::map const& depthsOf, SearchMode const& mode = RootToLeaf) const; private: - Operation* jointQuery; + Operation* rootOp; }; } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h new file mode 100644 index 00000000..834e1401 --- /dev/null +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h @@ -0,0 +1,31 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPVECTORIZATIONPASS_H +#define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPVECTORIZATIONPASS_H + +#include "mlir/Pass/Pass.h" + +namespace mlir { + namespace spn { + + struct SLPVectorizationPass : public PassWrapper> { + + public: + + explicit SLPVectorizationPass() = default; + + protected: + void runOnOperation() override; + + private: + + }; + + std::unique_ptr createSLPVectorizationPass(); + } +} + +#endif //SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPVECTORIZATIONPASS_H diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/TargetInformation.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/TargetInformation.h similarity index 100% rename from mlir/lib/Conversion/LoSPNtoCPU/Vectorization/TargetInformation.h rename to mlir/include/Conversion/LoSPNtoCPU/Vectorization/TargetInformation.h diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/VectorOptimizationPasses.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/VectorOptimizationPasses.h index a7fecad3..22380b2b 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/VectorOptimizationPasses.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/VectorOptimizationPasses.h @@ -11,16 +11,15 @@ namespace mlir { namespace spn { - struct ReplaceGatherWithShufflePass : public PassWrapper> { + struct ReplaceGatherWithShufflePass : public PassWrapper> { protected: - void runOnOperation() - override; + void runOnOperation() override; - }; + }; - std::unique_ptr createReplaceGatherWithShufflePass(); + std::unique_ptr createReplaceGatherWithShufflePass(); -} + } } #endif //SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_VECTOROPTIMIZATIONPASSES_H diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/VectorizationPatterns.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/VectorizationPatterns.h index cf35c705..57f2c49c 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/VectorizationPatterns.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/VectorizationPatterns.h @@ -19,7 +19,7 @@ namespace mlir { using OpConversionPattern::OpConversionPattern; LogicalResult matchAndRewrite(low::SPNTask op, - ArrayRef operands, + ArrayRef operands, ConversionPatternRewriter& rewriter) const override; }; @@ -34,7 +34,7 @@ namespace mlir { using OpConversionPattern::OpConversionPattern; LogicalResult matchAndRewrite(low::SPNBatchRead op, - ArrayRef operands, + ArrayRef operands, ConversionPatternRewriter& rewriter) const override; }; @@ -43,7 +43,7 @@ namespace mlir { using OpConversionPattern::OpConversionPattern; LogicalResult matchAndRewrite(low::SPNBatchWrite op, - ArrayRef operands, + ArrayRef operands, ConversionPatternRewriter& rewriter) const override; }; @@ -52,7 +52,7 @@ namespace mlir { using OpConversionPattern::OpConversionPattern; LogicalResult matchAndRewrite(low::SPNMul op, - ArrayRef operands, + ArrayRef operands, ConversionPatternRewriter& rewriter) const override; }; @@ -61,7 +61,7 @@ namespace mlir { using OpConversionPattern::OpConversionPattern; LogicalResult matchAndRewrite(low::SPNMul op, - ArrayRef operands, + ArrayRef operands, ConversionPatternRewriter& rewriter) const override; }; @@ -70,7 +70,7 @@ namespace mlir { using OpConversionPattern::OpConversionPattern; LogicalResult matchAndRewrite(low::SPNAdd op, - ArrayRef operands, + ArrayRef operands, ConversionPatternRewriter& rewriter) const override; }; @@ -79,7 +79,7 @@ namespace mlir { using OpConversionPattern::OpConversionPattern; LogicalResult matchAndRewrite(low::SPNAdd op, - ArrayRef operands, + ArrayRef operands, ConversionPatternRewriter& rewriter) const override; }; @@ -88,7 +88,7 @@ namespace mlir { using OpConversionPattern::OpConversionPattern; LogicalResult matchAndRewrite(low::SPNLog op, - ArrayRef operands, + ArrayRef operands, ConversionPatternRewriter& rewriter) const override; }; @@ -97,7 +97,7 @@ namespace mlir { using OpConversionPattern::OpConversionPattern; LogicalResult matchAndRewrite(low::SPNGaussianLeaf op, - ArrayRef operands, + ArrayRef operands, ConversionPatternRewriter& rewriter) const override; }; @@ -106,7 +106,7 @@ namespace mlir { using OpConversionPattern::OpConversionPattern; LogicalResult matchAndRewrite(low::SPNGaussianLeaf op, - ArrayRef operands, + ArrayRef operands, ConversionPatternRewriter& rewriter) const override; }; @@ -115,7 +115,7 @@ namespace mlir { using OpConversionPattern::OpConversionPattern; LogicalResult matchAndRewrite(low::SPNCategoricalLeaf op, - ArrayRef operands, + ArrayRef operands, ConversionPatternRewriter& rewriter) const override; }; @@ -124,7 +124,7 @@ namespace mlir { using OpConversionPattern::OpConversionPattern; LogicalResult matchAndRewrite(low::SPNHistogramLeaf op, - ArrayRef operands, + ArrayRef operands, ConversionPatternRewriter& rewriter) const override; }; @@ -133,7 +133,7 @@ namespace mlir { using OpConversionPattern::OpConversionPattern; LogicalResult matchAndRewrite(low::SPNConstant op, - ArrayRef operands, + ArrayRef operands, ConversionPatternRewriter& rewriter) const override; }; @@ -142,7 +142,7 @@ namespace mlir { using OpConversionPattern::OpConversionPattern; LogicalResult matchAndRewrite(low::SPNStripLog op, - ArrayRef operands, + ArrayRef operands, ConversionPatternRewriter& rewriter) const override; }; diff --git a/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt b/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt index 9f792047..e38e2f1a 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt +++ b/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt @@ -10,6 +10,7 @@ add_mlir_conversion_library(MLIRLoSPNtoCPUTransforms Vectorization/SLP/SLPSeeding.cpp Vectorization/SLP/SLPTree.cpp Vectorization/SLP/SLPVectorizationPatterns.cpp + Vectorization/SLP/SLPVectorizationPass.cpp LoSPNtoCPUConversionPasses.cpp ADDITIONAL_HEADER_DIRS diff --git a/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp b/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp index 1d0c941d..49fafffe 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp @@ -10,12 +10,9 @@ #include "LoSPNtoCPU/NodePatterns.h" #include "LoSPNtoCPU/Vectorization/VectorizationPatterns.h" #include "LoSPNtoCPU/Vectorization/LoSPNVectorizationTypeConverter.h" -#include "mlir/Dialect/StandardOps/IR/Ops.h" #include "mlir/Dialect/SCF/SCF.h" #include "mlir/Dialect/Vector/VectorOps.h" -#include "mlir/Dialect/Math/IR/Math.h" #include "mlir/Dialect/Linalg/IR/LinalgOps.h" -#include "LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h" void mlir::spn::LoSPNtoCPUStructureConversionPass::runOnOperation() { ConversionTarget target(getContext()); @@ -34,7 +31,6 @@ void mlir::spn::LoSPNtoCPUStructureConversionPass::runOnOperation() { OwningRewritePatternList patterns; if (vectorize) { - // auto& depthAnalysis = getAnalysis(); // Try to vectorize tasks if vectorization was requested. mlir::spn::populateLoSPNCPUVectorizationStructurePatterns(patterns, &getContext(), typeConverter); } @@ -45,43 +41,6 @@ void mlir::spn::LoSPNtoCPUStructureConversionPass::runOnOperation() { if (failed(applyPartialConversion(op, target, frozenPatterns))) { signalPassFailure(); } - - if (vectorize) { - auto module = getOperation(); - - // ============ TREE CHECK ============ // - module.walk([&](Operation* op) { - llvm::StringMap> operationsByOpCode; - operationsByOpCode[op->getName().getStringRef().str()].emplace_back(op); - auto const& uses = std::distance(op->getUses().begin(), op->getUses().end()); - if (uses > 1) { - // TODO: how to handle such cases? and is special handling required at all? - //assert(false && "SPN is not a tree!"); - } - }); - // ==================================== // - - // auto& depthAnalysis = getAnalysis(); - auto& seedAnalysis = getAnalysis(); - /*auto seeds = seedAnalysis.getSeeds(4, depthAnalysis); - - if (!seeds.empty()) { - slp::SLPTree graph(seeds.front(), 3); - OwningRewritePatternList patterns; - std::vector> vectors; - for (auto const& node_ptr : graph.getNodes()) { - for (size_t i = 0; i < node_ptr->numVectors(); ++i) { - vectors.emplace_back(node_ptr->getVector(i)); - } - } - slp::populateVectorizationPatterns(patterns, &getContext(), vectors); - auto op = getOperation(); - FrozenRewritePatternList frozenPatterns(std::move(patterns)); - applyPatternsAndFoldGreedily(op, frozenPatterns); - } -*/ - } - } std::unique_ptr mlir::spn::createLoSPNtoCPUStructureConversionPass(bool enableVectorization) { diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp index 79d7c0ed..f32cdb62 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp @@ -4,28 +4,45 @@ // #include "LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h" -#include using namespace mlir; using namespace mlir::spn; using namespace mlir::spn::slp; -SeedAnalysis::SeedAnalysis(Operation* jointQuery) : jointQuery{jointQuery} {} +SeedAnalysis::SeedAnalysis(Operation* rootOp) : rootOp{rootOp} {} + +std::map SeedAnalysis::getOpDepths() const { + + std::map opDepths; + + rootOp->walk([&](Operation* op) { + if (!opDepths.count(op)) { + opDepths[op] = 0; + } + for (auto const& use : op->getUsers()) { + if (!opDepths.count(use) || opDepths[op] + 1 > opDepths[use]) { + opDepths[use] = opDepths[op] + 1; + } + } + }); + + return opDepths; +} std::vector SeedAnalysis::getSeeds(size_t const& width, - std::map const& nodeLevels, + std::map const& depthsOf, SearchMode const& mode) const { llvm::StringMap> seedsByOpName; - jointQuery->walk([&](Operation* op) { - auto depth = nodeLevels.at(op); - if (!op->hasTrait() || depth < log2(width)) { + rootOp->walk([&](Operation* op) { + auto depth = depthsOf.at(op); + if (!op->hasTrait() || depth < log2(width)) { return; } bool needsNewSeed = true; for (auto& seed : seedsByOpName[op->getName().getStringRef()]) { - if (seed.size() < width && nodeLevels.at(seed.front()) == depth) { + if (seed.size() < width && depthsOf.at(seed.front()) == depth) { seed.emplace_back(op); needsNewSeed = false; break; @@ -51,9 +68,9 @@ std::vector SeedAnalysis::getSeeds(size_t const& width, // or those closest to the leaves (LeafToRoot). std::sort(seeds.begin(), seeds.end(), [&](seed_t const& seed1, seed_t const& seed2) { if (mode == RootToLeaf) { - return nodeLevels.at(seed1.front()) < nodeLevels.at(seed2.front()); + return depthsOf.at(seed1.front()) < depthsOf.at(seed2.front()); } else if (mode == LeafToRoot) { - return nodeLevels.at(seed1.front()) > nodeLevels.at(seed2.front()); + return depthsOf.at(seed1.front()) > depthsOf.at(seed2.front()); } else { // Unused so far. assert(false); diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPTree.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPTree.cpp index bf54345f..16c9f81e 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPTree.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPTree.cpp @@ -83,18 +83,18 @@ void SLPTree::buildGraph(std::vector const& operations, node_t const } } } - // 2. Non-Commutative - // Only consider operands further when the current operations aren't leaf nodes. - assert(false); - /* - else if (!dyn_cast(operations.front())) { - for (auto const& operandOperations : getOperandsTransposed(operations)) { - auto const& operandNode = std::make_shared(operandOperations); - operandsOf[currentNode].emplace_back(operandNode); - buildGraph(operandOperations, operandNode); - } + // 2. Non-Commutative + // Only consider operands further when the current operations aren't leaf nodes. + assert(false); + /* +else if (!dyn_cast(operations.front())) { + for (auto const& operandOperations : getOperandsTransposed(operations)) { + auto const& operandNode = std::make_shared(operandOperations); + operandsOf[currentNode].emplace_back(operandNode); + buildGraph(operandOperations, operandNode); } - */ +} + */ } std::vector> SLPTree::reorderOperands(node_t const& multinode) { @@ -227,9 +227,9 @@ int SLPTree::getLookAheadScore(Operation* last, Operation* candidate, size_t con return 0; } assert(false); - /* if (dyn_cast(last)) { - return areConsecutive(last, candidate) ? 1 : 0; - }*/ + /* if (dyn_cast(last)) { + return areConsecutive(last, candidate) ? 1 : 0; + }*/ return 1; } auto scoreSum = 0; diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp new file mode 100644 index 00000000..6f0f4eb5 --- /dev/null +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp @@ -0,0 +1,66 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h" +#include "LoSPNtoCPU/Vectorization/TargetInformation.h" +#include "LoSPNtoCPU/LoSPNtoCPUTypeConverter.h" +#include "LoSPNtoCPU/StructurePatterns.h" +#include "LoSPNtoCPU/NodePatterns.h" +#include "mlir/Dialect/SCF/SCF.h" +#include "mlir/Dialect/Vector/VectorOps.h" +#include "mlir/Dialect/Linalg/IR/LinalgOps.h" +#include "LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h" + +void mlir::spn::SLPVectorizationPass::runOnOperation() { + llvm::StringRef funcName = getOperation().getName(); + if (!funcName.contains("task_")) { + return; + } + + ConversionTarget target(getContext()); + + target.addLegalDialect(); + target.addLegalDialect(); + target.addLegalDialect(); + target.addLegalOp(); + target.addLegalOp(); + + target.addLegalDialect(); + + auto function = getOperation(); + + auto& seedAnalysis = getAnalysis(); + + auto computationType = function.getArguments().back().getType().dyn_cast().getElementType(); + auto width = TargetInformation::nativeCPUTarget().getHWVectorEntries(computationType); + + getOperation()->dump(); + + auto seeds = seedAnalysis.getSeeds(width, seedAnalysis.getOpDepths()); + llvm::outs() << "nnn\n"; +/* + if (!seeds.empty()) { + slp::SLPTree graph(seeds.front(), 3); + OwningRewritePatternList patterns; + std::vector> vectors; + for (auto const& node_ptr : graph.getNodes()) { + for (size_t i = 0; i < node_ptr->numVectors(); ++i) { + vectors.emplace_back(node_ptr->getVector(i)); + } + } + slp::populateVectorizationPatterns(patterns, &getContext(), vectors); + auto op = getOperation(); + FrozenRewritePatternList frozenPatterns(std::move(patterns)); + applyPatternsAndFoldGreedily(op, frozenPatterns); + } +*/ + + +} + +std::unique_ptr mlir::spn::createSLPVectorizationPass() { + return std::make_unique(); +} + diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/TargetInformation.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/TargetInformation.cpp index 00f29f02..3e1d6026 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/TargetInformation.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/TargetInformation.cpp @@ -3,7 +3,7 @@ // Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. // -#include "TargetInformation.h" +#include "LoSPNtoCPU/Vectorization/TargetInformation.h" #include "llvm/Support/Host.h" #include "llvm/ADT/StringRef.h" #include diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeNodePatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeNodePatterns.cpp index b6eb977c..087f27a1 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeNodePatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeNodePatterns.cpp @@ -5,7 +5,7 @@ #include #include "LoSPNtoCPU/Vectorization/VectorizationPatterns.h" -#include "TargetInformation.h" +#include "LoSPNtoCPU/Vectorization/TargetInformation.h" #include "llvm/Support/FormatVariadic.h" #include "mlir/Dialect/StandardOps/IR/Ops.h" #include "mlir/Dialect/SCF/SCF.h" diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp index 02c84ff8..50e293b7 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp @@ -5,7 +5,7 @@ #include #include "LoSPNtoCPU/Vectorization/VectorizationPatterns.h" -#include "TargetInformation.h" +#include "LoSPNtoCPU/Vectorization/TargetInformation.h" #include "llvm/Support/FormatVariadic.h" #include "mlir/Dialect/StandardOps/IR/Ops.h" #include "mlir/Dialect/SCF/SCF.h" diff --git a/mlir/tools/spnc-opt/spnc-opt.cpp b/mlir/tools/spnc-opt/spnc-opt.cpp index 0579fd33..54777e09 100644 --- a/mlir/tools/spnc-opt/spnc-opt.cpp +++ b/mlir/tools/spnc-opt/spnc-opt.cpp @@ -17,6 +17,7 @@ #include "LoSPN/LoSPNPasses.h" #include "HiSPNtoLoSPN/HiSPNtoLoSPNConversionPasses.h" #include "LoSPNtoCPU/LoSPNtoCPUConversionPasses.h" +#include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h" static llvm::cl::opt inputFilename(llvm::cl::Positional, llvm::cl::desc(""), @@ -98,6 +99,11 @@ int main(int argc, char** argv) { return mlir::spn::createLoSPNNodeVectorizationPass(); }); + mlir::registerPass("slp-vectorize-structure", "Run SLP vectorization of the structure for CPU target", + []() -> std::unique_ptr { + return mlir::spn::createSLPVectorizationPass(); + }); + mlir::registerPass("spn-to-dot", "Prints the SPN to stderr", []() -> std::unique_ptr { return mlir::createPrintOpGraphPass(); }); From ba4494553f6dc657387664538c9a19485d59a80d Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Fri, 19 Mar 2021 03:14:03 +0100 Subject: [PATCH 048/385] Adapted SLP graph generation to standard dialect. --- .../SLP/{SLPTree.h => SLPGraph.h} | 100 +++++++++++------- .../LoSPNtoCPU/Vectorization/SLP/SLPMode.h | 2 - .../LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h | 6 +- mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt | 2 +- .../SLP/{SLPTree.cpp => SLPGraph.cpp} | 57 +++++----- .../LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp | 8 +- .../Vectorization/SLP/SLPSeeding.cpp | 10 +- .../SLP/SLPVectorizationPass.cpp | 32 +++--- 8 files changed, 122 insertions(+), 95 deletions(-) rename mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/{SLPTree.h => SLPGraph.h} (65%) rename mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/{SLPTree.cpp => SLPGraph.cpp} (83%) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPTree.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h similarity index 65% rename from mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPTree.h rename to mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h index 38c7899c..83daa706 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPTree.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h @@ -3,10 +3,11 @@ // Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. // -#ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPTREE_H -#define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPTREE_H +#ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPGRAPH_H +#define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPGRAPH_H #include "mlir/IR/Operation.h" +#include "mlir/Dialect/StandardOps/IR/Ops.h" #include "LoSPN/LoSPNTraits.h" #include "SLPMode.h" #include "SLPNode.h" @@ -18,13 +19,13 @@ namespace mlir { namespace spn { namespace slp { - class SLPTree { + class SLPGraph { typedef std::shared_ptr node_t; public: - SLPTree(seed_t const& seed, size_t const& maxLookAhead); + SLPGraph(seed_t const& seed, size_t const& maxLookAhead); std::vector> getNodes() const; @@ -41,37 +42,53 @@ namespace mlir { int getLookAheadScore(Operation* last, Operation* candidate, size_t const& maxLevel) const; static bool areConsecutive(Operation* op1, Operation* op2) { - assert(false); + auto load1 = dyn_cast(op1); + auto load2 = dyn_cast(op2); + if (!load1 || !load2) { + return false; + } + if (load1.indices().size() != load2.indices().size()) { + return false; + } + for (size_t i = 0; i < load1.indices().size(); ++i) { + auto const1 = load1.indices()[i].getDefiningOp(); + auto const2 = load2.indices()[i].getDefiningOp(); + if (!const1 || !const2) { + return false; + } + auto index1 = const1.value().dyn_cast(); + auto index2 = const2.value().dyn_cast(); + if (!index1 || !index2) { + return false; + } + if (i == load1.indices().size() - 1) { + return index1.getInt() == index2.getInt() - 1; + } else if (index1.getInt() != index2.getInt()) { + return false; + } + } return false; } - /// Checks if the given operations are vectorizable. Operations are vectorizable iff the SPN dialect says they're - /// vectorizable and they all share the same opcode. + /// Checks if the given operations are vectorizable. /// \param operations The potentially vectorizable operations. /// \return True if the operations can be vectorized, otherwise false. static bool vectorizable(std::vector const& operations) { for (size_t i = 0; i < operations.size(); ++i) { - if (!operations.at(i)->hasTrait() - || (i > 0 && operations.at(i)->getName() != operations.front()->getName())) { + auto* op = operations[i]; + if (!op->hasTrait() || (i > 0 && op->getName() != operations.front()->getName())) { return false; } } - /* - if (dyn_cast(operations.front())) { - for (size_t i = 1; i < operations.size(); ++i) { - if (!areConsecutive(operations.at(i - 1), operations.at(i))) { - return false; - } - } - } - */ - assert(false); return true; } static bool commutative(std::vector const& operations) { - return std::all_of(std::begin(operations), std::end(operations), [&](Operation* operation) { - return operation->hasTrait(); + return std::all_of(std::begin(operations), std::end(operations), [&](Operation* op) { + if (op->hasTrait()) { + return true; + } + return dyn_cast(op) || dyn_cast(op); }); } @@ -153,24 +170,35 @@ namespace mlir { } } - llvm::errs() << "digraph debug_graph {\n"; - llvm::errs() << "rankdir = BT;\n"; - llvm::errs() << "node[shape=box];\n"; + llvm::dbgs() << "digraph debug_graph {\n"; + llvm::dbgs() << "rankdir = BT;\n"; + llvm::dbgs() << "node[shape=box];\n"; for (auto& op : nodes) { - llvm::errs() << "node_" << op << "[label=\"" << op->getName().getStringRef() << "\\n" << op; - assert(false); - /* - if (auto leaf = dyn_cast(op)) { - llvm::errs() << "\\narg: " << leaf.getFeatureIndex() << "\\n"; - } - */ - llvm::errs() << "\", fillcolor=\"#a0522d\"];\n"; + printNode(op); } for (auto& edge : edges) { - llvm::errs() << "node_" << std::get<0>(edge) << " -> node_" << std::get<1>(edge) << "[label=\"" - << std::get<2>(edge) << "\"];\n"; + printEdge(std::get<0>(edge), std::get<1>(edge), std::get<2>(edge)); } - llvm::errs() << "}\n"; + llvm::dbgs() << "}\n"; + } + + /// For debugging purposes. + static void printNode(Operation* op) { + llvm::dbgs() << "node_" << op << "[label=\"" << op->getName().getStringRef() << "\\n" << op; + if (auto constantOp = dyn_cast(op)) { + if (constantOp.value().getType().isIntOrIndex()) { + llvm::dbgs() << "\\nvalue: " << std::to_string(constantOp.value().dyn_cast().getInt()); + } else if (constantOp.value().getType().isIntOrFloat()) { + llvm::dbgs() << "\\nvalue: " + << std::to_string(constantOp.value().dyn_cast().getValueAsDouble()); + } + } + llvm::dbgs() << "\", fillcolor=\"#a0522d\"];\n"; + } + + /// For debugging purposes. + static void printEdge(Operation* src, Operation* dst, size_t index) { + llvm::dbgs() << "node_" << src << " -> node_" << dst << "[label=\"" << std::to_string(index) << "\"];\n"; } std::map> operandsOf; @@ -182,4 +210,4 @@ namespace mlir { } } -#endif //SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPTREE_H +#endif //SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPGRAPH_H diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.h index 3b032bba..c829b394 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.h @@ -16,12 +16,10 @@ namespace mlir { // look for a constant CONST, // look for a consecutive load to that in the previous lane - // TODO: do we have load operations? or something that can be treated equivalently? LOAD, // look for an operation of the same opcode OPCODE, // look for the exact same operation - // TODO: determine whether SPLAT mode is actually relevant (are the same operands used in more than one lane?) SPLAT, // vectorization has failed, give higher priority to others FAILED diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h index 37de9fb3..43d89569 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h @@ -16,9 +16,9 @@ namespace mlir { enum SearchMode { /// - RootToLeaf, + DefBeforeUse, /// - LeafToRoot, + UseBeforeDef, /// TODO? Chain }; @@ -33,7 +33,7 @@ namespace mlir { std::vector getSeeds(size_t const& op, std::map const& depthsOf, - SearchMode const& mode = RootToLeaf) const; + SearchMode const& mode) const; private: diff --git a/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt b/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt index e38e2f1a..e7574654 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt +++ b/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt @@ -8,7 +8,7 @@ add_mlir_conversion_library(MLIRLoSPNtoCPUTransforms Vectorization/SLP/SLPMode.cpp Vectorization/SLP/SLPNode.cpp Vectorization/SLP/SLPSeeding.cpp - Vectorization/SLP/SLPTree.cpp + Vectorization/SLP/SLPGraph.cpp Vectorization/SLP/SLPVectorizationPatterns.cpp Vectorization/SLP/SLPVectorizationPass.cpp LoSPNtoCPUConversionPasses.cpp diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPTree.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp similarity index 83% rename from mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPTree.cpp rename to mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp index 16c9f81e..6e741fc9 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPTree.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp @@ -3,21 +3,25 @@ // Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. // -#include "LoSPNtoCPU/Vectorization/SLP/SLPTree.h" +#include "LoSPNtoCPU/Vectorization/SLP/SLPGraph.h" #include using namespace mlir; using namespace mlir::spn; using namespace mlir::spn::slp; -SLPTree::SLPTree(seed_t const& seed, size_t const& maxLookAhead) : operandsOf{}, maxLookAhead{maxLookAhead} { +SLPGraph::SLPGraph(seed_t const& seed, size_t const& maxLookAhead) : operandsOf{}, maxLookAhead{maxLookAhead} { auto const& currentNode = std::make_shared(seed); operandsOf[currentNode] = {}; - printSubgraph(seed); + //printSubgraph(seed); + llvm::dbgs() << "Building graph with seed:\n"; + for (auto* op : seed) { + printNode(op); + } buildGraph(seed, currentNode); } -std::vector> SLPTree::getNodes() const { +std::vector> SLPGraph::getNodes() const { std::vector> nodes; for (auto const& entry : operandsOf) { nodes.emplace_back(entry.first); @@ -30,12 +34,7 @@ std::vector> SLPTree::getNodes() const { return nodes; } -void SLPTree::buildGraph(std::vector const& operations, node_t const& currentNode) { - llvm::errs() << "Building graph with:\n"; - for (auto* op : operations) { - llvm::errs() << op << ": "; - op->dump(); - } +void SLPGraph::buildGraph(std::vector const& operations, node_t const& currentNode) { // Stop growing graph if (!vectorizable(operations)) { return; @@ -50,6 +49,11 @@ void SLPTree::buildGraph(std::vector const& operations, node_t const sortByOpcode(operands, currentOpCode); } for (size_t i = 0; i < operations.front()->getNumOperands(); ++i) { + llvm::dbgs() << "\n"; + for (auto* op : operations) { + printNode(op->getOperand(i).getDefiningOp()); + printEdge(op, op->getOperand(i).getDefiningOp(), i); + } if (std::all_of(std::begin(allOperands), std::end(allOperands), [&](auto const& operandOperations) { return operandOperations[i]->getName() == currentOpCode && !escapesMultinode(operandOperations[i]); })) { @@ -63,9 +67,6 @@ void SLPTree::buildGraph(std::vector const& operations, node_t const for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { operandOperations.emplace_back(allOperands[lane][i]); } - for (auto const& tmp : operandOperations) { - tmp->dump(); - } operandsOf[currentNode].emplace_back(std::make_shared(operandOperations)); } } @@ -83,21 +84,19 @@ void SLPTree::buildGraph(std::vector const& operations, node_t const } } } - // 2. Non-Commutative - // Only consider operands further when the current operations aren't leaf nodes. - assert(false); - /* -else if (!dyn_cast(operations.front())) { - for (auto const& operandOperations : getOperandsTransposed(operations)) { - auto const& operandNode = std::make_shared(operandOperations); - operandsOf[currentNode].emplace_back(operandNode); - buildGraph(operandOperations, operandNode); + // 2. Non-Commutative + // Only consider operands further when the current operations aren't leaf nodes. + else if (!dyn_cast(operations.front())) { + for (auto const& operandOperations : getOperandsTransposed(operations)) { + auto const& operandNode = std::make_shared(operandOperations); + operandsOf[currentNode].emplace_back(operandNode); + buildGraph(operandOperations, operandNode); + } } -} - */ + } -std::vector> SLPTree::reorderOperands(node_t const& multinode) { +std::vector> SLPGraph::reorderOperands(node_t const& multinode) { assert(multinode->isMultiNode()); auto const& numOperands = operandsOf.at(multinode).size(); std::vector> finalOrder{multinode->numLanes()}; @@ -151,9 +150,9 @@ std::vector> SLPTree::reorderOperands(node_t const& mult return finalOrder; } -std::pair SLPTree::getBest(Mode const& mode, - Operation* last, - std::vector& candidates) const { +std::pair SLPGraph::getBest(Mode const& mode, + Operation* last, + std::vector& candidates) const { Operation* best = nullptr; Mode resultMode = mode; std::vector bestCandidates; @@ -221,7 +220,7 @@ std::pair SLPTree::getBest(Mode const& mode, return {best, resultMode}; } -int SLPTree::getLookAheadScore(Operation* last, Operation* candidate, size_t const& maxLevel) const { +int SLPGraph::getLookAheadScore(Operation* last, Operation* candidate, size_t const& maxLevel) const { if (maxLevel == 0) { if (last->getName() != candidate->getName()) { return 0; diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp index 6eef770f..4263d620 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp @@ -3,17 +3,19 @@ // Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. // +#include "mlir/Dialect/StandardOps/IR/Ops.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPMode.h" using namespace mlir; using namespace mlir::spn; using namespace mlir::spn::slp; -Mode slp::modeFromOperation(Operation const* operation) { - if (dyn_cast(operation)) { +Mode slp::modeFromOperation(const Operation* operation) { + if (dyn_cast(operation)) { return CONST; + } else if (dyn_cast(operation)) { + return LOAD; } - // We don't have LOADs. Therefore just return OPCODE. return OPCODE; } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp index f32cdb62..3706fb1c 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp @@ -19,7 +19,7 @@ std::map SeedAnalysis::getOpDepths() const { if (!opDepths.count(op)) { opDepths[op] = 0; } - for (auto const& use : op->getUsers()) { + for (auto* use : op->getUsers()) { if (!opDepths.count(use) || opDepths[op] + 1 > opDepths[use]) { opDepths[use] = opDepths[op] + 1; } @@ -64,12 +64,12 @@ std::vector SeedAnalysis::getSeeds(size_t const& width, seeds.emplace_back(seed); } } - // Sort the seeds such that either the seeds closest to the root come first (RootToLeaf), - // or those closest to the leaves (LeafToRoot). + // Sort the seeds such that either the seeds closest to the beginning of the function come first (DefBeforeUse), + // or those closest to the return statement (UseBeforeDef). std::sort(seeds.begin(), seeds.end(), [&](seed_t const& seed1, seed_t const& seed2) { - if (mode == RootToLeaf) { + if (mode == DefBeforeUse) { return depthsOf.at(seed1.front()) < depthsOf.at(seed2.front()); - } else if (mode == LeafToRoot) { + } else if (mode == UseBeforeDef) { return depthsOf.at(seed1.front()) > depthsOf.at(seed2.front()); } else { // Unused so far. diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp index 6f0f4eb5..c45e5623 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp @@ -12,6 +12,7 @@ #include "mlir/Dialect/Vector/VectorOps.h" #include "mlir/Dialect/Linalg/IR/LinalgOps.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h" +#include "LoSPNtoCPU/Vectorization/SLP/SLPGraph.h" void mlir::spn::SLPVectorizationPass::runOnOperation() { llvm::StringRef funcName = getOperation().getName(); @@ -38,24 +39,23 @@ void mlir::spn::SLPVectorizationPass::runOnOperation() { getOperation()->dump(); - auto seeds = seedAnalysis.getSeeds(width, seedAnalysis.getOpDepths()); - llvm::outs() << "nnn\n"; -/* - if (!seeds.empty()) { - slp::SLPTree graph(seeds.front(), 3); - OwningRewritePatternList patterns; - std::vector> vectors; - for (auto const& node_ptr : graph.getNodes()) { - for (size_t i = 0; i < node_ptr->numVectors(); ++i) { - vectors.emplace_back(node_ptr->getVector(i)); - } + auto seeds = seedAnalysis.getSeeds(width, seedAnalysis.getOpDepths(), slp::UseBeforeDef); + assert(!seeds.empty() && "couldn't find a seed!"); + + slp::SLPGraph tree(seeds.front(), 3); + std::vector> vectors; + for (auto const& node_ptr : tree.getNodes()) { + for (size_t i = 0; i < node_ptr->numVectors(); ++i) { + vectors.emplace_back(node_ptr->getVector(i)); } - slp::populateVectorizationPatterns(patterns, &getContext(), vectors); - auto op = getOperation(); - FrozenRewritePatternList frozenPatterns(std::move(patterns)); - applyPatternsAndFoldGreedily(op, frozenPatterns); } -*/ + /* + OwningRewritePatternList patterns; + slp::populateVectorizationPatterns(patterns, &getContext(), vectors); + auto op = getOperation(); + FrozenRewritePatternList frozenPatterns(std::move(patterns)); + applyPatternsAndFoldGreedily(op, frozenPatterns); + */ } From 622c72c6b0f06b932c4e7b318ce46708b5caa20c Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 21 Mar 2021 04:17:37 +0100 Subject: [PATCH 049/385] Added some consecutive fixes, did some code movement. --- .../LoSPNtoCPU/Vectorization/SLP/SLPGraph.h | 89 ++++++++----------- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.h | 1 + .../Vectorization/SLP/SLPVectorizationPass.h | 59 ++++++++++++ .../LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp | 65 +++++++------- 4 files changed, 131 insertions(+), 83 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h index 83daa706..89ca13f5 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h @@ -33,7 +33,7 @@ namespace mlir { void buildGraph(std::vector const& operations, node_t const& currentNode); - std::vector> reorderOperands(node_t const& multinode); + void reorderOperands(node_t const& multinode); std::pair getBest(Mode const& mode, Operation* last, @@ -116,15 +116,19 @@ namespace mlir { return allOperands; } - static std::vector> getOperandsTransposed(std::vector const& operations) { + static std::vector> getOperandsVectorized(std::vector const& operations) { + for (auto* operation : operations) { + assert(operation->getNumOperands() == operations.front()->getNumOperands() + && "operations must have same numbers of operands"); + } std::vector> allOperands; for (size_t i = 0; i < operations.front()->getNumOperands(); ++i) { - std::vector operand; - operand.reserve(operations.size()); + std::vector operands; + operands.reserve(operations.size()); for (auto* operation : operations) { - operand.emplace_back(operation->getOperand(i).getDefiningOp()); + operands.emplace_back(operation->getOperand(i).getDefiningOp()); } - allOperands.emplace_back(operand); + allOperands.emplace_back(operands); } return allOperands; } @@ -143,62 +147,43 @@ namespace mlir { }); } - /// For debugging purposes. - static void printSubgraph(std::vector const& operations) { - std::vector nodes; - std::vector> edges; - - std::stack worklist; - for (auto& op : operations) { - worklist.push(op); - } - - while (!worklist.empty()) { - - auto op = worklist.top(); - worklist.pop(); - - if (std::find(std::begin(nodes), std::end(nodes), op) == nodes.end()) { - nodes.emplace_back(op); - for (size_t i = 0; i < op->getNumOperands(); ++i) { - auto const& operand = op->getOperand(i); - if (operand.getDefiningOp() != nullptr) { - edges.emplace_back(std::make_tuple(op, operand.getDefiningOp(), i)); - worklist.push(operand.getDefiningOp()); - } - } - } - } - + static void print(SLPGraph const& graph) { llvm::dbgs() << "digraph debug_graph {\n"; llvm::dbgs() << "rankdir = BT;\n"; llvm::dbgs() << "node[shape=box];\n"; - for (auto& op : nodes) { - printNode(op); - } - for (auto& edge : edges) { - printEdge(std::get<0>(edge), std::get<1>(edge), std::get<2>(edge)); + for (auto const& entry : graph.operandsOf) { + printNode(entry.first.get()); + for (auto const& operand : entry.second) { + llvm::dbgs() << "node_" << entry.first.get() << "->" << "node_" << operand.get() << ";\n"; + } } llvm::dbgs() << "}\n"; } /// For debugging purposes. - static void printNode(Operation* op) { - llvm::dbgs() << "node_" << op << "[label=\"" << op->getName().getStringRef() << "\\n" << op; - if (auto constantOp = dyn_cast(op)) { - if (constantOp.value().getType().isIntOrIndex()) { - llvm::dbgs() << "\\nvalue: " << std::to_string(constantOp.value().dyn_cast().getInt()); - } else if (constantOp.value().getType().isIntOrFloat()) { - llvm::dbgs() << "\\nvalue: " - << std::to_string(constantOp.value().dyn_cast().getValueAsDouble()); + static void printNode(SLPNode const* node) { + llvm::dbgs() << "node_" << node << "[label=<\n"; + llvm::dbgs() + << "\t\n"; + for (size_t i = node->numVectors(); i-- > 0;) { + llvm::dbgs() << "\t\t\n"; + for (size_t j = 0; j < node->numLanes(); ++j) { + auto* operation = node->getOperation(j, i); + if (std::intptr_t(operation) == 0x903a08) { + llvm::dbgs(); + } + llvm::dbgs() << "\t\t\t\n"; + if (j < node->numLanes() - 1) { + llvm::dbgs() << "\t\t\t\n"; + } } + llvm::dbgs() << "\t\t\n"; } - llvm::dbgs() << "\", fillcolor=\"#a0522d\"];\n"; - } - - /// For debugging purposes. - static void printEdge(Operation* src, Operation* dst, size_t index) { - llvm::dbgs() << "node_" << src << " -> node_" << dst << "[label=\"" << std::to_string(index) << "\"];\n"; + llvm::dbgs() << "\t
\n"; + llvm::dbgs() << "\t\t\t\t" << operation->getName() << " (" << operation + << ")\n"; + llvm::dbgs() << "\t\t\t
\n"; + llvm::dbgs() << ">];\n"; } std::map> operandsOf; diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h index f93396d6..e8019a52 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h @@ -18,6 +18,7 @@ namespace mlir { public: + SLPNode() = delete; explicit SLPNode(std::vector const& operations); void addOperationToLane(Operation* operation, size_t const& lane); diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h index 834e1401..e78075b0 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h @@ -7,6 +7,7 @@ #define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPVECTORIZATIONPASS_H #include "mlir/Pass/Pass.h" +#include "mlir/Dialect/StandardOps/IR/Ops.h" namespace mlir { namespace spn { @@ -22,6 +23,64 @@ namespace mlir { private: + /// For debugging purposes. + static void printSubgraph(std::vector const& operations) { + std::vector nodes; + std::vector> edges; + + std::stack worklist; + for (auto& op : operations) { + worklist.push(op); + } + + while (!worklist.empty()) { + + auto op = worklist.top(); + worklist.pop(); + + if (std::find(std::begin(nodes), std::end(nodes), op) == nodes.end()) { + nodes.emplace_back(op); + for (size_t i = 0; i < op->getNumOperands(); ++i) { + auto const& operand = op->getOperand(i); + if (operand.getDefiningOp() != nullptr) { + edges.emplace_back(std::make_tuple(op, operand.getDefiningOp(), i)); + worklist.push(operand.getDefiningOp()); + } + } + } + } + + llvm::dbgs() << "digraph debug_graph {\n"; + llvm::dbgs() << "rankdir = BT;\n"; + llvm::dbgs() << "node[shape=box];\n"; + for (auto& op : nodes) { + printNode(op); + } + for (auto& edge : edges) { + printEdge(std::get<0>(edge), std::get<1>(edge), std::get<2>(edge)); + } + llvm::dbgs() << "}\n"; + } + + /// For debugging purposes. + static void printNode(Operation* op) { + llvm::dbgs() << "node_" << op << "[label=\"" << op->getName().getStringRef() << "\\n" << op; + if (auto constantOp = dyn_cast(op)) { + if (constantOp.value().getType().isIntOrIndex()) { + llvm::dbgs() << "\\nvalue: " << std::to_string(constantOp.value().dyn_cast().getInt()); + } else if (constantOp.value().getType().isIntOrFloat()) { + llvm::dbgs() << "\\nvalue: " + << std::to_string(constantOp.value().dyn_cast().getValueAsDouble()); + } + } + llvm::dbgs() << "\", fillcolor=\"#a0522d\"];\n"; + } + + /// For debugging purposes. + static void printEdge(Operation* src, Operation* dst, size_t index) { + llvm::dbgs() << "node_" << src << " -> node_" << dst << "[label=\"" << std::to_string(index) << "\"];\n"; + } + }; std::unique_ptr createSLPVectorizationPass(); diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp index 6e741fc9..2e61210f 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp @@ -13,12 +13,8 @@ using namespace mlir::spn::slp; SLPGraph::SLPGraph(seed_t const& seed, size_t const& maxLookAhead) : operandsOf{}, maxLookAhead{maxLookAhead} { auto const& currentNode = std::make_shared(seed); operandsOf[currentNode] = {}; - //printSubgraph(seed); - llvm::dbgs() << "Building graph with seed:\n"; - for (auto* op : seed) { - printNode(op); - } buildGraph(seed, currentNode); + print(*this); } std::vector> SLPGraph::getNodes() const { @@ -35,6 +31,9 @@ std::vector> SLPGraph::getNodes() const { } void SLPGraph::buildGraph(std::vector const& operations, node_t const& currentNode) { + if (std::intptr_t(operations.front()) == 0x5ebd28) { + llvm::dbgs(); + } // Stop growing graph if (!vectorizable(operations)) { return; @@ -49,11 +48,6 @@ void SLPGraph::buildGraph(std::vector const& operations, node_t cons sortByOpcode(operands, currentOpCode); } for (size_t i = 0; i < operations.front()->getNumOperands(); ++i) { - llvm::dbgs() << "\n"; - for (auto* op : operations) { - printNode(op->getOperand(i).getDefiningOp()); - printEdge(op, op->getOperand(i).getDefiningOp(), i); - } if (std::all_of(std::begin(allOperands), std::end(allOperands), [&](auto const& operandOperations) { return operandOperations[i]->getName() == currentOpCode && !escapesMultinode(operandOperations[i]); })) { @@ -67,36 +61,40 @@ void SLPGraph::buildGraph(std::vector const& operations, node_t cons for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { operandOperations.emplace_back(allOperands[lane][i]); } - operandsOf[currentNode].emplace_back(std::make_shared(operandOperations)); + auto const& operandNode = std::make_shared(operandOperations); + operandsOf[currentNode].emplace_back(operandNode); + operandsOf[operandNode] = {}; } } // B. Normal Mode: Finished building multi-node if (currentNode->isMultiNode() && currentNode->areRootOfNode(operations)) { - auto const& order = reorderOperands(currentNode); - for (size_t operandIndex = 0; operandIndex < operandsOf.at(currentNode).size(); ++operandIndex) { - for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { - operandsOf.at(currentNode).at(operandIndex)->setOperation(lane, 0, order[lane][operandIndex]); - } - } + reorderOperands(currentNode); for (auto& operandNode : operandsOf.at(currentNode)) { buildGraph(operandNode->getVector(operandNode->numVectors() - 1), operandNode); } } } // 2. Non-Commutative + else { // Only consider operands further when the current operations aren't leaf nodes. - else if (!dyn_cast(operations.front())) { - for (auto const& operandOperations : getOperandsTransposed(operations)) { - auto const& operandNode = std::make_shared(operandOperations); + for (auto* operation : operations) { + for (auto const& operand : operation->getOperands()) { + if (operand.isa()) { + return; + } + } + } + for (auto const& operandVector : getOperandsVectorized(operations)) { + auto const& operandNode = std::make_shared(operandVector); operandsOf[currentNode].emplace_back(operandNode); - buildGraph(operandOperations, operandNode); + operandsOf[operandNode] = {}; + buildGraph(operandVector, operandNode); } } - } -std::vector> SLPGraph::reorderOperands(node_t const& multinode) { +void SLPGraph::reorderOperands(node_t const& multinode) { assert(multinode->isMultiNode()); auto const& numOperands = operandsOf.at(multinode).size(); std::vector> finalOrder{multinode->numLanes()}; @@ -147,7 +145,11 @@ std::vector> SLPGraph::reorderOperands(node_t const& mul } } } - return finalOrder; + for (size_t operandIndex = 0; operandIndex < operandsOf.at(multinode).size(); ++operandIndex) { + for (size_t lane = 0; lane < multinode->numLanes(); ++lane) { + operandsOf.at(multinode)[operandIndex]->setOperation(lane, 0, finalOrder[lane][operandIndex]); + } + } } std::pair SLPGraph::getBest(Mode const& mode, @@ -172,8 +174,11 @@ std::pair SLPGraph::getBest(Mode const& mode, // Default value best = candidates.front(); for (auto& candidate : candidates) { - // We don't have consecutive memory accesses, therefore we're only interested in opcode comparisons. - if (candidate->getName() == last->getName()) { + if (mode == LOAD) { + if (areConsecutive(last, candidate)) { + bestCandidates.emplace_back(candidate); + } + } else if (candidate->getName() == last->getName()) { bestCandidates.emplace_back(candidate); } } @@ -215,7 +220,7 @@ std::pair SLPGraph::getBest(Mode const& mode, } // Remove best from candidates if (best != nullptr) { - candidates.erase(std::remove(std::begin(candidates), std::end(candidates), best), std::end(candidates)); + candidates.erase(std::find(std::begin(candidates), std::end(candidates), best)); } return {best, resultMode}; } @@ -225,14 +230,12 @@ int SLPGraph::getLookAheadScore(Operation* last, Operation* candidate, size_t co if (last->getName() != candidate->getName()) { return 0; } - assert(false); - /* if (dyn_cast(last)) { + if (dyn_cast(last)) { return areConsecutive(last, candidate) ? 1 : 0; - }*/ + } return 1; } auto scoreSum = 0; - // TODO: if operands are commutative, sort operands to speed up lookahead? for (auto& lastOperand : getOperands(last)) { for (auto& candidateOperand : getOperands(candidate)) { // Can be null if the operand is a block argument. From be82c7d044eb1b90b34a94cbf009b6174e921ec1 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 21 Mar 2021 04:40:08 +0100 Subject: [PATCH 050/385] Better dot output. --- .../LoSPNtoCPU/Vectorization/SLP/SLPGraph.h | 16 +++++++--------- .../Vectorization/SLP/SLPVectorizationPass.h | 6 +++--- .../Vectorization/SLP/SLPVectorizationPass.cpp | 6 ++---- 3 files changed, 12 insertions(+), 16 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h index 89ca13f5..ca4eec7c 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h @@ -164,21 +164,19 @@ namespace mlir { static void printNode(SLPNode const* node) { llvm::dbgs() << "node_" << node << "[label=<\n"; llvm::dbgs() - << "\t\n"; + << "\t
\n"; for (size_t i = node->numVectors(); i-- > 0;) { llvm::dbgs() << "\t\t\n"; for (size_t j = 0; j < node->numLanes(); ++j) { auto* operation = node->getOperation(j, i); - if (std::intptr_t(operation) == 0x903a08) { - llvm::dbgs(); - } - llvm::dbgs() << "\t\t\t\n"; + llvm::dbgs() << "\t\t\t"; if (j < node->numLanes() - 1) { - llvm::dbgs() << "\t\t\t\n"; + llvm::dbgs() << ""; } + llvm::dbgs() << "\n"; } llvm::dbgs() << "\t\t\n"; } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h index e78075b0..414a8455 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h @@ -24,7 +24,7 @@ namespace mlir { private: /// For debugging purposes. - static void printSubgraph(std::vector const& operations) { + static void printSPN(std::vector const& operations) { std::vector nodes; std::vector> edges; @@ -54,7 +54,7 @@ namespace mlir { llvm::dbgs() << "rankdir = BT;\n"; llvm::dbgs() << "node[shape=box];\n"; for (auto& op : nodes) { - printNode(op); + printOperation(op); } for (auto& edge : edges) { printEdge(std::get<0>(edge), std::get<1>(edge), std::get<2>(edge)); @@ -63,7 +63,7 @@ namespace mlir { } /// For debugging purposes. - static void printNode(Operation* op) { + static void printOperation(Operation* op) { llvm::dbgs() << "node_" << op << "[label=\"" << op->getName().getStringRef() << "\\n" << op; if (auto constantOp = dyn_cast(op)) { if (constantOp.value().getType().isIntOrIndex()) { diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp index c45e5623..ca2f9eb5 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp @@ -37,14 +37,12 @@ void mlir::spn::SLPVectorizationPass::runOnOperation() { auto computationType = function.getArguments().back().getType().dyn_cast().getElementType(); auto width = TargetInformation::nativeCPUTarget().getHWVectorEntries(computationType); - getOperation()->dump(); - auto seeds = seedAnalysis.getSeeds(width, seedAnalysis.getOpDepths(), slp::UseBeforeDef); assert(!seeds.empty() && "couldn't find a seed!"); - slp::SLPGraph tree(seeds.front(), 3); + slp::SLPGraph graph(seeds.front(), 3); std::vector> vectors; - for (auto const& node_ptr : tree.getNodes()) { + for (auto const& node_ptr : graph.getNodes()) { for (size_t i = 0; i < node_ptr->numVectors(); ++i) { vectors.emplace_back(node_ptr->getVector(i)); } From 9701ef89c50f861273da26596b5f0b2f2b246cc9 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 21 Mar 2021 05:21:02 +0100 Subject: [PATCH 051/385] Code movement. --- .../LoSPNtoCPU/Vectorization/SLP/SLPGraph.h | 39 +------------------ .../LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp | 32 ++++++++++++++- .../SLP/SLPVectorizationPass.cpp | 1 + 3 files changed, 34 insertions(+), 38 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h index ca4eec7c..100ddc36 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h @@ -29,6 +29,8 @@ namespace mlir { std::vector> getNodes() const; + void dump() const; + private: void buildGraph(std::vector const& operations, node_t const& currentNode); @@ -147,43 +149,6 @@ namespace mlir { }); } - static void print(SLPGraph const& graph) { - llvm::dbgs() << "digraph debug_graph {\n"; - llvm::dbgs() << "rankdir = BT;\n"; - llvm::dbgs() << "node[shape=box];\n"; - for (auto const& entry : graph.operandsOf) { - printNode(entry.first.get()); - for (auto const& operand : entry.second) { - llvm::dbgs() << "node_" << entry.first.get() << "->" << "node_" << operand.get() << ";\n"; - } - } - llvm::dbgs() << "}\n"; - } - - /// For debugging purposes. - static void printNode(SLPNode const* node) { - llvm::dbgs() << "node_" << node << "[label=<\n"; - llvm::dbgs() - << "\t
\n"; - llvm::dbgs() << "\t\t\t\t" << operation->getName() << " (" << operation - << ")\n"; - llvm::dbgs() << "\t\t\t"; + llvm::dbgs() << "" << operation->getName() << " (" << operation + << ")"; + llvm::dbgs() << "
\n"; - for (size_t i = node->numVectors(); i-- > 0;) { - llvm::dbgs() << "\t\t\n"; - for (size_t j = 0; j < node->numLanes(); ++j) { - auto* operation = node->getOperation(j, i); - llvm::dbgs() << "\t\t\t"; - if (j < node->numLanes() - 1) { - llvm::dbgs() << ""; - } - llvm::dbgs() << "\n"; - } - llvm::dbgs() << "\t\t\n"; - } - llvm::dbgs() << "\t
"; - llvm::dbgs() << "" << operation->getName() << " (" << operation - << ")"; - llvm::dbgs() << "
\n"; - llvm::dbgs() << ">];\n"; - } - std::map> operandsOf; size_t const maxLookAhead; diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp index 2e61210f..efc448bc 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp @@ -14,7 +14,6 @@ SLPGraph::SLPGraph(seed_t const& seed, size_t const& maxLookAhead) : operandsOf{ auto const& currentNode = std::make_shared(seed); operandsOf[currentNode] = {}; buildGraph(seed, currentNode); - print(*this); } std::vector> SLPGraph::getNodes() const { @@ -248,3 +247,34 @@ int SLPGraph::getLookAheadScore(Operation* last, Operation* candidate, size_t co } return scoreSum; } + +void SLPGraph::dump() const { + llvm::dbgs() << "digraph debug_graph {\n"; + llvm::dbgs() << "rankdir = BT;\n"; + llvm::dbgs() << "node[shape=box];\n"; + for (auto const& entry : operandsOf) { + auto const* node = entry.first.get(); + llvm::dbgs() << "node_" << node << "[label=<\n"; + llvm::dbgs() << "\t\n"; + for (size_t i = node->numVectors(); i-- > 0;) { + llvm::dbgs() << "\t\t\n"; + for (size_t lane = 0; lane < node->numLanes(); ++lane) { + auto* operation = node->getOperation(lane, i); + llvm::dbgs() << "\t\t\t"; + if (lane < node->numLanes() - 1) { + llvm::dbgs() << ""; + } + llvm::dbgs() << "\n"; + } + llvm::dbgs() << "\t\t\n"; + } + llvm::dbgs() << "\t
"; + llvm::dbgs() << "" << operation->getName() << " (" << operation << ")"; + llvm::dbgs() << "
\n"; + llvm::dbgs() << ">];\n"; + for (auto const& operand : entry.second) { + llvm::dbgs() << "node_" << entry.first.get() << "->" << "node_" << operand.get() << ";\n"; + } + } + llvm::dbgs() << "}\n"; +} \ No newline at end of file diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp index ca2f9eb5..da102161 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp @@ -41,6 +41,7 @@ void mlir::spn::SLPVectorizationPass::runOnOperation() { assert(!seeds.empty() && "couldn't find a seed!"); slp::SLPGraph graph(seeds.front(), 3); + graph.dump(); std::vector> vectors; for (auto const& node_ptr : graph.getNodes()) { for (size_t i = 0; i < node_ptr->numVectors(); ++i) { From 937d776b7110a30ae129784dae3884f79a193ae6 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Mon, 22 Mar 2021 02:03:00 +0100 Subject: [PATCH 052/385] SLP graph & node refactoring. --- .../LoSPNtoCPU/Vectorization/SLP/SLPGraph.h | 118 +-------- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.h | 9 +- .../Vectorization/SLP/SLPVectorizationPass.h | 4 + .../LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp | 240 ++++++++++++------ .../LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp | 29 +++ .../SLP/SLPVectorizationPass.cpp | 24 +- 6 files changed, 225 insertions(+), 199 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h index 100ddc36..9eff0e9a 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h @@ -21,21 +21,19 @@ namespace mlir { class SLPGraph { - typedef std::shared_ptr node_t; - public: SLPGraph(seed_t const& seed, size_t const& maxLookAhead); - std::vector> getNodes() const; + SLPNode& getRoot(); void dump() const; private: - void buildGraph(std::vector const& operations, node_t const& currentNode); + void buildGraph(std::vector const& operations, SLPNode& currentNode); - void reorderOperands(node_t const& multinode); + void reorderOperands(SLPNode const& multinode); std::pair getBest(Mode const& mode, Operation* last, @@ -43,116 +41,10 @@ namespace mlir { int getLookAheadScore(Operation* last, Operation* candidate, size_t const& maxLevel) const; - static bool areConsecutive(Operation* op1, Operation* op2) { - auto load1 = dyn_cast(op1); - auto load2 = dyn_cast(op2); - if (!load1 || !load2) { - return false; - } - if (load1.indices().size() != load2.indices().size()) { - return false; - } - for (size_t i = 0; i < load1.indices().size(); ++i) { - auto const1 = load1.indices()[i].getDefiningOp(); - auto const2 = load2.indices()[i].getDefiningOp(); - if (!const1 || !const2) { - return false; - } - auto index1 = const1.value().dyn_cast(); - auto index2 = const2.value().dyn_cast(); - if (!index1 || !index2) { - return false; - } - if (i == load1.indices().size() - 1) { - return index1.getInt() == index2.getInt() - 1; - } else if (index1.getInt() != index2.getInt()) { - return false; - } - } - return false; - } - - /// Checks if the given operations are vectorizable. - /// \param operations The potentially vectorizable operations. - /// \return True if the operations can be vectorized, otherwise false. - static bool vectorizable(std::vector const& operations) { - for (size_t i = 0; i < operations.size(); ++i) { - auto* op = operations[i]; - if (!op->hasTrait() || (i > 0 && op->getName() != operations.front()->getName())) { - return false; - } - } - return true; - } - - static bool commutative(std::vector const& operations) { - return std::all_of(std::begin(operations), std::end(operations), [&](Operation* op) { - if (op->hasTrait()) { - return true; - } - return dyn_cast(op) || dyn_cast(op); - }); - } - - static bool escapesMultinode(Operation* operation) { - // TODO: check if some intermediate, temporary value of a multinode is used outside of it - assert(operation); - return false; - } - - static std::vector getOperands(Operation* operation) { - std::vector operands; - operands.reserve(operation->getNumOperands()); - for (auto operand : operation->getOperands()) { - operands.emplace_back(operand.getDefiningOp()); - } - return operands; - } - - static std::vector> getOperands(std::vector const& operations) { - std::vector> allOperands; - allOperands.reserve(operations.size()); - for (auto* operation : operations) { - allOperands.emplace_back(getOperands(operation)); - } - return allOperands; - } - - static std::vector> getOperandsVectorized(std::vector const& operations) { - for (auto* operation : operations) { - assert(operation->getNumOperands() == operations.front()->getNumOperands() - && "operations must have same numbers of operands"); - } - std::vector> allOperands; - for (size_t i = 0; i < operations.front()->getNumOperands(); ++i) { - std::vector operands; - operands.reserve(operations.size()); - for (auto* operation : operations) { - operands.emplace_back(operation->getOperand(i).getDefiningOp()); - } - allOperands.emplace_back(operands); - } - return allOperands; - } - - static void sortByOpcode(std::vector& operations, - Optional const& smallestOpcode) { - std::sort(std::begin(operations), std::end(operations), [&](Operation* lhs, Operation* rhs) { - if (smallestOpcode.hasValue()) { - if (lhs->getName() == smallestOpcode.getValue()) { - return rhs->getName() != smallestOpcode.getValue(); - } else if (rhs->getName() == smallestOpcode.getValue()) { - return false; - } - } - return lhs->getName().getStringRef() < rhs->getName().getStringRef(); - }); - } - - std::map> operandsOf; - size_t const maxLookAhead; + SLPNode root; + }; } } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h index e8019a52..0b66c84a 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h @@ -18,7 +18,6 @@ namespace mlir { public: - SLPNode() = delete; explicit SLPNode(std::vector const& operations); void addOperationToLane(Operation* operation, size_t const& lane); @@ -26,6 +25,7 @@ namespace mlir { void setOperation(size_t lane, size_t index, Operation* operation); bool isMultiNode() const; + bool isUniform() const; bool areRootOfNode(std::vector const& operations) const; size_t numLanes() const; @@ -33,6 +33,11 @@ namespace mlir { std::vector getVector(size_t index) const; + SLPNode& addOperand(std::vector const& operations); + SLPNode& getOperand(size_t index) const; + std::vector getOperands() const; + size_t numOperands() const; + friend bool operator==(SLPNode const& lhs, SLPNode const& rhs) { return std::tie(lhs.lanes) == std::tie(rhs.lanes); } @@ -48,6 +53,8 @@ namespace mlir { /// ones in the source code. std::vector> lanes; + std::vector> operandNodes; + }; } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h index 414a8455..30f8bfdc 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h @@ -8,6 +8,7 @@ #include "mlir/Pass/Pass.h" #include "mlir/Dialect/StandardOps/IR/Ops.h" +#include "SLPGraph.h" namespace mlir { namespace spn { @@ -23,6 +24,9 @@ namespace mlir { private: + void transform(slp::SLPGraph& graph); + Operation* transform(slp::SLPNode& node, bool isRoot); + /// For debugging purposes. static void printSPN(std::vector const& operations) { std::vector nodes; diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp index efc448bc..fb88a8de 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp @@ -5,34 +5,169 @@ #include "LoSPNtoCPU/Vectorization/SLP/SLPGraph.h" #include +#include using namespace mlir; using namespace mlir::spn; using namespace mlir::spn::slp; -SLPGraph::SLPGraph(seed_t const& seed, size_t const& maxLookAhead) : operandsOf{}, maxLookAhead{maxLookAhead} { - auto const& currentNode = std::make_shared(seed); - operandsOf[currentNode] = {}; - buildGraph(seed, currentNode); +SLPGraph::SLPGraph(seed_t const& seed, size_t const& maxLookAhead) : maxLookAhead{maxLookAhead}, root{seed} { + buildGraph(seed, root); } -std::vector> SLPGraph::getNodes() const { - std::vector> nodes; - for (auto const& entry : operandsOf) { - nodes.emplace_back(entry.first); - for (auto const& operand : entry.second) { - if (std::find(std::begin(nodes), std::end(nodes), operand) == std::end(nodes)) { - nodes.emplace_back(operand); +SLPNode& SLPGraph::getRoot() { + return root; +} + +void SLPGraph::dump() const { + + llvm::dbgs() << "digraph debug_graph {\n"; + llvm::dbgs() << "rankdir = BT;\n"; + llvm::dbgs() << "node[shape=box];\n"; + + std::queue worklist; + worklist.emplace(&root); + + while (!worklist.empty()) { + auto const* node = worklist.front(); + worklist.pop(); + + llvm::dbgs() << "node_" << node << "[label=<\n"; + llvm::dbgs() << "\t\n"; + for (size_t i = node->numVectors(); i-- > 0;) { + llvm::dbgs() << "\t\t\n"; + for (size_t lane = 0; lane < node->numLanes(); ++lane) { + auto* operation = node->getOperation(lane, i); + llvm::dbgs() << "\t\t\t"; + if (lane < node->numLanes() - 1) { + llvm::dbgs() << ""; + } + llvm::dbgs() << "\n"; } + llvm::dbgs() << "\t\t\n"; + } + llvm::dbgs() << "\t
"; + llvm::dbgs() << "" << operation->getName() << " (" << operation << ")"; + llvm::dbgs() << "
\n"; + llvm::dbgs() << ">];\n"; + + for (auto const& operand : node->getOperands()) { + llvm::dbgs() << "node_" << node << "->" << "node_" << operand << ";\n"; + worklist.emplace(operand); } } - return nodes; + llvm::dbgs() << "}\n"; } -void SLPGraph::buildGraph(std::vector const& operations, node_t const& currentNode) { - if (std::intptr_t(operations.front()) == 0x5ebd28) { - llvm::dbgs(); +// Some helper functions in an anonymous namespace. +namespace { + + bool areConsecutive(Operation* op1, Operation* op2) { + auto load1 = dyn_cast(op1); + auto load2 = dyn_cast(op2); + if (!load1 || !load2) { + return false; + } + if (load1.indices().size() != load2.indices().size()) { + return false; + } + for (size_t i = 0; i < load1.indices().size(); ++i) { + auto const1 = load1.indices()[i].getDefiningOp(); + auto const2 = load2.indices()[i].getDefiningOp(); + if (!const1 || !const2) { + return false; + } + auto index1 = const1.value().dyn_cast(); + auto index2 = const2.value().dyn_cast(); + if (!index1 || !index2) { + return false; + } + if (i == load1.indices().size() - 1) { + return index1.getInt() == index2.getInt() - 1; + } else if (index1.getInt() != index2.getInt()) { + return false; + } + } + return false; + } + + bool vectorizable(std::vector const& operations) { + for (size_t i = 0; i < operations.size(); ++i) { + auto* op = operations[i]; + if (!op->hasTrait() || (i > 0 && op->getName() != operations.front()->getName())) { + return false; + } + } + return true; } + + bool commutative(std::vector const& operations) { + return std::all_of(std::begin(operations), std::end(operations), [&](Operation* op) { + if (op->hasTrait()) { + return true; + } + return dyn_cast(op) || dyn_cast(op); + }); + } + + bool escapesMultinode(Operation* operation) { + // TODO: check if some intermediate, temporary value of a multinode is used outside of it + assert(operation); + return false; + } + + std::vector getOperands(Operation* operation) { + std::vector operands; + operands.reserve(operation->getNumOperands()); + for (auto operand : operation->getOperands()) { + operands.emplace_back(operand.getDefiningOp()); + } + return operands; + } + + std::vector> getOperands(std::vector const& operations) { + std::vector> allOperands; + allOperands.reserve(operations.size()); + for (auto* operation : operations) { + allOperands.emplace_back(getOperands(operation)); + } + return allOperands; + } + + std::vector> getOperandsVectorized(std::vector const& operations) { + for (auto* operation : operations) { + assert(operation->getNumOperands() == operations.front()->getNumOperands() + && "operations must have same numbers of operands"); + } + std::vector> allOperands; + for (size_t i = 0; i < operations.front()->getNumOperands(); ++i) { + std::vector operands; + operands.reserve(operations.size()); + for (auto* operation : operations) { + operands.emplace_back(operation->getOperand(i).getDefiningOp()); + } + allOperands.emplace_back(operands); + } + return allOperands; + } + + void sortByOpcode(std::vector& operations, + Optional const& smallestOpcode) { + std::sort(std::begin(operations), std::end(operations), [&](Operation* lhs, Operation* rhs) { + if (smallestOpcode.hasValue()) { + if (lhs->getName() == smallestOpcode.getValue()) { + return rhs->getName() != smallestOpcode.getValue(); + } else if (rhs->getName() == smallestOpcode.getValue()) { + return false; + } + } + return lhs->getName().getStringRef() < rhs->getName().getStringRef(); + }); + } + +} + +void SLPGraph::buildGraph(std::vector const& operations, SLPNode& currentNode) { // Stop growing graph if (!vectorizable(operations)) { return; @@ -50,27 +185,25 @@ void SLPGraph::buildGraph(std::vector const& operations, node_t cons if (std::all_of(std::begin(allOperands), std::end(allOperands), [&](auto const& operandOperations) { return operandOperations[i]->getName() == currentOpCode && !escapesMultinode(operandOperations[i]); })) { - for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { - currentNode->addOperationToLane(allOperands[lane][i], lane); + for (size_t lane = 0; lane < currentNode.numLanes(); ++lane) { + currentNode.addOperationToLane(allOperands[lane][i], lane); } - buildGraph(currentNode->getVector(currentNode->numVectors() - 1), currentNode); + buildGraph(currentNode.getVector(currentNode.numVectors() - 1), currentNode); } else { // TODO: here might be a good place to implement variable vector width std::vector operandOperations; - for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { + for (size_t lane = 0; lane < currentNode.numLanes(); ++lane) { operandOperations.emplace_back(allOperands[lane][i]); } - auto const& operandNode = std::make_shared(operandOperations); - operandsOf[currentNode].emplace_back(operandNode); - operandsOf[operandNode] = {}; + currentNode.addOperand(operandOperations); } } // B. Normal Mode: Finished building multi-node - if (currentNode->isMultiNode() && currentNode->areRootOfNode(operations)) { + if (currentNode.isMultiNode() && currentNode.areRootOfNode(operations)) { reorderOperands(currentNode); - for (auto& operandNode : operandsOf.at(currentNode)) { - buildGraph(operandNode->getVector(operandNode->numVectors() - 1), operandNode); + for (auto* operandNode : currentNode.getOperands()) { + buildGraph(operandNode->getVector(operandNode->numVectors() - 1), *operandNode); } } } @@ -85,30 +218,26 @@ void SLPGraph::buildGraph(std::vector const& operations, node_t cons } } for (auto const& operandVector : getOperandsVectorized(operations)) { - auto const& operandNode = std::make_shared(operandVector); - operandsOf[currentNode].emplace_back(operandNode); - operandsOf[operandNode] = {}; - buildGraph(operandVector, operandNode); + buildGraph(operandVector, currentNode.addOperand(operandVector)); } } } -void SLPGraph::reorderOperands(node_t const& multinode) { - assert(multinode->isMultiNode()); - auto const& numOperands = operandsOf.at(multinode).size(); - std::vector> finalOrder{multinode->numLanes()}; - std::vector> mode{multinode->numLanes()}; +void SLPGraph::reorderOperands(SLPNode const& multinode) { + auto const& numOperands = multinode.numOperands(); + std::vector> finalOrder{multinode.numLanes()}; + std::vector> mode{multinode.numLanes()}; // 1. Strip first lane for (size_t i = 0; i < numOperands; ++i) { - auto operation = operandsOf.at(multinode)[i]->getOperation(0, 0); + auto operation = multinode.getOperand(i).getOperation(0, 0); finalOrder[0].emplace_back(operation); mode[0].emplace_back(modeFromOperation(operation)); } // 2. For all other lanes, find best candidate - for (size_t lane = 1; lane < multinode->numLanes(); ++lane) { + for (size_t lane = 1; lane < multinode.numLanes(); ++lane) { std::vector candidates; - for (auto const& operand : operandsOf.at(multinode)) { + for (auto const& operand : multinode.getOperands()) { candidates.emplace_back(operand->getOperation(lane, 0)); } // Look for a matching candidate @@ -144,9 +273,9 @@ void SLPGraph::reorderOperands(node_t const& multinode) { } } } - for (size_t operandIndex = 0; operandIndex < operandsOf.at(multinode).size(); ++operandIndex) { - for (size_t lane = 0; lane < multinode->numLanes(); ++lane) { - operandsOf.at(multinode)[operandIndex]->setOperation(lane, 0, finalOrder[lane][operandIndex]); + for (size_t operandIndex = 0; operandIndex < multinode.numOperands(); ++operandIndex) { + for (size_t lane = 0; lane < multinode.numLanes(); ++lane) { + multinode.getOperand(operandIndex).setOperation(lane, 0, finalOrder[lane][operandIndex]); } } } @@ -247,34 +376,3 @@ int SLPGraph::getLookAheadScore(Operation* last, Operation* candidate, size_t co } return scoreSum; } - -void SLPGraph::dump() const { - llvm::dbgs() << "digraph debug_graph {\n"; - llvm::dbgs() << "rankdir = BT;\n"; - llvm::dbgs() << "node[shape=box];\n"; - for (auto const& entry : operandsOf) { - auto const* node = entry.first.get(); - llvm::dbgs() << "node_" << node << "[label=<\n"; - llvm::dbgs() << "\t\n"; - for (size_t i = node->numVectors(); i-- > 0;) { - llvm::dbgs() << "\t\t\n"; - for (size_t lane = 0; lane < node->numLanes(); ++lane) { - auto* operation = node->getOperation(lane, i); - llvm::dbgs() << "\t\t\t"; - if (lane < node->numLanes() - 1) { - llvm::dbgs() << ""; - } - llvm::dbgs() << "\n"; - } - llvm::dbgs() << "\t\t\n"; - } - llvm::dbgs() << "\t
"; - llvm::dbgs() << "" << operation->getName() << " (" << operation << ")"; - llvm::dbgs() << "
\n"; - llvm::dbgs() << ">];\n"; - for (auto const& operand : entry.second) { - llvm::dbgs() << "node_" << entry.first.get() << "->" << "node_" << operand.get() << ";\n"; - } - } - llvm::dbgs() << "}\n"; -} \ No newline at end of file diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp index b9328bdd..1ee78aaf 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp @@ -33,6 +33,12 @@ bool SLPNode::isMultiNode() const { return lanes.front().size() > 1; } +bool SLPNode::isUniform() const { + return std::all_of(std::begin(lanes), std::end(lanes), [&](auto const& operations) { + return operations[0]->getName() == lanes[0][0]->getName(); + }); +} + bool SLPNode::areRootOfNode(std::vector const& operations) const { for (size_t lane = 0; lane < numLanes(); ++lane) { if (operations[lane] != lanes[lane].front()) { @@ -58,3 +64,26 @@ std::vector SLPNode::getVector(size_t index) const { } return vector; } + +SLPNode& SLPNode::addOperand(std::vector const& operations) { + operandNodes.emplace_back(std::make_unique(operations)); + return *operandNodes.back(); +} + +SLPNode& SLPNode::getOperand(size_t index) const { + assert(index <= operandNodes.size()); + return *operandNodes[index]; +} + +std::vector SLPNode::getOperands() const { + std::vector operands; + operands.reserve(operands.size()); + for (auto const& operand : operandNodes) { + operands.emplace_back(operand.get()); + } + return operands; +} + +size_t SLPNode::numOperands() const { + return operandNodes.size(); +} diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp index da102161..721dc1fd 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp @@ -41,22 +41,18 @@ void mlir::spn::SLPVectorizationPass::runOnOperation() { assert(!seeds.empty() && "couldn't find a seed!"); slp::SLPGraph graph(seeds.front(), 3); - graph.dump(); - std::vector> vectors; - for (auto const& node_ptr : graph.getNodes()) { - for (size_t i = 0; i < node_ptr->numVectors(); ++i) { - vectors.emplace_back(node_ptr->getVector(i)); - } - } - /* - OwningRewritePatternList patterns; - slp::populateVectorizationPatterns(patterns, &getContext(), vectors); - auto op = getOperation(); - FrozenRewritePatternList frozenPatterns(std::move(patterns)); - applyPatternsAndFoldGreedily(op, frozenPatterns); - */ + transform(graph); + +} +void mlir::spn::SLPVectorizationPass::transform(slp::SLPGraph& graph) { + graph.dump(); + transform(graph.getRoot(), true); +} +mlir::Operation* mlir::spn::SLPVectorizationPass::transform(slp::SLPNode& node, bool isRoot) { + bool isMixed = false; + return node.getVector(0).front(); } std::unique_ptr mlir::spn::createSLPVectorizationPass() { From 36319424d77e33a30b1258987cdce27cf9f35a3e Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Mon, 22 Mar 2021 23:18:58 +0100 Subject: [PATCH 053/385] Some refactoring. --- .../LoSPNtoCPU/Vectorization/SLP/SLPGraph.h | 36 +++--- .../LoSPNtoCPU/Vectorization/SLP/SLPMode.h | 30 +++-- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.h | 63 +++++----- .../LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h | 43 ++++--- .../Vectorization/SLP/SLPVectorizationPass.h | 66 +--------- .../SLP/SLPVectorizationPatterns.h | 53 ++------ .../LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp | 5 +- .../LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp | 3 +- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp | 1 - .../Vectorization/SLP/SLPSeeding.cpp | 3 +- .../SLP/SLPVectorizationPass.cpp | 117 ++++++++++++++++-- .../SLP/SLPVectorizationPatterns.cpp | 74 ++--------- 12 files changed, 212 insertions(+), 282 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h index 9eff0e9a..6b3256fe 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h @@ -7,46 +7,40 @@ #define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPGRAPH_H #include "mlir/IR/Operation.h" -#include "mlir/Dialect/StandardOps/IR/Ops.h" -#include "LoSPN/LoSPNTraits.h" #include "SLPMode.h" #include "SLPNode.h" #include "SLPSeeding.h" -#include - namespace mlir { namespace spn { - namespace slp { - class SLPGraph { + class SLPGraph { - public: + public: - SLPGraph(seed_t const& seed, size_t const& maxLookAhead); + SLPGraph(seed_t const& seed, size_t const& maxLookAhead); - SLPNode& getRoot(); + SLPNode& getRoot(); - void dump() const; + void dump() const; - private: + private: - void buildGraph(std::vector const& operations, SLPNode& currentNode); + void buildGraph(std::vector const& operations, SLPNode& currentNode); - void reorderOperands(SLPNode const& multinode); + void reorderOperands(SLPNode const& multinode); - std::pair getBest(Mode const& mode, - Operation* last, - std::vector& candidates) const; + std::pair getBest(Mode const& mode, + Operation* last, + std::vector& candidates) const; - int getLookAheadScore(Operation* last, Operation* candidate, size_t const& maxLevel) const; + int getLookAheadScore(Operation* last, Operation* candidate, size_t const& maxLevel) const; - size_t const maxLookAhead; + size_t const maxLookAhead; - SLPNode root; + SLPNode root; - }; - } + }; } } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.h index c829b394..1afa8aeb 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.h @@ -6,28 +6,26 @@ #ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPMODE_H #define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPMODE_H -#include "LoSPN/LoSPNOps.h" +#include "mlir/IR/Operation.h" namespace mlir { namespace spn { - namespace slp { - enum Mode { - // look for a constant - CONST, - // look for a consecutive load to that in the previous lane - LOAD, - // look for an operation of the same opcode - OPCODE, - // look for the exact same operation - SPLAT, - // vectorization has failed, give higher priority to others - FAILED - }; + enum Mode { + // look for a constant + CONST, + // look for a consecutive load to that in the previous lane + LOAD, + // look for an operation of the same opcode + OPCODE, + // look for the exact same operation + SPLAT, + // vectorization has failed, give higher priority to others + FAILED + }; - Mode modeFromOperation(Operation const* operation); + Mode modeFromOperation(Operation const* operation); - } } } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h index 0b66c84a..ee7b8f59 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h @@ -7,57 +7,52 @@ #define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPNODE_H #include "mlir/IR/Operation.h" -#include "mlir/IR/OpDefinition.h" -#include "llvm/ADT/SmallVector.h" namespace mlir { namespace spn { - namespace slp { - class SLPNode { + class SLPNode { - public: + public: - explicit SLPNode(std::vector const& operations); + explicit SLPNode(std::vector const& operations); - void addOperationToLane(Operation* operation, size_t const& lane); - Operation* getOperation(size_t lane, size_t index) const; - void setOperation(size_t lane, size_t index, Operation* operation); + void addOperationToLane(Operation* operation, size_t const& lane); + Operation* getOperation(size_t lane, size_t index) const; + void setOperation(size_t lane, size_t index, Operation* operation); - bool isMultiNode() const; - bool isUniform() const; - bool areRootOfNode(std::vector const& operations) const; + bool isMultiNode() const; + bool isUniform() const; + bool areRootOfNode(std::vector const& operations) const; - size_t numLanes() const; - size_t numVectors() const; + size_t numLanes() const; + size_t numVectors() const; - std::vector getVector(size_t index) const; + std::vector getVector(size_t index) const; - SLPNode& addOperand(std::vector const& operations); - SLPNode& getOperand(size_t index) const; - std::vector getOperands() const; - size_t numOperands() const; + SLPNode& addOperand(std::vector const& operations); + SLPNode& getOperand(size_t index) const; + std::vector getOperands() const; + size_t numOperands() const; - friend bool operator==(SLPNode const& lhs, SLPNode const& rhs) { - return std::tie(lhs.lanes) == std::tie(rhs.lanes); - } + friend bool operator==(SLPNode const& lhs, SLPNode const& rhs) { + return std::tie(lhs.lanes) == std::tie(rhs.lanes); + } - friend bool operator!=(SLPNode const& lhs, SLPNode const& rhs) { - return !(lhs == rhs); - } + friend bool operator!=(SLPNode const& lhs, SLPNode const& rhs) { + return !(lhs == rhs); + } - private: + private: - /// Stores lanes as lists of operations. An inner vector (i.e. a lane) only contains more than one operation - /// if this node is a multinode. Operations with smaller indices inside a lane are executed "after" the higher - /// ones in the source code. - std::vector> lanes; + /// Stores lanes as lists of operations. An inner vector (i.e. a lane) only contains more than one operation + /// if this node is a multinode. Operations with smaller indices inside a lane are executed "after" the higher + /// ones in the source code. + std::vector> lanes; - std::vector> operandNodes; + std::vector> operandNodes; - }; - - } + }; } } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h index 43d89569..7e53a526 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h @@ -6,41 +6,40 @@ #ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPSEEDING_H #define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPSEEDING_H -#include "LoSPN/LoSPNOps.h" +#include "mlir/IR/Operation.h" +#include namespace mlir { namespace spn { - namespace slp { - typedef std::vector seed_t; + typedef std::vector seed_t; - enum SearchMode { - /// - DefBeforeUse, - /// - UseBeforeDef, - /// TODO? - Chain - }; + enum SearchMode { + /// + DefBeforeUse, + /// + UseBeforeDef, + /// TODO? + Chain + }; - class SeedAnalysis { + class SeedAnalysis { - public: + public: - explicit SeedAnalysis(Operation* rootOp); + explicit SeedAnalysis(Operation* rootOp); - std::map getOpDepths() const; + std::map getOpDepths() const; - std::vector getSeeds(size_t const& op, - std::map const& depthsOf, - SearchMode const& mode) const; + std::vector getSeeds(size_t const& op, + std::map const& depthsOf, + SearchMode const& mode) const; - private: + private: - Operation* rootOp; + Operation* rootOp; - }; - } + }; } } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h index 30f8bfdc..68dbd57a 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h @@ -6,8 +6,8 @@ #ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPVECTORIZATIONPASS_H #define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPVECTORIZATIONPASS_H +#include "mlir/IR/Operation.h" #include "mlir/Pass/Pass.h" -#include "mlir/Dialect/StandardOps/IR/Ops.h" #include "SLPGraph.h" namespace mlir { @@ -17,73 +17,15 @@ namespace mlir { public: - explicit SLPVectorizationPass() = default; - protected: void runOnOperation() override; private: - void transform(slp::SLPGraph& graph); - Operation* transform(slp::SLPNode& node, bool isRoot); - - /// For debugging purposes. - static void printSPN(std::vector const& operations) { - std::vector nodes; - std::vector> edges; - - std::stack worklist; - for (auto& op : operations) { - worklist.push(op); - } - - while (!worklist.empty()) { - - auto op = worklist.top(); - worklist.pop(); - - if (std::find(std::begin(nodes), std::end(nodes), op) == nodes.end()) { - nodes.emplace_back(op); - for (size_t i = 0; i < op->getNumOperands(); ++i) { - auto const& operand = op->getOperand(i); - if (operand.getDefiningOp() != nullptr) { - edges.emplace_back(std::make_tuple(op, operand.getDefiningOp(), i)); - worklist.push(operand.getDefiningOp()); - } - } - } - } - - llvm::dbgs() << "digraph debug_graph {\n"; - llvm::dbgs() << "rankdir = BT;\n"; - llvm::dbgs() << "node[shape=box];\n"; - for (auto& op : nodes) { - printOperation(op); - } - for (auto& edge : edges) { - printEdge(std::get<0>(edge), std::get<1>(edge), std::get<2>(edge)); - } - llvm::dbgs() << "}\n"; - } - - /// For debugging purposes. - static void printOperation(Operation* op) { - llvm::dbgs() << "node_" << op << "[label=\"" << op->getName().getStringRef() << "\\n" << op; - if (auto constantOp = dyn_cast(op)) { - if (constantOp.value().getType().isIntOrIndex()) { - llvm::dbgs() << "\\nvalue: " << std::to_string(constantOp.value().dyn_cast().getInt()); - } else if (constantOp.value().getType().isIntOrFloat()) { - llvm::dbgs() << "\\nvalue: " - << std::to_string(constantOp.value().dyn_cast().getValueAsDouble()); - } - } - llvm::dbgs() << "\", fillcolor=\"#a0522d\"];\n"; - } + void transform(SLPGraph& graph); + Operation* transform(SLPNode& node, bool isRoot); - /// For debugging purposes. - static void printEdge(Operation* src, Operation* dst, size_t index) { - llvm::dbgs() << "node_" << src << " -> node_" << dst << "[label=\"" << std::to_string(index) << "\"];\n"; - } + std::map>> extractOps; }; diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h index dc439242..7f8f77e9 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h @@ -6,60 +6,23 @@ #ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPVECTORIZATIONPATTERNS_H #define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPVECTORIZATIONPATTERNS_H -#include - #include "mlir/Transforms/DialectConversion.h" -#include "mlir/Dialect/StandardOps/IR/Ops.h" -#include "LoSPN/LoSPNOps.h" -#include "LoSPN/LoSPNDialect.h" +#include "LoSPNtoCPU/Vectorization/SLP/SLPGraph.h" namespace mlir { namespace spn { - namespace low { - namespace slp { - - template - class VectorizationPattern : public mlir::OpRewritePattern { - public: - VectorizationPattern(MLIRContext* context, std::vector> const& vectors) - : OpRewritePattern(context, 1), vectors{vectors} { - for (size_t i = 0; i < vectors.size(); ++i) { - for (auto* op : vectors[i]) { - vectorIndices[op].emplace_back(i); - } - } - } - protected: - - bool isAssignedToVector(Operation* op) const { - return vectorIndices.count(op); - } - - bool isVectorMixed(std::vector vector) const { - return std::any_of(std::begin(vector), std::end(vector), [&](Operation* op) { - return op->getName() != vector.front()->getName(); - }); - } - - std::map> vectorIndices; - std::vector> const& vectors; - }; - /// Pattern for transforming SPN sum ops to a vectorized version. - struct SumOpVectorization : public VectorizationPattern { + struct VectorizeTask : OpRewritePattern { - using VectorizationPattern::VectorizationPattern; + using OpRewritePattern::OpRewritePattern; - LogicalResult matchAndRewrite(SPNAdd op, PatternRewriter& rewriter) const override; + explicit VectorizeTask(MLIRContext* context, SLPGraph& graph); - }; + LogicalResult matchAndRewrite(FuncOp op, PatternRewriter& rewriter) const override; - static void populateVectorizationPatterns(OwningRewritePatternList& patterns, MLIRContext* context, - std::vector> const& vectors) { - patterns.insert(context, vectors); - } - } - } + private: + SLPGraph& graph; + }; } } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp index fb88a8de..e9bba80f 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp @@ -3,13 +3,14 @@ // Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. // +#include "mlir/Dialect/StandardOps/IR/Ops.h" +#include "llvm/Support/Debug.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPGraph.h" #include #include using namespace mlir; using namespace mlir::spn; -using namespace mlir::spn::slp; SLPGraph::SLPGraph(seed_t const& seed, size_t const& maxLookAhead) : maxLookAhead{maxLookAhead}, root{seed} { buildGraph(seed, root); @@ -90,7 +91,7 @@ namespace { } return false; } - + bool vectorizable(std::vector const& operations) { for (size_t i = 0; i < operations.size(); ++i) { auto* op = operations[i]; diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp index 4263d620..6bf645fa 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp @@ -8,9 +8,8 @@ using namespace mlir; using namespace mlir::spn; -using namespace mlir::spn::slp; -Mode slp::modeFromOperation(const Operation* operation) { +Mode mlir::spn::modeFromOperation(const Operation* operation) { if (dyn_cast(operation)) { return CONST; } else if (dyn_cast(operation)) { diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp index 1ee78aaf..d23c482a 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp @@ -7,7 +7,6 @@ using namespace mlir; using namespace mlir::spn; -using namespace mlir::spn::slp; SLPNode::SLPNode(std::vector const& operations) : lanes{operations.size()} { for (size_t i = 0; i < operations.size(); ++i) { diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp index 3706fb1c..f5131211 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp @@ -4,10 +4,11 @@ // #include "LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h" +#include "llvm/ADT/StringMap.h" +#include "mlir/Dialect/Traits.h" using namespace mlir; using namespace mlir::spn; -using namespace mlir::spn::slp; SeedAnalysis::SeedAnalysis(Operation* rootOp) : rootOp{rootOp} {} diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp index 721dc1fd..2ddc07cd 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp @@ -3,16 +3,76 @@ // Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. // +#include "LoSPN/LoSPNDialect.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h" #include "LoSPNtoCPU/Vectorization/TargetInformation.h" -#include "LoSPNtoCPU/LoSPNtoCPUTypeConverter.h" -#include "LoSPNtoCPU/StructurePatterns.h" -#include "LoSPNtoCPU/NodePatterns.h" +#include "LoSPNtoCPU/Vectorization/LoSPNVectorizationTypeConverter.h" #include "mlir/Dialect/SCF/SCF.h" #include "mlir/Dialect/Vector/VectorOps.h" -#include "mlir/Dialect/Linalg/IR/LinalgOps.h" -#include "LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h" -#include "LoSPNtoCPU/Vectorization/SLP/SLPGraph.h" +#include "mlir/Dialect/StandardOps/IR/Ops.h" +#include "mlir/Transforms/DialectConversion.h" + +// Anonymous namespace for helper functions. +namespace { + + /// For debugging purposes. + void printOperation(mlir::Operation* op) { + llvm::dbgs() << "node_" << op << "[label=\"" << op->getName().getStringRef() << "\\n" << op; + if (auto constantOp = llvm::dyn_cast(op)) { + if (constantOp.value().getType().isIntOrIndex()) { + llvm::dbgs() << "\\nvalue: " << std::to_string(constantOp.value().dyn_cast().getInt()); + } else if (constantOp.value().getType().isIntOrFloat()) { + llvm::dbgs() << "\\nvalue: " + << std::to_string(constantOp.value().dyn_cast().getValueAsDouble()); + } + } + llvm::dbgs() << "\", fillcolor=\"#a0522d\"];\n"; + } + + /// For debugging purposes. + void printEdge(mlir::Operation* src, mlir::Operation* dst, size_t index) { + llvm::dbgs() << "node_" << src << " -> node_" << dst << "[label=\"" << std::to_string(index) << "\"];\n"; + } + + /// For debugging purposes. + void printSeedTree(std::vector const& operations) { + std::vector nodes; + std::vector> edges; + + std::stack worklist; + for (auto& op : operations) { + worklist.push(op); + } + + while (!worklist.empty()) { + + auto op = worklist.top(); + worklist.pop(); + + if (std::find(std::begin(nodes), std::end(nodes), op) == nodes.end()) { + nodes.emplace_back(op); + for (size_t i = 0; i < op->getNumOperands(); ++i) { + auto const& operand = op->getOperand(i); + if (operand.getDefiningOp() != nullptr) { + edges.emplace_back(std::make_tuple(op, operand.getDefiningOp(), i)); + worklist.push(operand.getDefiningOp()); + } + } + } + } + + llvm::dbgs() << "digraph debug_graph {\n"; + llvm::dbgs() << "rankdir = BT;\n"; + llvm::dbgs() << "node[shape=box];\n"; + for (auto& op : nodes) { + printOperation(op); + } + for (auto& edge : edges) { + printEdge(std::get<0>(edge), std::get<1>(edge), std::get<2>(edge)); + } + llvm::dbgs() << "}\n"; + } +} void mlir::spn::SLPVectorizationPass::runOnOperation() { llvm::StringRef funcName = getOperation().getName(); @@ -32,26 +92,59 @@ void mlir::spn::SLPVectorizationPass::runOnOperation() { auto function = getOperation(); - auto& seedAnalysis = getAnalysis(); + auto& seedAnalysis = getAnalysis(); auto computationType = function.getArguments().back().getType().dyn_cast().getElementType(); auto width = TargetInformation::nativeCPUTarget().getHWVectorEntries(computationType); - auto seeds = seedAnalysis.getSeeds(width, seedAnalysis.getOpDepths(), slp::UseBeforeDef); + auto seeds = seedAnalysis.getSeeds(width, seedAnalysis.getOpDepths(), SearchMode::UseBeforeDef); assert(!seeds.empty() && "couldn't find a seed!"); - slp::SLPGraph graph(seeds.front(), 3); + SLPGraph graph(seeds.front(), 3); transform(graph); } -void mlir::spn::SLPVectorizationPass::transform(slp::SLPGraph& graph) { +void mlir::spn::SLPVectorizationPass::transform(SLPGraph& graph) { graph.dump(); - transform(graph.getRoot(), true); + auto* rootOperation = transform(graph.getRoot(), true); + assert(false); + // Update users of vector operations that aren't contained in the graph. + for (auto const& entry : extractOps) { + for (auto const& extractOpData : entry.second) { + extractOpData.first->setOperand(extractOpData.second, nullptr); + } + } } -mlir::Operation* mlir::spn::SLPVectorizationPass::transform(slp::SLPNode& node, bool isRoot) { +mlir::Operation* mlir::spn::SLPVectorizationPass::transform(SLPNode& node, bool isRoot) { + + for (size_t vectorIndex = 0; vectorIndex < node.numVectors(); ++vectorIndex) { + if (node.isUniform()) { + + for (size_t lane = 0; lane < node.numLanes(); ++lane) { + auto* operation = node.getOperation(lane, vectorIndex); + + auto type = VectorType::get(node.numLanes(), operation->getResult(0).getType()); + auto operands = llvm::SmallVector{}; + auto* vectorOperation = Operation::create(operation->getLoc(), + operation->getName(), + type, + operands, + operation->getAttrs(), + operation->getSuccessors(), + operation->getNumRegions()); + + operation->dropAllUses(); + + } + } else { + + } + } bool isMixed = false; + assert(false); + extractOps[nullptr]; return node.getVector(0).front(); } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp index 278e5a3d..4b2c9527 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp @@ -8,74 +8,20 @@ #include "mlir/Dialect/Vector/VectorOps.h" #include "mlir/Dialect/Vector/VectorTransforms.h" -mlir::LogicalResult mlir::spn::low::slp::SumOpVectorization::matchAndRewrite(mlir::spn::low::SPNAdd op, - mlir::PatternRewriter& rewriter) const { +using namespace mlir; +using namespace mlir::spn; - if (!isAssignedToVector(op.getOperation())) { - return failure(); - } - - auto a = op.getOperation(); - - for (auto const& index : vectorIndices.at(op.getOperation())) { - auto const& vector = vectors[index]; - Type vectorType = VectorType::get(vector.size(), op.getType()); - Type memrefType = MemRefType::get(vector.size(), op.getType()); - ValueRange range; - rewriter.create(op.getLoc(), memrefType, range); - op.getOperation()->getBlock()->dump(); - } - /* - if (isVectorMixed(vector)) { - OpBuilder builder(op.getContext()); - builder.setInsertionPoint(op); - llvm::SmallVector values; - values.reserve(vector.size()); - for (size_t i = 0; i < vector.size(); ++i) { - values[i] = vector[i]->getResult(0); - } - //DenseElementsAttr::get(vectorType, llvm::ArrayRef{values}); - } - - for (size_t i = 0; i < n; ++i) { - indices[i] = dyn_cast(vector[i]).index(); - means[i] = dyn_cast(vector[i]).mean().convertToDouble(); - stddevs[i] = dyn_cast(vector[i]).stddev().convertToDouble(); - indices[i].dump(); - values[i] = dyn_cast(vector[i]).getOperand(); - } +LogicalResult VectorizeTask::matchAndRewrite(FuncOp op, + PatternRewriter& rewriter) const { - auto t = rewriter.create(op.getLoc(), - op.index(), - llvm::ArrayRef(means, n), - llvm::ArrayRef(stddevs, n)); - ValueRange valueRange{}; - rewriter.replaceOp(t.getOperation(), {}); - t.getOperation()->dump(); - for (size_t i = 0; i < vector.size(); ++i) { - values[i] = vector[i]->getResult(0); - } - //DenseElementsAttr::get(vectorType, llvm::ArrayRef{values}); - } - - for (size_t i = 0; i < n; ++i) { - indices[i] = dyn_cast(vector[i]).index(); - means[i] = dyn_cast(vector[i]).mean().convertToDouble(); - stddevs[i] = dyn_cast(vector[i]).stddev().convertToDouble(); - indices[i].dump(); - values[i] = dyn_cast(vector[i]).getOperand(); + if (!op->getName().getStringRef().contains("task_")) { + return failure(); } + //op->replaceUsesOfWith(); - x = x - 1; -} else { - -} -for (auto const& gaussianOp : vector) { - rewriter.eraseOp(gaussianOp); -} + return success(); } +VectorizeTask::VectorizeTask(MLIRContext* context, + SLPGraph& graph) : OpRewritePattern(context), graph{graph} { } - */ - return success(); -} \ No newline at end of file From 505f9574bb6259e1cc0a4d624fac3ee0be65ed32 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Mon, 22 Mar 2021 23:54:21 +0100 Subject: [PATCH 054/385] Namespace changes. --- .../LoSPNtoCPU/Vectorization/SLP/SLPGraph.h | 34 +++++----- .../LoSPNtoCPU/Vectorization/SLP/SLPMode.h | 30 +++++---- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.h | 62 ++++++++++--------- .../LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h | 42 +++++++------ .../Vectorization/SLP/SLPVectorizationPass.h | 23 ++++--- .../SLP/SLPVectorizationPatterns.h | 18 +++--- .../LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp | 2 +- .../LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp | 4 +- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp | 1 + .../Vectorization/SLP/SLPSeeding.cpp | 2 +- .../SLP/SLPVectorizationPass.cpp | 11 ++-- .../SLP/SLPVectorizationPatterns.cpp | 8 +-- 12 files changed, 130 insertions(+), 107 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h index 6b3256fe..088cf419 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h @@ -13,34 +13,38 @@ namespace mlir { namespace spn { + namespace low { + namespace slp { - class SLPGraph { + class SLPGraph { - public: + public: - SLPGraph(seed_t const& seed, size_t const& maxLookAhead); + SLPGraph(seed_t const& seed, size_t const& maxLookAhead); - SLPNode& getRoot(); + SLPNode& getRoot(); - void dump() const; + void dump() const; - private: + private: - void buildGraph(std::vector const& operations, SLPNode& currentNode); + void buildGraph(std::vector const& operations, SLPNode& currentNode); - void reorderOperands(SLPNode const& multinode); + void reorderOperands(SLPNode const& multinode); - std::pair getBest(Mode const& mode, - Operation* last, - std::vector& candidates) const; + std::pair getBest(Mode const& mode, + Operation* last, + std::vector& candidates) const; - int getLookAheadScore(Operation* last, Operation* candidate, size_t const& maxLevel) const; + int getLookAheadScore(Operation* last, Operation* candidate, size_t const& maxLevel) const; - size_t const maxLookAhead; + size_t const maxLookAhead; - SLPNode root; + SLPNode root; - }; + }; + } + } } } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.h index 1afa8aeb..421ecd5b 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.h @@ -10,22 +10,26 @@ namespace mlir { namespace spn { + namespace low { + namespace slp { - enum Mode { - // look for a constant - CONST, - // look for a consecutive load to that in the previous lane - LOAD, - // look for an operation of the same opcode - OPCODE, - // look for the exact same operation - SPLAT, - // vectorization has failed, give higher priority to others - FAILED - }; + enum Mode { + // look for a constant + CONST, + // look for a consecutive load to that in the previous lane + LOAD, + // look for an operation of the same opcode + OPCODE, + // look for the exact same operation + SPLAT, + // vectorization has failed, give higher priority to others + FAILED + }; - Mode modeFromOperation(Operation const* operation); + Mode modeFromOperation(Operation const* operation); + } + } } } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h index ee7b8f59..1ea7e546 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h @@ -10,49 +10,53 @@ namespace mlir { namespace spn { + namespace low { + namespace slp { - class SLPNode { + class SLPNode { - public: + public: - explicit SLPNode(std::vector const& operations); + explicit SLPNode(std::vector const& operations); - void addOperationToLane(Operation* operation, size_t const& lane); - Operation* getOperation(size_t lane, size_t index) const; - void setOperation(size_t lane, size_t index, Operation* operation); + void addOperationToLane(Operation* operation, size_t const& lane); + Operation* getOperation(size_t lane, size_t index) const; + void setOperation(size_t lane, size_t index, Operation* operation); - bool isMultiNode() const; - bool isUniform() const; - bool areRootOfNode(std::vector const& operations) const; + bool isMultiNode() const; + bool isUniform() const; + bool areRootOfNode(std::vector const& operations) const; - size_t numLanes() const; - size_t numVectors() const; + size_t numLanes() const; + size_t numVectors() const; - std::vector getVector(size_t index) const; + std::vector getVector(size_t index) const; - SLPNode& addOperand(std::vector const& operations); - SLPNode& getOperand(size_t index) const; - std::vector getOperands() const; - size_t numOperands() const; + SLPNode& addOperand(std::vector const& operations); + SLPNode& getOperand(size_t index) const; + std::vector getOperands() const; + size_t numOperands() const; - friend bool operator==(SLPNode const& lhs, SLPNode const& rhs) { - return std::tie(lhs.lanes) == std::tie(rhs.lanes); - } + friend bool operator==(SLPNode const& lhs, SLPNode const& rhs) { + return std::tie(lhs.lanes) == std::tie(rhs.lanes); + } - friend bool operator!=(SLPNode const& lhs, SLPNode const& rhs) { - return !(lhs == rhs); - } + friend bool operator!=(SLPNode const& lhs, SLPNode const& rhs) { + return !(lhs == rhs); + } - private: + private: - /// Stores lanes as lists of operations. An inner vector (i.e. a lane) only contains more than one operation - /// if this node is a multinode. Operations with smaller indices inside a lane are executed "after" the higher - /// ones in the source code. - std::vector> lanes; + /// Stores lanes as lists of operations. An inner vector (i.e. a lane) only contains more than one operation + /// if this node is a multinode. Operations with smaller indices inside a lane are executed "after" the higher + /// ones in the source code. + std::vector> lanes; - std::vector> operandNodes; + std::vector> operandNodes; - }; + }; + } + } } } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h index 7e53a526..ed5910b2 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h @@ -11,35 +11,39 @@ namespace mlir { namespace spn { + namespace low { + namespace slp { - typedef std::vector seed_t; + typedef std::vector seed_t; - enum SearchMode { - /// - DefBeforeUse, - /// - UseBeforeDef, - /// TODO? - Chain - }; + enum SearchMode { + /// + DefBeforeUse, + /// + UseBeforeDef, + /// TODO? + Chain + }; - class SeedAnalysis { + class SeedAnalysis { - public: + public: - explicit SeedAnalysis(Operation* rootOp); + explicit SeedAnalysis(Operation* rootOp); - std::map getOpDepths() const; + std::map getOpDepths() const; - std::vector getSeeds(size_t const& op, - std::map const& depthsOf, - SearchMode const& mode) const; + std::vector getSeeds(size_t const& op, + std::map const& depthsOf, + SearchMode const& mode) const; - private: + private: - Operation* rootOp; + Operation* rootOp; - }; + }; + } + } } } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h index 68dbd57a..33eb9c88 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h @@ -12,23 +12,26 @@ namespace mlir { namespace spn { + namespace low { + namespace slp { - struct SLPVectorizationPass : public PassWrapper> { + struct SLPVectorizationPass : public PassWrapper> { - public: + public: - protected: - void runOnOperation() override; + protected: + void runOnOperation() override; - private: + private: - void transform(SLPGraph& graph); - Operation* transform(SLPNode& node, bool isRoot); + void transform(SLPGraph& graph); + Operation* transform(SLPNode& node, bool isRoot); - std::map>> extractOps; - - }; + std::map>> extractOps; + }; + } + } std::unique_ptr createSLPVectorizationPass(); } } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h index 7f8f77e9..c3afecee 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h @@ -11,18 +11,22 @@ namespace mlir { namespace spn { + namespace low { + namespace slp { - struct VectorizeTask : OpRewritePattern { + struct VectorizeTask : OpRewritePattern { - using OpRewritePattern::OpRewritePattern; + using OpRewritePattern::OpRewritePattern; - explicit VectorizeTask(MLIRContext* context, SLPGraph& graph); + explicit VectorizeTask(MLIRContext* context, SLPGraph& graph); - LogicalResult matchAndRewrite(FuncOp op, PatternRewriter& rewriter) const override; + LogicalResult matchAndRewrite(FuncOp op, PatternRewriter& rewriter) const override; - private: - SLPGraph& graph; - }; + private: + SLPGraph& graph; + }; + } + } } } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp index e9bba80f..125cbc14 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp @@ -10,7 +10,7 @@ #include using namespace mlir; -using namespace mlir::spn; +using namespace mlir::spn::low::slp; SLPGraph::SLPGraph(seed_t const& seed, size_t const& maxLookAhead) : maxLookAhead{maxLookAhead}, root{seed} { buildGraph(seed, root); diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp index 6bf645fa..98db6588 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp @@ -7,9 +7,9 @@ #include "LoSPNtoCPU/Vectorization/SLP/SLPMode.h" using namespace mlir; -using namespace mlir::spn; +using namespace mlir::spn::low::slp; -Mode mlir::spn::modeFromOperation(const Operation* operation) { +Mode mlir::spn::low::slp::modeFromOperation(const Operation* operation) { if (dyn_cast(operation)) { return CONST; } else if (dyn_cast(operation)) { diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp index d23c482a..73032b2a 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp @@ -7,6 +7,7 @@ using namespace mlir; using namespace mlir::spn; +using namespace mlir::spn::low::slp; SLPNode::SLPNode(std::vector const& operations) : lanes{operations.size()} { for (size_t i = 0; i < operations.size(); ++i) { diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp index f5131211..6c685517 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp @@ -8,7 +8,7 @@ #include "mlir/Dialect/Traits.h" using namespace mlir; -using namespace mlir::spn; +using namespace mlir::spn::low::slp; SeedAnalysis::SeedAnalysis(Operation* rootOp) : rootOp{rootOp} {} diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp index 2ddc07cd..3625978c 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp @@ -6,6 +6,7 @@ #include "LoSPN/LoSPNDialect.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h" #include "LoSPNtoCPU/Vectorization/TargetInformation.h" +#include "LoSPNtoCPU/LoSPNtoCPUTypeConverter.h" #include "LoSPNtoCPU/Vectorization/LoSPNVectorizationTypeConverter.h" #include "mlir/Dialect/SCF/SCF.h" #include "mlir/Dialect/Vector/VectorOps.h" @@ -74,7 +75,7 @@ namespace { } } -void mlir::spn::SLPVectorizationPass::runOnOperation() { +void mlir::spn::low::slp::SLPVectorizationPass::runOnOperation() { llvm::StringRef funcName = getOperation().getName(); if (!funcName.contains("task_")) { return; @@ -105,7 +106,7 @@ void mlir::spn::SLPVectorizationPass::runOnOperation() { } -void mlir::spn::SLPVectorizationPass::transform(SLPGraph& graph) { +void mlir::spn::low::slp::SLPVectorizationPass::transform(SLPGraph& graph) { graph.dump(); auto* rootOperation = transform(graph.getRoot(), true); assert(false); @@ -117,8 +118,8 @@ void mlir::spn::SLPVectorizationPass::transform(SLPGraph& graph) { } } -mlir::Operation* mlir::spn::SLPVectorizationPass::transform(SLPNode& node, bool isRoot) { - +mlir::Operation* mlir::spn::low::slp::SLPVectorizationPass::transform(SLPNode& node, bool isRoot) { + LoSPNVectorizationTypeConverter typeConverter{4}; for (size_t vectorIndex = 0; vectorIndex < node.numVectors(); ++vectorIndex) { if (node.isUniform()) { @@ -149,6 +150,6 @@ mlir::Operation* mlir::spn::SLPVectorizationPass::transform(SLPNode& node, bool } std::unique_ptr mlir::spn::createSLPVectorizationPass() { - return std::make_unique(); + return std::make_unique(); } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp index 4b2c9527..b675697b 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp @@ -9,10 +9,9 @@ #include "mlir/Dialect/Vector/VectorTransforms.h" using namespace mlir; -using namespace mlir::spn; +using namespace mlir::spn::low::slp; -LogicalResult VectorizeTask::matchAndRewrite(FuncOp op, - PatternRewriter& rewriter) const { +LogicalResult VectorizeTask::matchAndRewrite(FuncOp op, PatternRewriter& rewriter) const { if (!op->getName().getStringRef().contains("task_")) { return failure(); @@ -21,7 +20,6 @@ LogicalResult VectorizeTask::matchAndRewrite(FuncOp op, return success(); } -VectorizeTask::VectorizeTask(MLIRContext* context, - SLPGraph& graph) : OpRewritePattern(context), graph{graph} { +VectorizeTask::VectorizeTask(MLIRContext* context, SLPGraph& graph) : OpRewritePattern(context), graph{graph} { } From 6e1b77acd9c3e0b3a303c80d2de60dd62b341f74 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Wed, 24 Mar 2021 10:40:42 +0100 Subject: [PATCH 055/385] Vectorization pass progress (SLPGraph dismantling). --- .../LoSPNtoCPU/Vectorization/SLP/SLPGraph.h | 2 +- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.h | 6 +- .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.h | 24 +++ .../Vectorization/SLP/SLPVectorizationPass.h | 13 +- .../SLP/SLPVectorizationPatterns.h | 33 ---- mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt | 2 +- .../LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp | 40 +---- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp | 19 ++- .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp | 57 +++++++ .../SLP/SLPVectorizationPass.cpp | 142 ++++++++++++------ .../SLP/SLPVectorizationPatterns.cpp | 25 --- 11 files changed, 223 insertions(+), 140 deletions(-) create mode 100644 mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h delete mode 100644 mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h create mode 100644 mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp delete mode 100644 mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h index 088cf419..1bad7555 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h @@ -22,7 +22,7 @@ namespace mlir { SLPGraph(seed_t const& seed, size_t const& maxLookAhead); - SLPNode& getRoot(); + SLPNode const& getRoot() const; void dump() const; diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h index 1ea7e546..1d19539d 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h @@ -28,8 +28,8 @@ namespace mlir { bool areRootOfNode(std::vector const& operations) const; size_t numLanes() const; - size_t numVectors() const; + size_t numVectors() const; std::vector getVector(size_t index) const; SLPNode& addOperand(std::vector const& operations); @@ -37,6 +37,10 @@ namespace mlir { std::vector getOperands() const; size_t numOperands() const; + Type getResultType() const; + + void dump() const; + friend bool operator==(SLPNode const& lhs, SLPNode const& rhs) { return std::tie(lhs.lanes) == std::tie(rhs.lanes); } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h new file mode 100644 index 00000000..d29f8e81 --- /dev/null +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h @@ -0,0 +1,24 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPUTIL_H +#define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPUTIL_H + +#include "mlir/IR/Operation.h" + +namespace mlir { + namespace spn { + namespace low { + namespace slp { + + bool areConsecutiveLoads(Operation* load1, Operation* load2); + bool areConsecutiveLoads(std::vector const& loads); + + } + } + } +} + +#endif //SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPUTIL_H diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h index 33eb9c88..84852bf5 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h @@ -15,6 +15,10 @@ namespace mlir { namespace low { namespace slp { + namespace { + typedef std::pair Extraction; + } + struct SLPVectorizationPass : public PassWrapper> { public: @@ -24,10 +28,13 @@ namespace mlir { private: - void transform(SLPGraph& graph); - Operation* transform(SLPNode& node, bool isRoot); + void transform(SLPGraph const& graph); + Operation* transform(SLPNode const& node, size_t vectorIndex, size_t spill); + void updateExtractions(SLPNode const& node, size_t const& vectorIndex, Operation* vectorOp); - std::map>> extractOps; + /// Stores where operations can find their operands after vectorization in case their defining operations + /// were deleted during vectorization. + std::map> extractions; }; } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h deleted file mode 100644 index c3afecee..00000000 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h +++ /dev/null @@ -1,33 +0,0 @@ -// -// This file is part of the SPNC project. -// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. -// - -#ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPVECTORIZATIONPATTERNS_H -#define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPVECTORIZATIONPATTERNS_H - -#include "mlir/Transforms/DialectConversion.h" -#include "LoSPNtoCPU/Vectorization/SLP/SLPGraph.h" - -namespace mlir { - namespace spn { - namespace low { - namespace slp { - - struct VectorizeTask : OpRewritePattern { - - using OpRewritePattern::OpRewritePattern; - - explicit VectorizeTask(MLIRContext* context, SLPGraph& graph); - - LogicalResult matchAndRewrite(FuncOp op, PatternRewriter& rewriter) const override; - - private: - SLPGraph& graph; - }; - } - } - } -} - -#endif //SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPVECTORIZATIONPATTERNS_H diff --git a/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt b/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt index e7574654..3a8e992d 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt +++ b/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt @@ -9,7 +9,7 @@ add_mlir_conversion_library(MLIRLoSPNtoCPUTransforms Vectorization/SLP/SLPNode.cpp Vectorization/SLP/SLPSeeding.cpp Vectorization/SLP/SLPGraph.cpp - Vectorization/SLP/SLPVectorizationPatterns.cpp + Vectorization/SLP/SLPUtil.cpp Vectorization/SLP/SLPVectorizationPass.cpp LoSPNtoCPUConversionPasses.cpp diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp index 125cbc14..d541163c 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp @@ -6,6 +6,7 @@ #include "mlir/Dialect/StandardOps/IR/Ops.h" #include "llvm/Support/Debug.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPGraph.h" +#include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" #include #include @@ -16,7 +17,7 @@ SLPGraph::SLPGraph(seed_t const& seed, size_t const& maxLookAhead) : maxLookAhea buildGraph(seed, root); } -SLPNode& SLPGraph::getRoot() { +SLPNode const& SLPGraph::getRoot() const { return root; } @@ -63,35 +64,6 @@ void SLPGraph::dump() const { // Some helper functions in an anonymous namespace. namespace { - bool areConsecutive(Operation* op1, Operation* op2) { - auto load1 = dyn_cast(op1); - auto load2 = dyn_cast(op2); - if (!load1 || !load2) { - return false; - } - if (load1.indices().size() != load2.indices().size()) { - return false; - } - for (size_t i = 0; i < load1.indices().size(); ++i) { - auto const1 = load1.indices()[i].getDefiningOp(); - auto const2 = load2.indices()[i].getDefiningOp(); - if (!const1 || !const2) { - return false; - } - auto index1 = const1.value().dyn_cast(); - auto index2 = const2.value().dyn_cast(); - if (!index1 || !index2) { - return false; - } - if (i == load1.indices().size() - 1) { - return index1.getInt() == index2.getInt() - 1; - } else if (index1.getInt() != index2.getInt()) { - return false; - } - } - return false; - } - bool vectorizable(std::vector const& operations) { for (size_t i = 0; i < operations.size(); ++i) { auto* op = operations[i]; @@ -225,6 +197,10 @@ void SLPGraph::buildGraph(std::vector const& operations, SLPNode& cu } void SLPGraph::reorderOperands(SLPNode const& multinode) { + if(std::uintptr_t(multinode.getOperation(0, 0)) == 12345) { + multinode.dump(); + llvm::dbgs() << "\n"; + } auto const& numOperands = multinode.numOperands(); std::vector> finalOrder{multinode.numLanes()}; std::vector> mode{multinode.numLanes()}; @@ -304,7 +280,7 @@ std::pair SLPGraph::getBest(Mode const& mode, best = candidates.front(); for (auto& candidate : candidates) { if (mode == LOAD) { - if (areConsecutive(last, candidate)) { + if (areConsecutiveLoads(last, candidate)) { bestCandidates.emplace_back(candidate); } } else if (candidate->getName() == last->getName()) { @@ -360,7 +336,7 @@ int SLPGraph::getLookAheadScore(Operation* last, Operation* candidate, size_t co return 0; } if (dyn_cast(last)) { - return areConsecutive(last, candidate) ? 1 : 0; + return areConsecutiveLoads(last, candidate) ? 1 : 0; } return 1; } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp index 73032b2a..1c0f1a74 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp @@ -4,6 +4,7 @@ // #include "LoSPNtoCPU/Vectorization/SLP/SLPNode.h" +#include "llvm/Support/Debug.h" using namespace mlir; using namespace mlir::spn; @@ -30,7 +31,7 @@ void SLPNode::setOperation(size_t lane, size_t index, Operation* operation) { } bool SLPNode::isMultiNode() const { - return lanes.front().size() > 1; + return numVectors() > 1; } bool SLPNode::isUniform() const { @@ -87,3 +88,19 @@ std::vector SLPNode::getOperands() const { size_t SLPNode::numOperands() const { return operandNodes.size(); } + +Type SLPNode::getResultType() const { + return lanes[0][0]->getResult(0).getType(); +} + +void SLPNode::dump() const { + for (size_t i = numVectors(); i-- > 0;) { + for (size_t lane = 0; lane < numLanes(); ++lane) { + llvm::dbgs() << *getOperation(lane, i) << "(" << getOperation(lane, i) << ")"; + if (lane < numLanes() - 1) { + llvm::dbgs() << "\t|\t"; + } + } + llvm::dbgs() << "\n"; + } +} diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp new file mode 100644 index 00000000..36be35c0 --- /dev/null +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp @@ -0,0 +1,57 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" +#include "mlir/Dialect/StandardOps/IR/Ops.h" + +using namespace mlir; + +bool mlir::spn::low::slp::areConsecutiveLoads(Operation* load1, Operation* load2) { + + if (load1 == load2) { + return false; + } + + auto loadOp1 = dyn_cast(load1); + auto loadOp2 = dyn_cast(load2); + + if (!loadOp1 || !loadOp2) { + return false; + } + + if (loadOp1.indices().size() != loadOp2.indices().size()) { + return false; + } + + for (size_t index = 0; index < loadOp1.indices().size(); ++index) { + auto constOp1 = loadOp1.indices()[index].getDefiningOp(); + auto constOp2 = loadOp2.indices()[index].getDefiningOp(); + if (!constOp1 || !constOp2) { + return false; + } + auto indexVal1 = constOp1.value().dyn_cast(); + auto indexVal2 = constOp2.value().dyn_cast(); + if (!indexVal1 || !indexVal2) { + return false; + } + if ((index == loadOp1.indices().size() - 1 && indexVal1.getInt() != indexVal2.getInt() - 1) + || indexVal1.getInt() != indexVal2.getInt()) { + return false; + } + } + return true; +} + +bool mlir::spn::low::slp::areConsecutiveLoads(std::vector const& loads) { + for (size_t i = 0; i < loads.size() - 1; ++i) { + auto* loadOp1 = loads[i]; + auto* loadOp2 = loads[i + 1]; + if (!areConsecutiveLoads(loadOp1, loadOp2)) { + return false; + } + } + return true; +} + diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp index 3625978c..266511c6 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp @@ -5,17 +5,25 @@ #include "LoSPN/LoSPNDialect.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h" +#include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" #include "LoSPNtoCPU/Vectorization/TargetInformation.h" #include "LoSPNtoCPU/LoSPNtoCPUTypeConverter.h" #include "LoSPNtoCPU/Vectorization/LoSPNVectorizationTypeConverter.h" #include "mlir/Dialect/SCF/SCF.h" #include "mlir/Dialect/Vector/VectorOps.h" #include "mlir/Dialect/StandardOps/IR/Ops.h" -#include "mlir/Transforms/DialectConversion.h" + +using namespace mlir::spn::low::slp; // Anonymous namespace for helper functions. namespace { + bool isBroadcastable(std::vector const& vector) { + return std::all_of(std::begin(vector), std::end(vector), [&](mlir::Operation* op) { + return op == vector.front(); + }); + } + /// For debugging purposes. void printOperation(mlir::Operation* op) { llvm::dbgs() << "node_" << op << "[label=\"" << op->getName().getStringRef() << "\\n" << op; @@ -73,24 +81,15 @@ namespace { } llvm::dbgs() << "}\n"; } + } -void mlir::spn::low::slp::SLPVectorizationPass::runOnOperation() { +void SLPVectorizationPass::runOnOperation() { llvm::StringRef funcName = getOperation().getName(); if (!funcName.contains("task_")) { return; } - ConversionTarget target(getContext()); - - target.addLegalDialect(); - target.addLegalDialect(); - target.addLegalDialect(); - target.addLegalOp(); - target.addLegalOp(); - - target.addLegalDialect(); - auto function = getOperation(); auto& seedAnalysis = getAnalysis(); @@ -106,50 +105,107 @@ void mlir::spn::low::slp::SLPVectorizationPass::runOnOperation() { } -void mlir::spn::low::slp::SLPVectorizationPass::transform(SLPGraph& graph) { - graph.dump(); - auto* rootOperation = transform(graph.getRoot(), true); +void SLPVectorizationPass::transform(SLPGraph const& graph) { + //graph.dump(); + auto* rootOperation = transform(graph.getRoot(), 0, 1); assert(false); // Update users of vector operations that aren't contained in the graph. - for (auto const& entry : extractOps) { - for (auto const& extractOpData : entry.second) { - extractOpData.first->setOperand(extractOpData.second, nullptr); - } - } } -mlir::Operation* mlir::spn::low::slp::SLPVectorizationPass::transform(SLPNode& node, bool isRoot) { - LoSPNVectorizationTypeConverter typeConverter{4}; - for (size_t vectorIndex = 0; vectorIndex < node.numVectors(); ++vectorIndex) { - if (node.isUniform()) { - - for (size_t lane = 0; lane < node.numLanes(); ++lane) { - auto* operation = node.getOperation(lane, vectorIndex); +mlir::Operation* SLPVectorizationPass::transform(SLPNode const& node, size_t vectorIndex, size_t spill) { - auto type = VectorType::get(node.numLanes(), operation->getResult(0).getType()); - auto operands = llvm::SmallVector{}; - auto* vectorOperation = Operation::create(operation->getLoc(), - operation->getName(), - type, - operands, - operation->getAttrs(), - operation->getSuccessors(), - operation->getNumRegions()); + node.dump(); + llvm::dbgs() << "\n"; + if (std::uintptr_t(node.getOperation(0, 0)) == 0x9033b8) { + llvm::dbgs() << "\n"; + } - operation->dropAllUses(); + LoSPNVectorizationTypeConverter typeConverter{static_cast(node.numLanes())}; + auto vectorType = typeConverter.convertType(node.getResultType()); + auto builder = OpBuilder{&getContext()}; + + auto const& vectorizableOps = node.getVector(vectorIndex); + auto const& firstOp = vectorizableOps.front(); + builder.setInsertionPointAfter(firstOp); + + Operation* vectorOp; + + if (isBroadcastable(vectorizableOps)) { + auto value = firstOp->getResult(0); + vectorOp = builder.create(firstOp->getLoc(), vectorType, value); + } else if (node.isUniform()) { + if (areConsecutiveLoads(vectorizableOps)) { + auto base = firstOp->getResult(0); + auto zero = builder.create(firstOp->getLoc(), builder.getIndexAttr(0)); + auto indices = ValueRange{zero.getResult()}; + vectorOp = builder.create(firstOp->getLoc(), vectorType, base, indices); + } else { + llvm::SmallVector operands; + for (size_t i = 0; i < firstOp->getNumOperands(); ++i) { + + SLPNode const* operandNode = &node; + size_t operandNodeIndex = 0; + + unsigned availableOperands = node.numVectors(); + unsigned usedOperands = vectorIndex * firstOp->getNumOperands() + spill; + size_t nextIndex = usedOperands + i; + size_t nodeSpill = spill; + + while (availableOperands <= nextIndex) { + if (operandNodeIndex == 0) { + nodeSpill = ((firstOp->getNumOperands() - 1) * node.numVectors()) + spill; + } else { + nodeSpill -= availableOperands; + } + operandNode = &node.getOperand(operandNodeIndex++); + nextIndex -= availableOperands; + availableOperands = operandNode->numVectors(); + } + operands.emplace_back(transform(*operandNode, nextIndex, nodeSpill)->getResult(0)); } - } else { + vectorOp = Operation::create(firstOp->getLoc(), + firstOp->getName(), + vectorType, + operands, + firstOp->getAttrs(), + firstOp->getSuccessors(), + firstOp->getNumRegions()); + builder.insert(vectorOp); + } + } else { + vectorOp = builder.create(firstOp->getLoc(), vectorType, firstOp->getResult(0)); + for (size_t lane = 1; lane < node.numLanes(); ++lane) { + vectorOp = builder.create(vectorOp->getLoc(), + vectorizableOps[lane]->getResult(0), + vectorOp->getResult(0), + lane); } } - bool isMixed = false; - assert(false); - extractOps[nullptr]; - return node.getVector(0).front(); + + vectorOp->dump(); + updateExtractions(node, vectorIndex, vectorOp); + + return vectorOp; +} + +void SLPVectorizationPass::updateExtractions(SLPNode const& node, size_t const& vectorIndex, Operation* vectorOp) { + for (size_t lane = 0; lane < node.numLanes(); ++lane) { + auto* operation = node.getOperation(lane, vectorIndex); + for (auto* use : operation->getUsers()) { + for (size_t i = 0; i < use->getNumOperands(); ++i) { + if (use->getOperand(i) == operation->getResult(0)) { + extractions[use][i] = std::make_pair(vectorOp, i); + break; + } + } + } + extractions.erase(operation); + } } std::unique_ptr mlir::spn::createSLPVectorizationPass() { - return std::make_unique(); + return std::make_unique(); } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp deleted file mode 100644 index b675697b..00000000 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp +++ /dev/null @@ -1,25 +0,0 @@ -// -// This file is part of the SPNC project. -// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. -// - -#include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h" -#include "mlir/IR/BlockAndValueMapping.h" -#include "mlir/Dialect/Vector/VectorOps.h" -#include "mlir/Dialect/Vector/VectorTransforms.h" - -using namespace mlir; -using namespace mlir::spn::low::slp; - -LogicalResult VectorizeTask::matchAndRewrite(FuncOp op, PatternRewriter& rewriter) const { - - if (!op->getName().getStringRef().contains("task_")) { - return failure(); - } - //op->replaceUsesOfWith(); - - return success(); -} -VectorizeTask::VectorizeTask(MLIRContext* context, SLPGraph& graph) : OpRewritePattern(context), graph{graph} { - -} From 611f8603e72183cc57ebcfffef86a1edb0f4ebe5 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Wed, 24 Mar 2021 14:49:31 +0100 Subject: [PATCH 056/385] Better dismantling. --- .../LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp | 2 +- .../SLP/SLPVectorizationPass.cpp | 53 ++++++++++--------- 2 files changed, 29 insertions(+), 26 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp index d541163c..c556552c 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp @@ -197,7 +197,7 @@ void SLPGraph::buildGraph(std::vector const& operations, SLPNode& cu } void SLPGraph::reorderOperands(SLPNode const& multinode) { - if(std::uintptr_t(multinode.getOperation(0, 0)) == 12345) { + if (std::uintptr_t(multinode.getOperation(0, 0)) == 12345) { multinode.dump(); llvm::dbgs() << "\n"; } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp index 266511c6..f8338897 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp @@ -133,27 +133,29 @@ mlir::Operation* SLPVectorizationPass::transform(SLPNode const& node, size_t vec if (isBroadcastable(vectorizableOps)) { auto value = firstOp->getResult(0); vectorOp = builder.create(firstOp->getLoc(), vectorType, value); - } else if (node.isUniform()) { - if (areConsecutiveLoads(vectorizableOps)) { - auto base = firstOp->getResult(0); - auto zero = builder.create(firstOp->getLoc(), builder.getIndexAttr(0)); - auto indices = ValueRange{zero.getResult()}; - vectorOp = builder.create(firstOp->getLoc(), vectorType, base, indices); - } else { - llvm::SmallVector operands; - for (size_t i = 0; i < firstOp->getNumOperands(); ++i) { - - SLPNode const* operandNode = &node; + } else if (areConsecutiveLoads(vectorizableOps)) { + auto base = firstOp->getResult(0); + auto zero = builder.create(firstOp->getLoc(), builder.getIndexAttr(0)); + auto indices = ValueRange{zero.getResult()}; + vectorOp = builder.create(firstOp->getLoc(), vectorType, base, indices); + } else if (node.isUniform() && firstOp->getNumOperands() > 0) { + llvm::SmallVector operands; + for (size_t i = 0; i < firstOp->getNumOperands(); ++i) { + + if (node.isMultiNode()) { + // TODO: visit non-multinode-operands first? size_t operandNodeIndex = 0; + SLPNode const* operandNode = &node; + + size_t nodeSpill = spill; unsigned availableOperands = node.numVectors(); - unsigned usedOperands = vectorIndex * firstOp->getNumOperands() + spill; + unsigned usedOperands = vectorIndex * firstOp->getNumOperands() + nodeSpill; size_t nextIndex = usedOperands + i; - size_t nodeSpill = spill; while (availableOperands <= nextIndex) { if (operandNodeIndex == 0) { - nodeSpill = ((firstOp->getNumOperands() - 1) * node.numVectors()) + spill; + nodeSpill = ((firstOp->getNumOperands() - 1) * node.numVectors()) + nodeSpill; } else { nodeSpill -= availableOperands; } @@ -161,19 +163,21 @@ mlir::Operation* SLPVectorizationPass::transform(SLPNode const& node, size_t vec nextIndex -= availableOperands; availableOperands = operandNode->numVectors(); } - + assert(nodeSpill > 0); operands.emplace_back(transform(*operandNode, nextIndex, nodeSpill)->getResult(0)); + } else { + operands.emplace_back(transform(node.getOperand(i), 0, 1)->getResult(0)); } - vectorOp = Operation::create(firstOp->getLoc(), - firstOp->getName(), - vectorType, - operands, - firstOp->getAttrs(), - firstOp->getSuccessors(), - firstOp->getNumRegions()); - builder.insert(vectorOp); - } + } + vectorOp = Operation::create(firstOp->getLoc(), + firstOp->getName(), + vectorType, + operands, + firstOp->getAttrs(), + firstOp->getSuccessors(), + firstOp->getNumRegions()); + builder.insert(vectorOp); } else { vectorOp = builder.create(firstOp->getLoc(), vectorType, firstOp->getResult(0)); for (size_t lane = 1; lane < node.numLanes(); ++lane) { @@ -208,4 +212,3 @@ void SLPVectorizationPass::updateExtractions(SLPNode const& node, size_t const& std::unique_ptr mlir::spn::createSLPVectorizationPass() { return std::make_unique(); } - From ccb280d391676cffcd47a55c82a9990540765675 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Fri, 26 Mar 2021 06:55:32 +0100 Subject: [PATCH 057/385] Better SLPGraph > vector instructions conversion. --- .../Vectorization/SLP/SLPVectorizationPass.h | 6 +- .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp | 32 ++---- .../SLP/SLPVectorizationPass.cpp | 108 +++++++++--------- 3 files changed, 67 insertions(+), 79 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h index 84852bf5..77bc0e1a 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h @@ -29,8 +29,10 @@ namespace mlir { private: void transform(SLPGraph const& graph); - Operation* transform(SLPNode const& node, size_t vectorIndex, size_t spill); - void updateExtractions(SLPNode const& node, size_t const& vectorIndex, Operation* vectorOp); + Value transform(SLPNode const& node, + size_t vectorIndex, + std::map& vectorsDone); + Operation* updateExtractions(SLPNode const& node, size_t const& vectorIndex, Operation* vectorOp); /// Stores where operations can find their operands after vectorization in case their defining operations /// were deleted during vectorization. diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp index 36be35c0..f8b36387 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp @@ -4,44 +4,32 @@ // #include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" -#include "mlir/Dialect/StandardOps/IR/Ops.h" +#include "LoSPN/LoSPNOps.h" using namespace mlir; -bool mlir::spn::low::slp::areConsecutiveLoads(Operation* load1, Operation* load2) { +bool mlir::spn::low::slp::areConsecutiveLoads(Operation* op1, Operation* op2) { - if (load1 == load2) { + if (op1 == op2) { return false; } - auto loadOp1 = dyn_cast(load1); - auto loadOp2 = dyn_cast(load2); + auto loadOp1 = dyn_cast(op1); + auto loadOp2 = dyn_cast(op2); if (!loadOp1 || !loadOp2) { return false; } - if (loadOp1.indices().size() != loadOp2.indices().size()) { + if (loadOp1.batchMem() != loadOp2.batchMem()) { return false; } - for (size_t index = 0; index < loadOp1.indices().size(); ++index) { - auto constOp1 = loadOp1.indices()[index].getDefiningOp(); - auto constOp2 = loadOp2.indices()[index].getDefiningOp(); - if (!constOp1 || !constOp2) { - return false; - } - auto indexVal1 = constOp1.value().dyn_cast(); - auto indexVal2 = constOp2.value().dyn_cast(); - if (!indexVal1 || !indexVal2) { - return false; - } - if ((index == loadOp1.indices().size() - 1 && indexVal1.getInt() != indexVal2.getInt() - 1) - || indexVal1.getInt() != indexVal2.getInt()) { - return false; - } + if (loadOp1.batchIndex() != loadOp2.batchIndex()) { + return false; } - return true; + + return loadOp1.sampleIndex() == loadOp2.sampleIndex(); } bool mlir::spn::low::slp::areConsecutiveLoads(std::vector const& loads) { diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp index f8338897..9c05bcd8 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp @@ -34,6 +34,10 @@ namespace { llvm::dbgs() << "\\nvalue: " << std::to_string(constantOp.value().dyn_cast().getValueAsDouble()); } + } else if (auto batchReadOp = llvm::dyn_cast(op)) { + llvm::dbgs() << "\\nbatch mem: " << batchReadOp.batchMem().dyn_cast().getArgNumber(); + llvm::dbgs() << "\\nbatch index: " << batchReadOp.batchMem().dyn_cast().getArgNumber(); + llvm::dbgs() << "\\nsample index: " << batchReadOp.sampleIndex(); } llvm::dbgs() << "\", fillcolor=\"#a0522d\"];\n"; } @@ -99,7 +103,7 @@ void SLPVectorizationPass::runOnOperation() { auto seeds = seedAnalysis.getSeeds(width, seedAnalysis.getOpDepths(), SearchMode::UseBeforeDef); assert(!seeds.empty() && "couldn't find a seed!"); - + printSeedTree(seeds.front()); SLPGraph graph(seeds.front(), 3); transform(graph); @@ -107,19 +111,25 @@ void SLPVectorizationPass::runOnOperation() { void SLPVectorizationPass::transform(SLPGraph const& graph) { //graph.dump(); - auto* rootOperation = transform(graph.getRoot(), 0, 1); + std::map vectorsDone; + auto rootOperation = transform(graph.getRoot(), 0, vectorsDone); + llvm::dbgs() << extractions.size() << "\n"; assert(false); // Update users of vector operations that aren't contained in the graph. } -mlir::Operation* SLPVectorizationPass::transform(SLPNode const& node, size_t vectorIndex, size_t spill) { +mlir::Value SLPVectorizationPass::transform(SLPNode const& node, + size_t vectorIndex, + std::map& vectorsDone) { node.dump(); llvm::dbgs() << "\n"; - if (std::uintptr_t(node.getOperation(0, 0)) == 0x9033b8) { + if (std::uintptr_t(node.getOperation(0, 0)) == 0x716008) { llvm::dbgs() << "\n"; } + vectorsDone[&node]++; + LoSPNVectorizationTypeConverter typeConverter{static_cast(node.numLanes())}; auto vectorType = typeConverter.convertType(node.getResultType()); auto builder = OpBuilder{&getContext()}; @@ -128,73 +138,60 @@ mlir::Operation* SLPVectorizationPass::transform(SLPNode const& node, size_t vec auto const& firstOp = vectorizableOps.front(); builder.setInsertionPointAfter(firstOp); - Operation* vectorOp; - if (isBroadcastable(vectorizableOps)) { auto value = firstOp->getResult(0); - vectorOp = builder.create(firstOp->getLoc(), vectorType, value); - } else if (areConsecutiveLoads(vectorizableOps)) { + auto vectorOp = builder.create(firstOp->getLoc(), vectorType, value); + return updateExtractions(node, vectorIndex, vectorOp)->getResult(0); + } + + if (areConsecutiveLoads(vectorizableOps)) { auto base = firstOp->getResult(0); auto zero = builder.create(firstOp->getLoc(), builder.getIndexAttr(0)); auto indices = ValueRange{zero.getResult()}; - vectorOp = builder.create(firstOp->getLoc(), vectorType, base, indices); - } else if (node.isUniform() && firstOp->getNumOperands() > 0) { - llvm::SmallVector operands; - for (size_t i = 0; i < firstOp->getNumOperands(); ++i) { - - if (node.isMultiNode()) { - // TODO: visit non-multinode-operands first? - size_t operandNodeIndex = 0; - SLPNode const* operandNode = &node; + auto vectorOp = builder.create(firstOp->getLoc(), vectorType, base, indices); + return updateExtractions(node, vectorIndex, vectorOp)->getResult(0); + } - size_t nodeSpill = spill; + if (node.isUniform()) { - unsigned availableOperands = node.numVectors(); - unsigned usedOperands = vectorIndex * firstOp->getNumOperands() + nodeSpill; - size_t nextIndex = usedOperands + i; + llvm::SmallVector operands; - while (availableOperands <= nextIndex) { - if (operandNodeIndex == 0) { - nodeSpill = ((firstOp->getNumOperands() - 1) * node.numVectors()) + nodeSpill; - } else { - nodeSpill -= availableOperands; - } - operandNode = &node.getOperand(operandNodeIndex++); - nextIndex -= availableOperands; - availableOperands = operandNode->numVectors(); - } - assert(nodeSpill > 0); - operands.emplace_back(transform(*operandNode, nextIndex, nodeSpill)->getResult(0)); - } else { - operands.emplace_back(transform(node.getOperand(i), 0, 1)->getResult(0)); + for (size_t i = 0; i < firstOp->getNumOperands(); ++i) { + size_t operandNodeIndex = vectorIndex % node.getOperands().size(); + auto* operandNode = (i == 0) ? &node : &node.getOperand(operandNodeIndex); + size_t nextIndex = vectorsDone[operandNode]; + while (nextIndex >= operandNode->numVectors()) { + operandNode = &node.getOperand(operandNodeIndex++ % node.getOperands().size()); + nextIndex = vectorsDone[operandNode]; } - + operands.emplace_back(transform(*operandNode, nextIndex, vectorsDone)); } - vectorOp = Operation::create(firstOp->getLoc(), - firstOp->getName(), - vectorType, - operands, - firstOp->getAttrs(), - firstOp->getSuccessors(), - firstOp->getNumRegions()); + + auto vectorOp = Operation::create(firstOp->getLoc(), + firstOp->getName(), + vectorType, + operands, + firstOp->getAttrs(), + firstOp->getSuccessors(), + firstOp->getNumRegions()); builder.insert(vectorOp); - } else { - vectorOp = builder.create(firstOp->getLoc(), vectorType, firstOp->getResult(0)); - for (size_t lane = 1; lane < node.numLanes(); ++lane) { - vectorOp = builder.create(vectorOp->getLoc(), - vectorizableOps[lane]->getResult(0), - vectorOp->getResult(0), - lane); - } + return updateExtractions(node, vectorIndex, vectorOp)->getResult(0); } - vectorOp->dump(); - updateExtractions(node, vectorIndex, vectorOp); + Operation* vectorOp = builder.create(firstOp->getLoc(), vectorType, firstOp->getResult(0)); + for (size_t lane = 1; lane < node.numLanes(); ++lane) { + vectorOp = builder.create(vectorOp->getLoc(), + vectorizableOps[lane]->getResult(0), + vectorOp->getResult(0), + lane); + } - return vectorOp; + return updateExtractions(node, vectorIndex, vectorOp)->getResult(0); } -void SLPVectorizationPass::updateExtractions(SLPNode const& node, size_t const& vectorIndex, Operation* vectorOp) { +mlir::Operation* SLPVectorizationPass::updateExtractions(SLPNode const& node, + size_t const& vectorIndex, + Operation* vectorOp) { for (size_t lane = 0; lane < node.numLanes(); ++lane) { auto* operation = node.getOperation(lane, vectorIndex); for (auto* use : operation->getUsers()) { @@ -207,6 +204,7 @@ void SLPVectorizationPass::updateExtractions(SLPNode const& node, size_t const& } extractions.erase(operation); } + return vectorOp; } std::unique_ptr mlir::spn::createSLPVectorizationPass() { From c6890bf31cbb7b4e0fb1adf505b78ca2d4e6669c Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sat, 27 Mar 2021 07:59:34 +0100 Subject: [PATCH 058/385] Reworked SLPGraph class, better SLPNode > code conversion. --- .../SLP/{SLPGraph.h => SLPGraphBuilder.h} | 20 +-- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.h | 10 +- .../Vectorization/SLP/SLPVectorizationPass.h | 13 +- mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt | 2 +- .../SLP/{SLPGraph.cpp => SLPGraphBuilder.cpp} | 164 ++++++------------ .../LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp | 59 ++++++- .../SLP/SLPVectorizationPass.cpp | 132 +++++++++----- 7 files changed, 226 insertions(+), 174 deletions(-) rename mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/{SLPGraph.h => SLPGraphBuilder.h} (76%) rename mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/{SLPGraph.cpp => SLPGraphBuilder.cpp} (62%) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h similarity index 76% rename from mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h rename to mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h index 1bad7555..0a26463b 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h @@ -3,8 +3,8 @@ // Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. // -#ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPGRAPH_H -#define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPGRAPH_H +#ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPGRAPHBUILDER_H +#define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPGRAPHBUILDER_H #include "mlir/IR/Operation.h" #include "SLPMode.h" @@ -16,21 +16,19 @@ namespace mlir { namespace low { namespace slp { - class SLPGraph { + class SLPGraphBuilder { public: - SLPGraph(seed_t const& seed, size_t const& maxLookAhead); + explicit SLPGraphBuilder(size_t maxLookAhead); - SLPNode const& getRoot() const; - - void dump() const; + std::unique_ptr build(seed_t const& seed) const; private: - void buildGraph(std::vector const& operations, SLPNode& currentNode); + void buildGraph(std::vector const& operations, SLPNode* currentNode) const; - void reorderOperands(SLPNode const& multinode); + void reorderOperands(SLPNode* multinode) const; std::pair getBest(Mode const& mode, Operation* last, @@ -40,12 +38,10 @@ namespace mlir { size_t const maxLookAhead; - SLPNode root; - }; } } } } -#endif //SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPGRAPH_H +#endif //SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPGRAPHBUILDER_H diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h index 1d19539d..fa62e340 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h @@ -32,14 +32,18 @@ namespace mlir { size_t numVectors() const; std::vector getVector(size_t index) const; - SLPNode& addOperand(std::vector const& operations); - SLPNode& getOperand(size_t index) const; + SLPNode* addOperand(std::vector const& operations); + SLPNode* getOperand(size_t index) const; std::vector getOperands() const; size_t numOperands() const; + void addNodeInput(Value const& value); + Value const& getNodeInput(size_t index) const; + Type getResultType() const; void dump() const; + void dumpGraph() const; friend bool operator==(SLPNode const& lhs, SLPNode const& rhs) { return std::tie(lhs.lanes) == std::tie(rhs.lanes); @@ -58,6 +62,8 @@ namespace mlir { std::vector> operandNodes; + std::vector nodeInputs; + }; } } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h index 77bc0e1a..4376b071 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h @@ -8,7 +8,7 @@ #include "mlir/IR/Operation.h" #include "mlir/Pass/Pass.h" -#include "SLPGraph.h" +#include "SLPGraphBuilder.h" namespace mlir { namespace spn { @@ -28,11 +28,12 @@ namespace mlir { private: - void transform(SLPGraph const& graph); - Value transform(SLPNode const& node, - size_t vectorIndex, - std::map& vectorsDone); - Operation* updateExtractions(SLPNode const& node, size_t const& vectorIndex, Operation* vectorOp); + void transform(SLPNode* root); + Value transform(SLPNode* node, + size_t vectorIndex, + std::map& vectorsDone, + std::map& nodeInputsDone); + Value applyCreation(SLPNode* node, size_t vectorIndex, Operation* vectorOp); /// Stores where operations can find their operands after vectorization in case their defining operations /// were deleted during vectorization. diff --git a/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt b/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt index 3a8e992d..c2dd9829 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt +++ b/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt @@ -8,7 +8,7 @@ add_mlir_conversion_library(MLIRLoSPNtoCPUTransforms Vectorization/SLP/SLPMode.cpp Vectorization/SLP/SLPNode.cpp Vectorization/SLP/SLPSeeding.cpp - Vectorization/SLP/SLPGraph.cpp + Vectorization/SLP/SLPGraphBuilder.cpp Vectorization/SLP/SLPUtil.cpp Vectorization/SLP/SLPVectorizationPass.cpp LoSPNtoCPUConversionPasses.cpp diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp similarity index 62% rename from mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp rename to mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp index c556552c..4006b5a7 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp @@ -5,69 +5,30 @@ #include "mlir/Dialect/StandardOps/IR/Ops.h" #include "llvm/Support/Debug.h" -#include "LoSPNtoCPU/Vectorization/SLP/SLPGraph.h" +#include "LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" #include #include +#include using namespace mlir; using namespace mlir::spn::low::slp; -SLPGraph::SLPGraph(seed_t const& seed, size_t const& maxLookAhead) : maxLookAhead{maxLookAhead}, root{seed} { - buildGraph(seed, root); -} +SLPGraphBuilder::SLPGraphBuilder(size_t maxLookAhead) : maxLookAhead{maxLookAhead} {} -SLPNode const& SLPGraph::getRoot() const { +std::unique_ptr SLPGraphBuilder::build(seed_t const& seed) const { + auto root = std::make_unique(SLPNode{seed}); + buildGraph(seed, root.get()); return root; } -void SLPGraph::dump() const { - - llvm::dbgs() << "digraph debug_graph {\n"; - llvm::dbgs() << "rankdir = BT;\n"; - llvm::dbgs() << "node[shape=box];\n"; - - std::queue worklist; - worklist.emplace(&root); - - while (!worklist.empty()) { - auto const* node = worklist.front(); - worklist.pop(); - - llvm::dbgs() << "node_" << node << "[label=<\n"; - llvm::dbgs() << "\t\n"; - for (size_t i = node->numVectors(); i-- > 0;) { - llvm::dbgs() << "\t\t\n"; - for (size_t lane = 0; lane < node->numLanes(); ++lane) { - auto* operation = node->getOperation(lane, i); - llvm::dbgs() << "\t\t\t"; - if (lane < node->numLanes() - 1) { - llvm::dbgs() << ""; - } - llvm::dbgs() << "\n"; - } - llvm::dbgs() << "\t\t\n"; - } - llvm::dbgs() << "\t
"; - llvm::dbgs() << "" << operation->getName() << " (" << operation << ")"; - llvm::dbgs() << "
\n"; - llvm::dbgs() << ">];\n"; - - for (auto const& operand : node->getOperands()) { - llvm::dbgs() << "node_" << node << "->" << "node_" << operand << ";\n"; - worklist.emplace(operand); - } - } - llvm::dbgs() << "}\n"; -} - // Some helper functions in an anonymous namespace. namespace { bool vectorizable(std::vector const& operations) { for (size_t i = 0; i < operations.size(); ++i) { auto* op = operations[i]; - if (!op->hasTrait() || (i > 0 && op->getName() != operations.front()->getName())) { + if (!op->hasTrait() || (op->getName() != operations.front()->getName())) { return false; } } @@ -107,25 +68,7 @@ namespace { return allOperands; } - std::vector> getOperandsVectorized(std::vector const& operations) { - for (auto* operation : operations) { - assert(operation->getNumOperands() == operations.front()->getNumOperands() - && "operations must have same numbers of operands"); - } - std::vector> allOperands; - for (size_t i = 0; i < operations.front()->getNumOperands(); ++i) { - std::vector operands; - operands.reserve(operations.size()); - for (auto* operation : operations) { - operands.emplace_back(operation->getOperand(i).getDefiningOp()); - } - allOperands.emplace_back(operands); - } - return allOperands; - } - - void sortByOpcode(std::vector& operations, - Optional const& smallestOpcode) { + void sortByOpcode(std::vector& operations, Optional const& smallestOpcode) { std::sort(std::begin(operations), std::end(operations), [&](Operation* lhs, Operation* rhs) { if (smallestOpcode.hasValue()) { if (lhs->getName() == smallestOpcode.getValue()) { @@ -137,10 +80,10 @@ namespace { return lhs->getName().getStringRef() < rhs->getName().getStringRef(); }); } +} // end namespace -} - -void SLPGraph::buildGraph(std::vector const& operations, SLPNode& currentNode) { +void SLPGraphBuilder::buildGraph(std::vector const& operations, + SLPNode* currentNode) const { // Stop growing graph if (!vectorizable(operations)) { return; @@ -156,65 +99,75 @@ void SLPGraph::buildGraph(std::vector const& operations, SLPNode& cu } for (size_t i = 0; i < operations.front()->getNumOperands(); ++i) { if (std::all_of(std::begin(allOperands), std::end(allOperands), [&](auto const& operandOperations) { - return operandOperations[i]->getName() == currentOpCode && !escapesMultinode(operandOperations[i]); + return operandOperations[i]->getName() == currentOpCode + && !escapesMultinode(operandOperations[i]); })) { - for (size_t lane = 0; lane < currentNode.numLanes(); ++lane) { - currentNode.addOperationToLane(allOperands[lane][i], lane); + for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { + currentNode->addOperationToLane(allOperands[lane][i], lane); } - buildGraph(currentNode.getVector(currentNode.numVectors() - 1), currentNode); + buildGraph(currentNode->getVector(currentNode->numVectors() - 1), currentNode); } else { // TODO: here might be a good place to implement variable vector width std::vector operandOperations; - for (size_t lane = 0; lane < currentNode.numLanes(); ++lane) { + for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { operandOperations.emplace_back(allOperands[lane][i]); } - currentNode.addOperand(operandOperations); + currentNode->addOperand(operandOperations); } } - // B. Normal Mode: Finished building multi-node - if (currentNode.isMultiNode() && currentNode.areRootOfNode(operations)) { + if (currentNode->isMultiNode() && currentNode->areRootOfNode(operations)) { reorderOperands(currentNode); - for (auto* operandNode : currentNode.getOperands()) { - buildGraph(operandNode->getVector(operandNode->numVectors() - 1), *operandNode); + for (auto* operandNode : currentNode->getOperands()) { + buildGraph(operandNode->getVector(operandNode->numVectors() - 1), operandNode); } } } // 2. Non-Commutative else { - // Only consider operands further when the current operations aren't leaf nodes. - for (auto* operation : operations) { - for (auto const& operand : operation->getOperands()) { - if (operand.isa()) { - return; + for (size_t i = 0; i < operations.front()->getNumOperands(); ++i) { + std::vector operands; + bool containsBlockArgument = false; + for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { + auto operand = currentNode->getOperation(lane, 0)->getOperand(i); + operands.emplace_back(operand); + containsBlockArgument |= operand.isa(); + } + // Only consider operands that don't contain block arguments for further graph building. + if (containsBlockArgument) { + for (auto const& operand : operands) { + currentNode->addNodeInput(operand); } + } else { + std::vector operandOperations; + operandOperations.reserve(operands.size()); + for (auto const& operand : operands) { + operandOperations.emplace_back(operand.getDefiningOp()); + } + buildGraph(operandOperations, currentNode->addOperand(operandOperations)); } } - for (auto const& operandVector : getOperandsVectorized(operations)) { - buildGraph(operandVector, currentNode.addOperand(operandVector)); - } } } -void SLPGraph::reorderOperands(SLPNode const& multinode) { - if (std::uintptr_t(multinode.getOperation(0, 0)) == 12345) { - multinode.dump(); +void SLPGraphBuilder::reorderOperands(SLPNode* multinode) const { + if (std::uintptr_t(multinode->getOperation(0, 0)) == 12345) { + multinode->dump(); llvm::dbgs() << "\n"; } - auto const& numOperands = multinode.numOperands(); - std::vector> finalOrder{multinode.numLanes()}; - std::vector> mode{multinode.numLanes()}; + auto const& numOperands = multinode->numOperands(); + std::vector> finalOrder{multinode->numLanes()}; + std::vector> mode{multinode->numLanes()}; // 1. Strip first lane for (size_t i = 0; i < numOperands; ++i) { - auto operation = multinode.getOperand(i).getOperation(0, 0); + auto operation = multinode->getOperand(i)->getOperation(0, 0); finalOrder[0].emplace_back(operation); mode[0].emplace_back(modeFromOperation(operation)); } - // 2. For all other lanes, find best candidate - for (size_t lane = 1; lane < multinode.numLanes(); ++lane) { + for (size_t lane = 1; lane < multinode->numLanes(); ++lane) { std::vector candidates; - for (auto const& operand : multinode.getOperands()) { + for (auto const& operand : multinode->getOperands()) { candidates.emplace_back(operand->getOperation(lane, 0)); } // Look for a matching candidate @@ -228,17 +181,14 @@ void SLPGraph::reorderOperands(SLPNode const& multinode) { } auto* last = finalOrder[lane - 1][i]; auto const& bestResult = getBest(mode[lane - 1][i], last, candidates); - // Update output finalOrder[lane].emplace_back(bestResult.first); - // Detect SPLAT mode if (i == 1 && bestResult.first == last) { mode[lane][i] = SPLAT; } else { mode[lane].emplace_back(bestResult.second); } - } // Distribute remaining candidates in case we encountered a FAILED. for (auto* candidate : candidates) { @@ -250,20 +200,19 @@ void SLPGraph::reorderOperands(SLPNode const& multinode) { } } } - for (size_t operandIndex = 0; operandIndex < multinode.numOperands(); ++operandIndex) { - for (size_t lane = 0; lane < multinode.numLanes(); ++lane) { - multinode.getOperand(operandIndex).setOperation(lane, 0, finalOrder[lane][operandIndex]); + for (size_t operandIndex = 0; operandIndex < multinode->numOperands(); ++operandIndex) { + for (size_t lane = 0; lane < multinode->numLanes(); ++lane) { + multinode->getOperand(operandIndex)->setOperation(lane, 0, finalOrder[lane][operandIndex]); } } } -std::pair SLPGraph::getBest(Mode const& mode, - Operation* last, - std::vector& candidates) const { +std::pair SLPGraphBuilder::getBest(Mode const& mode, + Operation* last, + std::vector& candidates) const { Operation* best = nullptr; Mode resultMode = mode; std::vector bestCandidates; - if (mode == FAILED) { // Don't select now, let others choose first best = nullptr; @@ -321,7 +270,6 @@ std::pair SLPGraph::getBest(Mode const& mode, } } } - } // Remove best from candidates if (best != nullptr) { @@ -330,7 +278,7 @@ std::pair SLPGraph::getBest(Mode const& mode, return {best, resultMode}; } -int SLPGraph::getLookAheadScore(Operation* last, Operation* candidate, size_t const& maxLevel) const { +int SLPGraphBuilder::getLookAheadScore(Operation* last, Operation* candidate, size_t const& maxLevel) const { if (maxLevel == 0) { if (last->getName() != candidate->getName()) { return 0; diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp index 1c0f1a74..16598d01 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp @@ -5,12 +5,13 @@ #include "LoSPNtoCPU/Vectorization/SLP/SLPNode.h" #include "llvm/Support/Debug.h" +#include using namespace mlir; using namespace mlir::spn; using namespace mlir::spn::low::slp; -SLPNode::SLPNode(std::vector const& operations) : lanes{operations.size()} { +SLPNode::SLPNode(std::vector const& operations) : lanes{operations.size()}, operandNodes{}, nodeInputs{} { for (size_t i = 0; i < operations.size(); ++i) { lanes[i].emplace_back(operations[i]); } @@ -66,14 +67,14 @@ std::vector SLPNode::getVector(size_t index) const { return vector; } -SLPNode& SLPNode::addOperand(std::vector const& operations) { +SLPNode* SLPNode::addOperand(std::vector const& operations) { operandNodes.emplace_back(std::make_unique(operations)); - return *operandNodes.back(); + return operandNodes.back().get(); } -SLPNode& SLPNode::getOperand(size_t index) const { +SLPNode* SLPNode::getOperand(size_t index) const { assert(index <= operandNodes.size()); - return *operandNodes[index]; + return operandNodes[index].get(); } std::vector SLPNode::getOperands() const { @@ -89,6 +90,14 @@ size_t SLPNode::numOperands() const { return operandNodes.size(); } +void SLPNode::addNodeInput(Value const& value) { + nodeInputs.emplace_back(value); +} + +Value const& SLPNode::getNodeInput(size_t index) const { + return nodeInputs[index]; +} + Type SLPNode::getResultType() const { return lanes[0][0]->getResult(0).getType(); } @@ -104,3 +113,43 @@ void SLPNode::dump() const { llvm::dbgs() << "\n"; } } + +void SLPNode::dumpGraph() const { + + llvm::dbgs() << "digraph debug_graph {\n"; + llvm::dbgs() << "rankdir = BT;\n"; + llvm::dbgs() << "node[shape=box];\n"; + + std::queue worklist; + worklist.emplace(this); + + while (!worklist.empty()) { + auto const* node = worklist.front(); + worklist.pop(); + + llvm::dbgs() << "node_" << node << "[label=<\n"; + llvm::dbgs() << "\t\n"; + for (size_t i = node->numVectors(); i-- > 0;) { + llvm::dbgs() << "\t\t\n"; + for (size_t lane = 0; lane < node->numLanes(); ++lane) { + auto* operation = node->getOperation(lane, i); + llvm::dbgs() << "\t\t\t"; + if (lane < node->numLanes() - 1) { + llvm::dbgs() << ""; + } + llvm::dbgs() << "\n"; + } + llvm::dbgs() << "\t\t\n"; + } + llvm::dbgs() << "\t
"; + llvm::dbgs() << "" << operation->getName() << " (" << operation << ")"; + llvm::dbgs() << "
\n"; + llvm::dbgs() << ">];\n"; + + for (auto const& operand : node->getOperands()) { + llvm::dbgs() << "node_" << node << "->" << "node_" << operand << ";\n"; + worklist.emplace(operand); + } + } + llvm::dbgs() << "}\n"; +} diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp index 9c05bcd8..3cbf2af0 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp @@ -6,6 +6,7 @@ #include "LoSPN/LoSPNDialect.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" +#include "LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h" #include "LoSPNtoCPU/Vectorization/TargetInformation.h" #include "LoSPNtoCPU/LoSPNtoCPUTypeConverter.h" #include "LoSPNtoCPU/Vectorization/LoSPNVectorizationTypeConverter.h" @@ -104,70 +105,121 @@ void SLPVectorizationPass::runOnOperation() { auto seeds = seedAnalysis.getSeeds(width, seedAnalysis.getOpDepths(), SearchMode::UseBeforeDef); assert(!seeds.empty() && "couldn't find a seed!"); printSeedTree(seeds.front()); - SLPGraph graph(seeds.front(), 3); - transform(graph); + SLPGraphBuilder builder{3}; + auto graph = builder.build(seeds.front()); + transform(graph.get()); } -void SLPVectorizationPass::transform(SLPGraph const& graph) { - //graph.dump(); - std::map vectorsDone; - auto rootOperation = transform(graph.getRoot(), 0, vectorsDone); +void SLPVectorizationPass::transform(SLPNode* root) { + root->dumpGraph(); + std::map vectorsDone; + std::map nodeInputsDone; + auto rootOperation = transform(root, 0, vectorsDone, nodeInputsDone); + getOperation()->dump(); llvm::dbgs() << extractions.size() << "\n"; assert(false); // Update users of vector operations that aren't contained in the graph. } -mlir::Value SLPVectorizationPass::transform(SLPNode const& node, +mlir::Value SLPVectorizationPass::transform(SLPNode* node, size_t vectorIndex, - std::map& vectorsDone) { + std::map& vectorsDone, + std::map& nodeInputsDone) { - node.dump(); + node->dump(); llvm::dbgs() << "\n"; - if (std::uintptr_t(node.getOperation(0, 0)) == 0x716008) { + if (std::uintptr_t(node->getOperation(0, 0)) == 0x716008) { llvm::dbgs() << "\n"; } - vectorsDone[&node]++; + vectorsDone[node]++; - LoSPNVectorizationTypeConverter typeConverter{static_cast(node.numLanes())}; - auto vectorType = typeConverter.convertType(node.getResultType()); + LoSPNVectorizationTypeConverter typeConverter{static_cast(node->numLanes())}; + auto vectorType = typeConverter.convertType(node->getResultType()); auto builder = OpBuilder{&getContext()}; - auto const& vectorizableOps = node.getVector(vectorIndex); + auto const& vectorizableOps = node->getVector(vectorIndex); auto const& firstOp = vectorizableOps.front(); - builder.setInsertionPointAfter(firstOp); + auto const& loc = firstOp->getLoc(); + builder.setInsertionPoint(firstOp); if (isBroadcastable(vectorizableOps)) { auto value = firstOp->getResult(0); - auto vectorOp = builder.create(firstOp->getLoc(), vectorType, value); - return updateExtractions(node, vectorIndex, vectorOp)->getResult(0); + auto vectorOp = builder.create(loc, vectorType, value); + return applyCreation(node, vectorIndex, vectorOp); } if (areConsecutiveLoads(vectorizableOps)) { auto base = firstOp->getResult(0); - auto zero = builder.create(firstOp->getLoc(), builder.getIndexAttr(0)); + auto zero = builder.create(loc, builder.getIndexAttr(0)); auto indices = ValueRange{zero.getResult()}; - auto vectorOp = builder.create(firstOp->getLoc(), vectorType, base, indices); - return updateExtractions(node, vectorIndex, vectorOp)->getResult(0); + auto vectorOp = builder.create(loc, vectorType, base, indices); + return applyCreation(node, vectorIndex, vectorOp); } - if (node.isUniform()) { + if (node->isUniform() && firstOp->getNumOperands() > 0) { llvm::SmallVector operands; for (size_t i = 0; i < firstOp->getNumOperands(); ++i) { - size_t operandNodeIndex = vectorIndex % node.getOperands().size(); - auto* operandNode = (i == 0) ? &node : &node.getOperand(operandNodeIndex); - size_t nextIndex = vectorsDone[operandNode]; - while (nextIndex >= operandNode->numVectors()) { - operandNode = &node.getOperand(operandNodeIndex++ % node.getOperands().size()); - nextIndex = vectorsDone[operandNode]; + + if (node->numOperands() == 0) { + size_t nextIndex = vectorsDone[node]; + if (nextIndex < node->numVectors()) { + operands.emplace_back(transform(node, nextIndex, vectorsDone, nodeInputsDone)); + } else { + auto nodeInput = node->getNodeInput(nodeInputsDone[node]++); + nodeInput.dump(); + Operation* vectorOp = builder.create(loc, vectorType, nodeInput); + for (size_t lane = 1; lane < node->numLanes(); ++lane) { + nodeInput = node->getNodeInput(nodeInputsDone[node]++); + vectorOp = builder.create(vectorOp->getLoc(), + nodeInput, + vectorOp->getResult(0), + lane); + } + operands.emplace_back(applyCreation(node, vectorIndex, vectorOp)); + } + } else { + + size_t initialOperandIndex = vectorIndex % node->getOperands().size(); + + size_t operandNodeIndex = initialOperandIndex; + SLPNode* operandNode = (i == 0) ? node : node->getOperand(operandNodeIndex); + bool useNodeInputs = false; + + size_t nextVectorIndex = vectorsDone[operandNode]; + while (nextVectorIndex >= operandNode->numVectors()) { + operandNode = node->getOperand(operandNodeIndex % node->getOperands().size()); + nextVectorIndex = vectorsDone[operandNode]; + // Check if we have looped through all operands already. + if (operandNodeIndex++ == initialOperandIndex + node->numOperands()) { + useNodeInputs = true; + break; + } + } + + if (useNodeInputs) { + auto nodeInput = node->getNodeInput(nodeInputsDone[node]++); + nodeInput.dump(); + Operation* vectorOp = builder.create(loc, vectorType, nodeInput); + for (size_t lane = 1; lane < node->numLanes(); ++lane) { + nodeInput = node->getNodeInput(nodeInputsDone[node]++); + vectorOp = builder.create(vectorOp->getLoc(), + nodeInput, + vectorOp->getResult(0), + lane); + } + operands.emplace_back(applyCreation(node, vectorIndex, vectorOp)); + } else { + operands.emplace_back(transform(operandNode, nextVectorIndex, vectorsDone, nodeInputsDone)); + } + } - operands.emplace_back(transform(*operandNode, nextIndex, vectorsDone)); } - auto vectorOp = Operation::create(firstOp->getLoc(), + auto vectorOp = Operation::create(loc, firstOp->getName(), vectorType, operands, @@ -175,36 +227,36 @@ mlir::Value SLPVectorizationPass::transform(SLPNode const& node, firstOp->getSuccessors(), firstOp->getNumRegions()); builder.insert(vectorOp); - return updateExtractions(node, vectorIndex, vectorOp)->getResult(0); + return applyCreation(node, vectorIndex, vectorOp); } - Operation* vectorOp = builder.create(firstOp->getLoc(), vectorType, firstOp->getResult(0)); - for (size_t lane = 1; lane < node.numLanes(); ++lane) { + Operation* vectorOp = builder.create(loc, vectorType, firstOp->getResult(0)); + for (size_t lane = 1; lane < node->numLanes(); ++lane) { vectorOp = builder.create(vectorOp->getLoc(), vectorizableOps[lane]->getResult(0), vectorOp->getResult(0), lane); } - return updateExtractions(node, vectorIndex, vectorOp)->getResult(0); + return applyCreation(node, vectorIndex, vectorOp); } -mlir::Operation* SLPVectorizationPass::updateExtractions(SLPNode const& node, - size_t const& vectorIndex, - Operation* vectorOp) { - for (size_t lane = 0; lane < node.numLanes(); ++lane) { - auto* operation = node.getOperation(lane, vectorIndex); +mlir::Value SLPVectorizationPass::applyCreation(SLPNode* node, + size_t vectorIndex, + Operation* createdVectorOp) { + for (size_t lane = 0; lane < node->numLanes(); ++lane) { + auto* operation = node->getOperation(lane, vectorIndex); for (auto* use : operation->getUsers()) { for (size_t i = 0; i < use->getNumOperands(); ++i) { if (use->getOperand(i) == operation->getResult(0)) { - extractions[use][i] = std::make_pair(vectorOp, i); + extractions[use][i] = std::make_pair(createdVectorOp, i); break; } } } extractions.erase(operation); } - return vectorOp; + return createdVectorOp->getResult(0); } std::unique_ptr mlir::spn::createSLPVectorizationPass() { From ddfeea6b2acdffa1ccf9e47d892d78aefb12eccd Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sat, 27 Mar 2021 08:04:49 +0100 Subject: [PATCH 059/385] Formatting. --- .../LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp index 4006b5a7..53cbd31a 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp @@ -7,9 +7,8 @@ #include "llvm/Support/Debug.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" +#include "LoSPN/LoSPNTraits.h" #include -#include -#include using namespace mlir; using namespace mlir::spn::low::slp; @@ -82,8 +81,7 @@ namespace { } } // end namespace -void SLPGraphBuilder::buildGraph(std::vector const& operations, - SLPNode* currentNode) const { +void SLPGraphBuilder::buildGraph(std::vector const& operations, SLPNode* currentNode) const { // Stop growing graph if (!vectorizable(operations)) { return; From ee74a7eb720f1ebee7647675902fe9e5a93f198f Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sat, 3 Apr 2021 01:57:57 +0200 Subject: [PATCH 060/385] Added examples. --- mini-example.mlir | 29 + mini-example_blockargs.mlir | 60 ++ mini-example_blockargs_only.mlir | 50 ++ speaker.mlir | 1386 ++++++++++++++++++++++++++++++ 4 files changed, 1525 insertions(+) create mode 100644 mini-example.mlir create mode 100644 mini-example_blockargs.mlir create mode 100644 mini-example_blockargs_only.mlir create mode 100644 speaker.mlir diff --git a/mini-example.mlir b/mini-example.mlir new file mode 100644 index 00000000..ae45c2e8 --- /dev/null +++ b/mini-example.mlir @@ -0,0 +1,29 @@ +module { + "hi_spn.joint_query"() ( { + "hi_spn.graph"() ( { + ^bb0(%arg0: f64, %arg1: f64, %arg2: f64, %arg3: f64, %arg4: f64, %arg5: f64, %arg6: f64, %arg7: f64, %arg8: f64, %arg9: f64, %arg10: f64, %arg11: f64, %arg12: f64, %arg13: f64, %arg14: f64, %arg15: f64): // no predecessors + %0 = "hi_spn.gaussian"(%arg0) {mean = 1.100000e-01 : f64, stddev = 1.000000e+00 : f64} : (f64) -> !hi_spn.probability + %1 = "hi_spn.gaussian"(%arg1) {mean = 1.200000e-01 : f64, stddev = 7.500000e-01 : f64} : (f64) -> !hi_spn.probability + %2 = "hi_spn.gaussian"(%arg2) {mean = 1.300000e-01 : f64, stddev = 5.000000e-01 : f64} : (f64) -> !hi_spn.probability + %3 = "hi_spn.gaussian"(%arg3) {mean = 1.400000e-01 : f64, stddev = 2.500000e-01 : f64} : (f64) -> !hi_spn.probability + %4 = "hi_spn.product"(%0, %1, %2, %3) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %5 = "hi_spn.gaussian"(%arg4) {mean = 2.100000e-01 : f64, stddev = 1.000000e+00 : f64} : (f64) -> !hi_spn.probability + %6 = "hi_spn.gaussian"(%arg5) {mean = 2.200000e-01 : f64, stddev = 7.500000e-01 : f64} : (f64) -> !hi_spn.probability + %7 = "hi_spn.gaussian"(%arg6) {mean = 2.300000e-01 : f64, stddev = 5.000000e-01 : f64} : (f64) -> !hi_spn.probability + %8 = "hi_spn.gaussian"(%arg7) {mean = 2.400000e-01 : f64, stddev = 2.500000e-01 : f64} : (f64) -> !hi_spn.probability + %9 = "hi_spn.product"(%5, %6, %7, %8) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %10 = "hi_spn.gaussian"(%arg8) {mean = 3.100000e-01 : f64, stddev = 1.000000e+00 : f64} : (f64) -> !hi_spn.probability + %11 = "hi_spn.gaussian"(%arg9) {mean = 3.200000e-01 : f64, stddev = 7.500000e-01 : f64} : (f64) -> !hi_spn.probability + %12 = "hi_spn.gaussian"(%arg10) {mean = 3.300000e-01 : f64, stddev = 5.000000e-01 : f64} : (f64) -> !hi_spn.probability + %13 = "hi_spn.gaussian"(%arg11) {mean = 3.400000e-01 : f64, stddev = 2.500000e-01 : f64} : (f64) -> !hi_spn.probability + %14 = "hi_spn.product"(%10, %11, %12, %13) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %15 = "hi_spn.gaussian"(%arg12) {mean = 4.100000e-01 : f64, stddev = 1.000000e+00 : f64} : (f64) -> !hi_spn.probability + %16 = "hi_spn.gaussian"(%arg13) {mean = 4.200000e-01 : f64, stddev = 7.500000e-01 : f64} : (f64) -> !hi_spn.probability + %17 = "hi_spn.gaussian"(%arg14) {mean = 4.300000e-01 : f64, stddev = 5.000000e-01 : f64} : (f64) -> !hi_spn.probability + %18 = "hi_spn.gaussian"(%arg15) {mean = 4.400000e-01 : f64, stddev = 2.500000e-01 : f64} : (f64) -> !hi_spn.probability + %19 = "hi_spn.product"(%15, %16, %17, %18) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %20 = "hi_spn.sum"(%4, %9, %14, %19) {weights = [2.500000e-01, 2.500000e-01, 2.500000e-01, 2.500000e-01]} : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + "hi_spn.root"(%20) : (!hi_spn.probability) -> () + }) {numFeatures = 16 : ui32} : () -> () + }) {batchSize = 1 : ui32, errorModel = 1 : i32, inputType = f64, kernelName = "spn_vector", maxError = 2.000000e-02 : f64, numFeatures = 16 : ui32} : () -> () +} \ No newline at end of file diff --git a/mini-example_blockargs.mlir b/mini-example_blockargs.mlir new file mode 100644 index 00000000..c9279581 --- /dev/null +++ b/mini-example_blockargs.mlir @@ -0,0 +1,60 @@ +module { + "hi_spn.joint_query"() ( { + "hi_spn.graph"() ( { + ^bb0(%arg0: f64, %arg1: f64, %arg2: f64, %arg3: f64, %arg4: f64, %arg5: f64, %arg6: f64, %arg7: f64, %arg8: f64, %arg9: f64, %arg10: f64, %arg11: f64, %arg12: f64, %arg13: f64, %arg14: f64, %arg15: f64): // no predecessors + %0 = "lo_spn.gaussian"(%arg0) {mean = 1.100000e-01 : f64, stddev = 1.000000e+00 : f64, supportMarginal = false} : (f64) -> f64 + %1 = "lo_spn.gaussian"(%arg1) {mean = 1.200000e-01 : f64, stddev = 7.500000e-01 : f64, supportMarginal = false} : (f64) -> f64 + %2 = "lo_spn.gaussian"(%arg2) {mean = 1.300000e-01 : f64, stddev = 5.000000e-01 : f64, supportMarginal = false} : (f64) -> f64 + %3 = "lo_spn.gaussian"(%arg3) {mean = 1.400000e-01 : f64, stddev = 2.500000e-01 : f64, supportMarginal = false} : (f64) -> f64 + %4 = "lo_spn.mul"(%0, %1) : (f64, f64) -> f64 + %5 = "lo_spn.mul"(%2, %3) : (f64, f64) -> f64 + %6 = "lo_spn.mul"(%4, %5) : (f64, f64) -> f64 + + %7 = "lo_spn.gaussian"(%arg4) {mean = 2.100000e-01 : f64, stddev = 1.000000e+00 : f64, supportMarginal = false} : (f64) -> f64 + %8 = "lo_spn.gaussian"(%arg5) {mean = 2.200000e-01 : f64, stddev = 7.500000e-01 : f64, supportMarginal = false} : (f64) -> f64 + %9 = "lo_spn.gaussian"(%arg6) {mean = 2.300000e-01 : f64, stddev = 5.000000e-01 : f64, supportMarginal = false} : (f64) -> f64 + %10 = "lo_spn.gaussian"(%arg7) {mean = 2.400000e-01 : f64, stddev = 2.500000e-01 : f64, supportMarginal = false} : (f64) -> f64 + %11 = "lo_spn.mul"(%7, %8) : (f64, f64) -> f64 + %12 = "lo_spn.mul"(%9, %10) : (f64, f64) -> f64 + %13 = "lo_spn.mul"(%11, %12) : (f64, f64) -> f64 + + %14 = "lo_spn.mul"(%arg8, %arg9) : (f64, f64) -> f64 + %15 = "lo_spn.mul"(%arg10, %arg11) : (f64, f64) -> f64 + %16 = "lo_spn.mul"(%14, %15) : (f64, f64) -> f64 + + %17 = "lo_spn.gaussian"(%arg12) {mean = 4.100000e-01 : f64, stddev = 1.000000e+00 : f64, supportMarginal = false} : (f64) -> f64 + %18 = "lo_spn.gaussian"(%arg13) {mean = 4.200000e-01 : f64, stddev = 7.500000e-01 : f64, supportMarginal = false} : (f64) -> f64 + %19 = "lo_spn.mul"(%arg14, %17) : (f64, f64) -> f64 + %20 = "lo_spn.mul"(%18, %arg15) : (f64, f64) -> f64 + %21 = "lo_spn.mul"(%19, %20) : (f64, f64) -> f64 + + + %22 = "lo_spn.constant"() {type = f64, value = 2.500000e-01 : f64} : () -> f64 + %23 = "lo_spn.mul"(%6, %22) : (f64, f64) -> f64 + + + %24 = "lo_spn.constant"() {type = f64, value = 2.500000e-01 : f64} : () -> f64 + %25 = "lo_spn.mul"(%13, %24) : (f64, f64) -> f64 + + %26 = "lo_spn.add"(%23, %25) : (f64, f64) -> f64 + + + %27 = "lo_spn.constant"() {type = f64, value = 2.500000e-01 : f64} : () -> f64 + %28 = "lo_spn.mul"(%16, %27) : (f64, f64) -> f64 + + + %29 = "lo_spn.constant"() {type = f64, value = 2.500000e-01 : f64} : () -> f64 + %30 = "lo_spn.mul"(%21, %29) : (f64, f64) -> f64 + + + %31 = "lo_spn.add"(%28, %30) : (f64, f64) -> f64 + + + %32 = "lo_spn.add"(%26, %31) : (f64, f64) -> f64 + + + %33 = "lo_spn.log"(%32) : (f64) -> f64 + "lo_spn.yield"(%33) : (f64) -> () + }) {numFeatures = 16 : ui32} : () -> () + }) {batchSize = 1 : ui32, errorModel = 1 : i32, inputType = f64, kernelName = "spn_vector", maxError = 2.000000e-02 : f64, numFeatures = 16 : ui32} : () -> () +} \ No newline at end of file diff --git a/mini-example_blockargs_only.mlir b/mini-example_blockargs_only.mlir new file mode 100644 index 00000000..215d8606 --- /dev/null +++ b/mini-example_blockargs_only.mlir @@ -0,0 +1,50 @@ +module { + "hi_spn.joint_query"() ( { + "hi_spn.graph"() ( { + ^bb0(%arg0: f64, %arg1: f64, %arg2: f64, %arg3: f64, %arg4: f64, %arg5: f64, %arg6: f64, %arg7: f64, %arg8: f64, %arg9: f64, %arg10: f64, %arg11: f64, %arg12: f64, %arg13: f64, %arg14: f64, %arg15: f64): // no predecessors + %4 = "lo_spn.mul"(%arg0, %arg1) : (f64, f64) -> f64 + %5 = "lo_spn.mul"(%arg2, %arg3) : (f64, f64) -> f64 + %6 = "lo_spn.mul"(%4, %5) : (f64, f64) -> f64 + + %11 = "lo_spn.mul"(%arg4, %arg5) : (f64, f64) -> f64 + %12 = "lo_spn.mul"(%arg6, %arg7) : (f64, f64) -> f64 + %13 = "lo_spn.mul"(%11, %12) : (f64, f64) -> f64 + + %14 = "lo_spn.mul"(%arg8, %arg9) : (f64, f64) -> f64 + %15 = "lo_spn.mul"(%arg10, %arg11) : (f64, f64) -> f64 + %16 = "lo_spn.mul"(%14, %15) : (f64, f64) -> f64 + + %19 = "lo_spn.mul"(%arg12, %arg13) : (f64, f64) -> f64 + %20 = "lo_spn.mul"(%arg14, %arg15) : (f64, f64) -> f64 + %21 = "lo_spn.mul"(%19, %20) : (f64, f64) -> f64 + + + %22 = "lo_spn.constant"() {type = f64, value = 2.500000e-01 : f64} : () -> f64 + %23 = "lo_spn.mul"(%6, %22) : (f64, f64) -> f64 + + + %24 = "lo_spn.constant"() {type = f64, value = 2.500000e-01 : f64} : () -> f64 + %25 = "lo_spn.mul"(%13, %24) : (f64, f64) -> f64 + + %26 = "lo_spn.add"(%23, %25) : (f64, f64) -> f64 + + + %27 = "lo_spn.constant"() {type = f64, value = 2.500000e-01 : f64} : () -> f64 + %28 = "lo_spn.mul"(%16, %27) : (f64, f64) -> f64 + + + %29 = "lo_spn.constant"() {type = f64, value = 2.500000e-01 : f64} : () -> f64 + %30 = "lo_spn.mul"(%21, %29) : (f64, f64) -> f64 + + + %31 = "lo_spn.add"(%28, %30) : (f64, f64) -> f64 + + + %32 = "lo_spn.add"(%26, %31) : (f64, f64) -> f64 + + + %33 = "lo_spn.log"(%32) : (f64) -> f64 + "lo_spn.yield"(%33) : (f64) -> () + }) {numFeatures = 16 : ui32} : () -> () + }) {batchSize = 1 : ui32, errorModel = 1 : i32, inputType = f64, kernelName = "spn_vector", maxError = 2.000000e-02 : f64, numFeatures = 16 : ui32} : () -> () +} \ No newline at end of file diff --git a/speaker.mlir b/speaker.mlir new file mode 100644 index 00000000..831132f9 --- /dev/null +++ b/speaker.mlir @@ -0,0 +1,1386 @@ +module { + "hi_spn.joint_query"() ( { + "hi_spn.graph"() ( { + ^bb0(%arg0: f64, %arg1: f64, %arg2: f64, %arg3: f64, %arg4: f64, %arg5: f64, %arg6: f64, %arg7: f64, %arg8: f64, %arg9: f64, %arg10: f64, %arg11: f64, %arg12: f64, %arg13: f64, %arg14: f64, %arg15: f64, %arg16: f64, %arg17: f64, %arg18: f64, %arg19: f64, %arg20: f64, %arg21: f64, %arg22: f64, %arg23: f64, %arg24: f64, %arg25: f64): // no predecessors + %0 = "hi_spn.gaussian"(%arg0) {mean = -17.008344650268555 : f64, stddev = 1.2322918176651001 : f64} : (f64) -> !hi_spn.probability + %1 = "hi_spn.gaussian"(%arg1) {mean = -17.51667594909668 : f64, stddev = 0.83712685108184814 : f64} : (f64) -> !hi_spn.probability + %2 = "hi_spn.gaussian"(%arg2) {mean = -18.007946014404297 : f64, stddev = 0.80053895711898804 : f64} : (f64) -> !hi_spn.probability + %3 = "hi_spn.gaussian"(%arg3) {mean = -19.112228393554688 : f64, stddev = 1.1566277742385864 : f64} : (f64) -> !hi_spn.probability + %4 = "hi_spn.gaussian"(%arg4) {mean = -19.001440048217773 : f64, stddev = 1.4131922721862793 : f64} : (f64) -> !hi_spn.probability + %5 = "hi_spn.gaussian"(%arg5) {mean = -18.086399078369141 : f64, stddev = 1.3832533359527588 : f64} : (f64) -> !hi_spn.probability + %6 = "hi_spn.gaussian"(%arg6) {mean = -17.926259994506836 : f64, stddev = 1.826938271522522 : f64} : (f64) -> !hi_spn.probability + %7 = "hi_spn.gaussian"(%arg7) {mean = -17.839334487915039 : f64, stddev = 1.7523599863052368 : f64} : (f64) -> !hi_spn.probability + %8 = "hi_spn.gaussian"(%arg8) {mean = -17.57574462890625 : f64, stddev = 1.6010535955429077 : f64} : (f64) -> !hi_spn.probability + %9 = "hi_spn.gaussian"(%arg9) {mean = -17.946182250976563 : f64, stddev = 1.6547287702560425 : f64} : (f64) -> !hi_spn.probability + %10 = "hi_spn.gaussian"(%arg10) {mean = -18.620752334594727 : f64, stddev = 1.9146925210952759 : f64} : (f64) -> !hi_spn.probability + %11 = "hi_spn.gaussian"(%arg11) {mean = -19.343420028686523 : f64, stddev = 0.48160973191261292 : f64} : (f64) -> !hi_spn.probability + %12 = "hi_spn.gaussian"(%arg12) {mean = -18.635164260864258 : f64, stddev = 0.74348711967468262 : f64} : (f64) -> !hi_spn.probability + %13 = "hi_spn.gaussian"(%arg13) {mean = -18.961502075195313 : f64, stddev = 1.1531294584274292 : f64} : (f64) -> !hi_spn.probability + %14 = "hi_spn.gaussian"(%arg14) {mean = -19.183233261108398 : f64, stddev = 0.78505074977874755 : f64} : (f64) -> !hi_spn.probability + %15 = "hi_spn.gaussian"(%arg15) {mean = -19.083650588989258 : f64, stddev = 1.1194009780883789 : f64} : (f64) -> !hi_spn.probability + %16 = "hi_spn.gaussian"(%arg16) {mean = -18.983146667480469 : f64, stddev = 1.1276484727859497 : f64} : (f64) -> !hi_spn.probability + %17 = "hi_spn.gaussian"(%arg17) {mean = -19.001323699951172 : f64, stddev = 1.0364922285079956 : f64} : (f64) -> !hi_spn.probability + %18 = "hi_spn.gaussian"(%arg18) {mean = -19.279079437255859 : f64, stddev = 0.85322260856628418 : f64} : (f64) -> !hi_spn.probability + %19 = "hi_spn.gaussian"(%arg19) {mean = -18.933378219604492 : f64, stddev = 0.86298847198486328 : f64} : (f64) -> !hi_spn.probability + %20 = "hi_spn.gaussian"(%arg20) {mean = -19.706975936889648 : f64, stddev = 1.2289904356002808 : f64} : (f64) -> !hi_spn.probability + %21 = "hi_spn.gaussian"(%arg21) {mean = -20.154388427734375 : f64, stddev = 1.258030891418457 : f64} : (f64) -> !hi_spn.probability + %22 = "hi_spn.gaussian"(%arg22) {mean = -19.222892761230469 : f64, stddev = 0.88799136877059937 : f64} : (f64) -> !hi_spn.probability + %23 = "hi_spn.gaussian"(%arg23) {mean = -19.150226593017578 : f64, stddev = 1.1795535087585449 : f64} : (f64) -> !hi_spn.probability + %24 = "hi_spn.gaussian"(%arg24) {mean = -19.51652717590332 : f64, stddev = 1.4334899187088013 : f64} : (f64) -> !hi_spn.probability + %25 = "hi_spn.gaussian"(%arg25) {mean = -19.534236907958984 : f64, stddev = 1.4469033479690552 : f64} : (f64) -> !hi_spn.probability + %26 = "hi_spn.product"(%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %27 = "hi_spn.gaussian"(%arg0) {mean = -11.691486358642578 : f64, stddev = 0.9555012583732605 : f64} : (f64) -> !hi_spn.probability + %28 = "hi_spn.gaussian"(%arg1) {mean = -13.05181884765625 : f64, stddev = 0.853557288646698 : f64} : (f64) -> !hi_spn.probability + %29 = "hi_spn.gaussian"(%arg2) {mean = -12.82150936126709 : f64, stddev = 0.85313618183135986 : f64} : (f64) -> !hi_spn.probability + %30 = "hi_spn.gaussian"(%arg3) {mean = -12.261768341064453 : f64, stddev = 1.241553783416748 : f64} : (f64) -> !hi_spn.probability + %31 = "hi_spn.gaussian"(%arg4) {mean = -13.287029266357422 : f64, stddev = 0.94631612300872802 : f64} : (f64) -> !hi_spn.probability + %32 = "hi_spn.gaussian"(%arg5) {mean = -13.460000991821289 : f64, stddev = 0.79668718576431274 : f64} : (f64) -> !hi_spn.probability + %33 = "hi_spn.gaussian"(%arg6) {mean = -13.672939300537109 : f64, stddev = 0.64314866065979004 : f64} : (f64) -> !hi_spn.probability + %34 = "hi_spn.gaussian"(%arg7) {mean = -14.173128128051758 : f64, stddev = 0.6592138409614563 : f64} : (f64) -> !hi_spn.probability + %35 = "hi_spn.gaussian"(%arg8) {mean = -14.192937850952148 : f64, stddev = 0.66672509908676147 : f64} : (f64) -> !hi_spn.probability + %36 = "hi_spn.gaussian"(%arg9) {mean = -14.56330394744873 : f64, stddev = 0.62382203340530396 : f64} : (f64) -> !hi_spn.probability + %37 = "hi_spn.gaussian"(%arg10) {mean = -14.593165397644043 : f64, stddev = 0.60054910182952881 : f64} : (f64) -> !hi_spn.probability + %38 = "hi_spn.gaussian"(%arg11) {mean = -14.720823287963867 : f64, stddev = 0.3894939124584198 : f64} : (f64) -> !hi_spn.probability + %39 = "hi_spn.gaussian"(%arg12) {mean = -14.746555328369141 : f64, stddev = 0.33225619792938232 : f64} : (f64) -> !hi_spn.probability + %40 = "hi_spn.gaussian"(%arg13) {mean = -14.828187942504883 : f64, stddev = 0.45201334357261658 : f64} : (f64) -> !hi_spn.probability + %41 = "hi_spn.gaussian"(%arg14) {mean = -14.910123825073242 : f64, stddev = 0.39101448655128479 : f64} : (f64) -> !hi_spn.probability + %42 = "hi_spn.gaussian"(%arg15) {mean = -14.75367259979248 : f64, stddev = 0.39089170098304749 : f64} : (f64) -> !hi_spn.probability + %43 = "hi_spn.gaussian"(%arg16) {mean = -14.679332733154297 : f64, stddev = 0.45400753617286682 : f64} : (f64) -> !hi_spn.probability + %44 = "hi_spn.gaussian"(%arg17) {mean = -14.763216018676758 : f64, stddev = 0.37351292371749878 : f64} : (f64) -> !hi_spn.probability + %45 = "hi_spn.gaussian"(%arg18) {mean = -14.893037796020508 : f64, stddev = 0.38001608848571777 : f64} : (f64) -> !hi_spn.probability + %46 = "hi_spn.gaussian"(%arg19) {mean = -14.931450843811035 : f64, stddev = 0.3294711709022522 : f64} : (f64) -> !hi_spn.probability + %47 = "hi_spn.gaussian"(%arg20) {mean = -14.875664710998535 : f64, stddev = 0.28642129898071289 : f64} : (f64) -> !hi_spn.probability + %48 = "hi_spn.gaussian"(%arg21) {mean = -14.765348434448242 : f64, stddev = 0.30316087603569031 : f64} : (f64) -> !hi_spn.probability + %49 = "hi_spn.gaussian"(%arg22) {mean = -14.788684844970703 : f64, stddev = 0.46982544660568237 : f64} : (f64) -> !hi_spn.probability + %50 = "hi_spn.gaussian"(%arg23) {mean = -14.712668418884277 : f64, stddev = 0.69984894990921021 : f64} : (f64) -> !hi_spn.probability + %51 = "hi_spn.gaussian"(%arg24) {mean = -14.619635581970215 : f64, stddev = 0.6502000093460083 : f64} : (f64) -> !hi_spn.probability + %52 = "hi_spn.gaussian"(%arg25) {mean = -14.570056915283203 : f64, stddev = 0.77849054336547852 : f64} : (f64) -> !hi_spn.probability + %53 = "hi_spn.product"(%27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %54 = "hi_spn.gaussian"(%arg0) {mean = -14.705244064331055 : f64, stddev = 0.0081562995910644531 : f64} : (f64) -> !hi_spn.probability + %55 = "hi_spn.gaussian"(%arg1) {mean = -15.727216720581055 : f64, stddev = 0.46601295471191406 : f64} : (f64) -> !hi_spn.probability + %56 = "hi_spn.gaussian"(%arg2) {mean = -15.293563842773438 : f64, stddev = 0.76385116577148438 : f64} : (f64) -> !hi_spn.probability + %57 = "hi_spn.gaussian"(%arg3) {mean = -15.602492332458496 : f64, stddev = 1.0235662460327148 : f64} : (f64) -> !hi_spn.probability + %58 = "hi_spn.gaussian"(%arg4) {mean = -15.725742340087891 : f64, stddev = 0.058207988739013672 : f64} : (f64) -> !hi_spn.probability + %59 = "hi_spn.gaussian"(%arg5) {mean = -15.370358467102051 : f64, stddev = 0.21091938018798828 : f64} : (f64) -> !hi_spn.probability + %60 = "hi_spn.gaussian"(%arg6) {mean = -15.42354679107666 : f64, stddev = 0.043168067932128906 : f64} : (f64) -> !hi_spn.probability + %61 = "hi_spn.gaussian"(%arg7) {mean = -14.921604156494141 : f64, stddev = 0.013431072235107422 : f64} : (f64) -> !hi_spn.probability + %62 = "hi_spn.gaussian"(%arg8) {mean = -14.780827522277832 : f64, stddev = 0.23374462127685547 : f64} : (f64) -> !hi_spn.probability + %63 = "hi_spn.gaussian"(%arg9) {mean = -14.073673248291016 : f64, stddev = 0.50023126602172852 : f64} : (f64) -> !hi_spn.probability + %64 = "hi_spn.gaussian"(%arg10) {mean = -14.851292610168457 : f64, stddev = 1.1555185317993164 : f64} : (f64) -> !hi_spn.probability + %65 = "hi_spn.gaussian"(%arg11) {mean = -14.301946640014648 : f64, stddev = 0.36252355575561523 : f64} : (f64) -> !hi_spn.probability + %66 = "hi_spn.gaussian"(%arg12) {mean = -13.973554611206055 : f64, stddev = 0.11240434646606445 : f64} : (f64) -> !hi_spn.probability + %67 = "hi_spn.gaussian"(%arg13) {mean = -14.852415084838867 : f64, stddev = 0.75914144515991211 : f64} : (f64) -> !hi_spn.probability + %68 = "hi_spn.gaussian"(%arg14) {mean = -16.232814788818359 : f64, stddev = 1.2697248458862305 : f64} : (f64) -> !hi_spn.probability + %69 = "hi_spn.gaussian"(%arg15) {mean = -16.982036590576172 : f64, stddev = 0.39824199676513672 : f64} : (f64) -> !hi_spn.probability + %70 = "hi_spn.gaussian"(%arg16) {mean = -16.325704574584961 : f64, stddev = 0.52779197692871094 : f64} : (f64) -> !hi_spn.probability + %71 = "hi_spn.gaussian"(%arg17) {mean = -15.555004119873047 : f64, stddev = 0.54830408096313477 : f64} : (f64) -> !hi_spn.probability + %72 = "hi_spn.gaussian"(%arg18) {mean = -15.379636764526367 : f64, stddev = 1.258521556854248 : f64} : (f64) -> !hi_spn.probability + %73 = "hi_spn.gaussian"(%arg19) {mean = -15.570907592773438 : f64, stddev = 0.74476194381713867 : f64} : (f64) -> !hi_spn.probability + %74 = "hi_spn.gaussian"(%arg20) {mean = -17.015893936157227 : f64, stddev = 0.55658721923828125 : f64} : (f64) -> !hi_spn.probability + %75 = "hi_spn.gaussian"(%arg21) {mean = -17.795625686645508 : f64, stddev = 0.55001449584960938 : f64} : (f64) -> !hi_spn.probability + %76 = "hi_spn.gaussian"(%arg22) {mean = -17.125846862792969 : f64, stddev = 0.33589649200439453 : f64} : (f64) -> !hi_spn.probability + %77 = "hi_spn.gaussian"(%arg23) {mean = -17.304031372070313 : f64, stddev = 0.54722881317138672 : f64} : (f64) -> !hi_spn.probability + %78 = "hi_spn.gaussian"(%arg24) {mean = -18.064449310302734 : f64, stddev = 0.40894603729248047 : f64} : (f64) -> !hi_spn.probability + %79 = "hi_spn.gaussian"(%arg25) {mean = -17.569829940795898 : f64, stddev = 0.30439567565917969 : f64} : (f64) -> !hi_spn.probability + %80 = "hi_spn.product"(%54, %55, %56, %57, %58, %59, %60, %61, %62, %63, %64, %65, %66, %67, %68, %69, %70, %71, %72, %73, %74, %75, %76, %77, %78, %79) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %81 = "hi_spn.gaussian"(%arg0) {mean = -12.278669357299805 : f64, stddev = 0.70607328414916992 : f64} : (f64) -> !hi_spn.probability + %82 = "hi_spn.gaussian"(%arg1) {mean = -13.748607635498047 : f64, stddev = 0.59538811445236206 : f64} : (f64) -> !hi_spn.probability + %83 = "hi_spn.gaussian"(%arg2) {mean = -14.028950691223145 : f64, stddev = 0.57415688037872314 : f64} : (f64) -> !hi_spn.probability + %84 = "hi_spn.gaussian"(%arg3) {mean = -14.098723411560059 : f64, stddev = 0.68949049711227417 : f64} : (f64) -> !hi_spn.probability + %85 = "hi_spn.gaussian"(%arg4) {mean = -14.483530044555664 : f64, stddev = 0.5110512375831604 : f64} : (f64) -> !hi_spn.probability + %86 = "hi_spn.gaussian"(%arg5) {mean = -14.239527702331543 : f64, stddev = 0.45093455910682678 : f64} : (f64) -> !hi_spn.probability + %87 = "hi_spn.gaussian"(%arg6) {mean = -14.486002922058105 : f64, stddev = 0.65600454807281494 : f64} : (f64) -> !hi_spn.probability + %88 = "hi_spn.gaussian"(%arg7) {mean = -14.59147834777832 : f64, stddev = 0.57572072744369507 : f64} : (f64) -> !hi_spn.probability + %89 = "hi_spn.gaussian"(%arg8) {mean = -14.403834342956543 : f64, stddev = 0.46818867325782776 : f64} : (f64) -> !hi_spn.probability + %90 = "hi_spn.gaussian"(%arg9) {mean = -14.677604675292969 : f64, stddev = 0.47902008891105652 : f64} : (f64) -> !hi_spn.probability + %91 = "hi_spn.gaussian"(%arg10) {mean = -14.663904190063477 : f64, stddev = 0.50219011306762695 : f64} : (f64) -> !hi_spn.probability + %92 = "hi_spn.gaussian"(%arg11) {mean = -14.690372467041016 : f64, stddev = 0.44716405868530273 : f64} : (f64) -> !hi_spn.probability + %93 = "hi_spn.gaussian"(%arg12) {mean = -14.6632080078125 : f64, stddev = 0.38495475053787231 : f64} : (f64) -> !hi_spn.probability + %94 = "hi_spn.gaussian"(%arg13) {mean = -14.72645378112793 : f64, stddev = 0.55035674571990967 : f64} : (f64) -> !hi_spn.probability + %95 = "hi_spn.gaussian"(%arg14) {mean = -14.78395938873291 : f64, stddev = 0.54925638437271118 : f64} : (f64) -> !hi_spn.probability + %96 = "hi_spn.gaussian"(%arg15) {mean = -14.858743667602539 : f64, stddev = 0.53312361240386963 : f64} : (f64) -> !hi_spn.probability + %97 = "hi_spn.gaussian"(%arg16) {mean = -14.806643486022949 : f64, stddev = 0.4745364785194397 : f64} : (f64) -> !hi_spn.probability + %98 = "hi_spn.gaussian"(%arg17) {mean = -14.853967666625977 : f64, stddev = 0.43914061784744263 : f64} : (f64) -> !hi_spn.probability + %99 = "hi_spn.gaussian"(%arg18) {mean = -14.963318824768066 : f64, stddev = 0.33155733346939087 : f64} : (f64) -> !hi_spn.probability + %100 = "hi_spn.gaussian"(%arg19) {mean = -14.783291816711426 : f64, stddev = 0.37577581405639648 : f64} : (f64) -> !hi_spn.probability + %101 = "hi_spn.gaussian"(%arg20) {mean = -14.804089546203613 : f64, stddev = 0.32199788093566895 : f64} : (f64) -> !hi_spn.probability + %102 = "hi_spn.gaussian"(%arg21) {mean = -14.762737274169922 : f64, stddev = 0.3795318603515625 : f64} : (f64) -> !hi_spn.probability + %103 = "hi_spn.gaussian"(%arg22) {mean = -14.827932357788086 : f64, stddev = 0.29625844955444336 : f64} : (f64) -> !hi_spn.probability + %104 = "hi_spn.gaussian"(%arg23) {mean = -14.874870300292969 : f64, stddev = 0.31367933750152588 : f64} : (f64) -> !hi_spn.probability + %105 = "hi_spn.gaussian"(%arg24) {mean = -14.777741432189941 : f64, stddev = 0.4508415162563324 : f64} : (f64) -> !hi_spn.probability + %106 = "hi_spn.gaussian"(%arg25) {mean = -14.7742919921875 : f64, stddev = 0.40639781951904297 : f64} : (f64) -> !hi_spn.probability + %107 = "hi_spn.product"(%81, %82, %83, %84, %85, %86, %87, %88, %89, %90, %91, %92, %93, %94, %95, %96, %97, %98, %99, %100, %101, %102, %103, %104, %105, %106) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %108 = "hi_spn.gaussian"(%arg0) {mean = -12.921130180358887 : f64, stddev = 0.76062649488449097 : f64} : (f64) -> !hi_spn.probability + %109 = "hi_spn.gaussian"(%arg1) {mean = -13.843625068664551 : f64, stddev = 0.60068219900131226 : f64} : (f64) -> !hi_spn.probability + %110 = "hi_spn.gaussian"(%arg2) {mean = -13.933586120605469 : f64, stddev = 0.47099682688713074 : f64} : (f64) -> !hi_spn.probability + %111 = "hi_spn.gaussian"(%arg3) {mean = -14.109395027160645 : f64, stddev = 0.69455677270889282 : f64} : (f64) -> !hi_spn.probability + %112 = "hi_spn.gaussian"(%arg4) {mean = -14.586555480957031 : f64, stddev = 0.81965476274490356 : f64} : (f64) -> !hi_spn.probability + %113 = "hi_spn.gaussian"(%arg5) {mean = -14.383593559265137 : f64, stddev = 0.51352304220199585 : f64} : (f64) -> !hi_spn.probability + %114 = "hi_spn.gaussian"(%arg6) {mean = -14.25749683380127 : f64, stddev = 0.5128941535949707 : f64} : (f64) -> !hi_spn.probability + %115 = "hi_spn.gaussian"(%arg7) {mean = -13.105278015136719 : f64, stddev = 0.67038100957870483 : f64} : (f64) -> !hi_spn.probability + %116 = "hi_spn.gaussian"(%arg8) {mean = -12.834145545959473 : f64, stddev = 0.64336085319519043 : f64} : (f64) -> !hi_spn.probability + %117 = "hi_spn.gaussian"(%arg9) {mean = -12.958279609680176 : f64, stddev = 0.51751166582107544 : f64} : (f64) -> !hi_spn.probability + %118 = "hi_spn.gaussian"(%arg10) {mean = -12.984625816345215 : f64, stddev = 0.59040850400924683 : f64} : (f64) -> !hi_spn.probability + %119 = "hi_spn.gaussian"(%arg11) {mean = -13.200656890869141 : f64, stddev = 0.67291450500488281 : f64} : (f64) -> !hi_spn.probability + %120 = "hi_spn.gaussian"(%arg12) {mean = -13.749299049377441 : f64, stddev = 0.9121706485748291 : f64} : (f64) -> !hi_spn.probability + %121 = "hi_spn.gaussian"(%arg13) {mean = -14.392987251281738 : f64, stddev = 0.89220792055130005 : f64} : (f64) -> !hi_spn.probability + %122 = "hi_spn.gaussian"(%arg14) {mean = -14.678248405456543 : f64, stddev = 0.23509034514427185 : f64} : (f64) -> !hi_spn.probability + %123 = "hi_spn.gaussian"(%arg15) {mean = -14.928545951843262 : f64, stddev = 0.26878252625465393 : f64} : (f64) -> !hi_spn.probability + %124 = "hi_spn.gaussian"(%arg16) {mean = -15.254768371582031 : f64, stddev = 0.33298963308334351 : f64} : (f64) -> !hi_spn.probability + %125 = "hi_spn.gaussian"(%arg17) {mean = -14.660717964172363 : f64, stddev = 0.28881913423538208 : f64} : (f64) -> !hi_spn.probability + %126 = "hi_spn.gaussian"(%arg18) {mean = -14.636052131652832 : f64, stddev = 0.36257320642471313 : f64} : (f64) -> !hi_spn.probability + %127 = "hi_spn.gaussian"(%arg19) {mean = -14.544581413269043 : f64, stddev = 0.70322251319885254 : f64} : (f64) -> !hi_spn.probability + %128 = "hi_spn.gaussian"(%arg20) {mean = -14.446244239807129 : f64, stddev = 0.99685549736022949 : f64} : (f64) -> !hi_spn.probability + %129 = "hi_spn.gaussian"(%arg21) {mean = -14.390015602111816 : f64, stddev = 0.92761713266372681 : f64} : (f64) -> !hi_spn.probability + %130 = "hi_spn.gaussian"(%arg22) {mean = -14.498837471008301 : f64, stddev = 0.86803668737411499 : f64} : (f64) -> !hi_spn.probability + %131 = "hi_spn.gaussian"(%arg23) {mean = -14.460858345031738 : f64, stddev = 0.8387107253074646 : f64} : (f64) -> !hi_spn.probability + %132 = "hi_spn.gaussian"(%arg24) {mean = -14.527970314025879 : f64, stddev = 0.716655433177948 : f64} : (f64) -> !hi_spn.probability + %133 = "hi_spn.gaussian"(%arg25) {mean = -14.570904731750488 : f64, stddev = 0.35508885979652405 : f64} : (f64) -> !hi_spn.probability + %134 = "hi_spn.product"(%108, %109, %110, %111, %112, %113, %114, %115, %116, %117, %118, %119, %120, %121, %122, %123, %124, %125, %126, %127, %128, %129, %130, %131, %132, %133) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %135 = "hi_spn.gaussian"(%arg0) {mean = -12.10405158996582 : f64, stddev = 0.82932960987091064 : f64} : (f64) -> !hi_spn.probability + %136 = "hi_spn.gaussian"(%arg1) {mean = -12.611331939697266 : f64, stddev = 1.4384791851043701 : f64} : (f64) -> !hi_spn.probability + %137 = "hi_spn.gaussian"(%arg2) {mean = -11.826279640197754 : f64, stddev = 1.8871874809265137 : f64} : (f64) -> !hi_spn.probability + %138 = "hi_spn.gaussian"(%arg3) {mean = -11.32281494140625 : f64, stddev = 1.4461213350296021 : f64} : (f64) -> !hi_spn.probability + %139 = "hi_spn.gaussian"(%arg4) {mean = -12.309955596923828 : f64, stddev = 1.1334563493728638 : f64} : (f64) -> !hi_spn.probability + %140 = "hi_spn.gaussian"(%arg5) {mean = -12.109665870666504 : f64, stddev = 1.169951319694519 : f64} : (f64) -> !hi_spn.probability + %141 = "hi_spn.gaussian"(%arg6) {mean = -11.549976348876953 : f64, stddev = 1.2032864093780518 : f64} : (f64) -> !hi_spn.probability + %142 = "hi_spn.gaussian"(%arg7) {mean = -11.617506980895996 : f64, stddev = 1.1078034639358521 : f64} : (f64) -> !hi_spn.probability + %143 = "hi_spn.gaussian"(%arg8) {mean = -11.533123970031738 : f64, stddev = 1.0218119621276855 : f64} : (f64) -> !hi_spn.probability + %144 = "hi_spn.gaussian"(%arg9) {mean = -11.92768383026123 : f64, stddev = 0.98147124052047729 : f64} : (f64) -> !hi_spn.probability + %145 = "hi_spn.gaussian"(%arg10) {mean = -12.666173934936523 : f64, stddev = 1.1082369089126587 : f64} : (f64) -> !hi_spn.probability + %146 = "hi_spn.gaussian"(%arg11) {mean = -12.856383323669434 : f64, stddev = 1.4115911722183228 : f64} : (f64) -> !hi_spn.probability + %147 = "hi_spn.gaussian"(%arg12) {mean = -12.95670223236084 : f64, stddev = 1.2633929252624512 : f64} : (f64) -> !hi_spn.probability + %148 = "hi_spn.gaussian"(%arg13) {mean = -13.341944694519043 : f64, stddev = 1.0164026021957397 : f64} : (f64) -> !hi_spn.probability + %149 = "hi_spn.gaussian"(%arg14) {mean = -13.832265853881836 : f64, stddev = 0.93667340278625488 : f64} : (f64) -> !hi_spn.probability + %150 = "hi_spn.gaussian"(%arg15) {mean = -13.82805347442627 : f64, stddev = 0.89648616313934326 : f64} : (f64) -> !hi_spn.probability + %151 = "hi_spn.gaussian"(%arg16) {mean = -13.879281997680664 : f64, stddev = 0.82228487730026245 : f64} : (f64) -> !hi_spn.probability + %152 = "hi_spn.gaussian"(%arg17) {mean = -13.89295482635498 : f64, stddev = 0.8839956521987915 : f64} : (f64) -> !hi_spn.probability + %153 = "hi_spn.gaussian"(%arg18) {mean = -14.114192962646484 : f64, stddev = 0.78311645984649658 : f64} : (f64) -> !hi_spn.probability + %154 = "hi_spn.gaussian"(%arg19) {mean = -14.176742553710938 : f64, stddev = 0.80813091993331909 : f64} : (f64) -> !hi_spn.probability + %155 = "hi_spn.gaussian"(%arg20) {mean = -14.317062377929688 : f64, stddev = 0.52505064010620117 : f64} : (f64) -> !hi_spn.probability + %156 = "hi_spn.gaussian"(%arg21) {mean = -14.403012275695801 : f64, stddev = 0.63639742136001587 : f64} : (f64) -> !hi_spn.probability + %157 = "hi_spn.gaussian"(%arg22) {mean = -14.533612251281738 : f64, stddev = 0.64418482780456543 : f64} : (f64) -> !hi_spn.probability + %158 = "hi_spn.gaussian"(%arg23) {mean = -14.533967018127441 : f64, stddev = 0.48413392901420593 : f64} : (f64) -> !hi_spn.probability + %159 = "hi_spn.gaussian"(%arg24) {mean = -14.455240249633789 : f64, stddev = 0.55774885416030884 : f64} : (f64) -> !hi_spn.probability + %160 = "hi_spn.gaussian"(%arg25) {mean = -14.514117240905762 : f64, stddev = 0.56677824258804321 : f64} : (f64) -> !hi_spn.probability + %161 = "hi_spn.product"(%135, %136, %137, %138, %139, %140, %141, %142, %143, %144, %145, %146, %147, %148, %149, %150, %151, %152, %153, %154, %155, %156, %157, %158, %159, %160) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %162 = "hi_spn.gaussian"(%arg0) {mean = -11.645013809204102 : f64, stddev = 0.55360662937164307 : f64} : (f64) -> !hi_spn.probability + %163 = "hi_spn.gaussian"(%arg1) {mean = -13.282425880432129 : f64, stddev = 0.6206824779510498 : f64} : (f64) -> !hi_spn.probability + %164 = "hi_spn.gaussian"(%arg2) {mean = -12.816708564758301 : f64, stddev = 0.93745017051696777 : f64} : (f64) -> !hi_spn.probability + %165 = "hi_spn.gaussian"(%arg3) {mean = -12.827051162719727 : f64, stddev = 1.1103771924972534 : f64} : (f64) -> !hi_spn.probability + %166 = "hi_spn.gaussian"(%arg4) {mean = -13.382885932922363 : f64, stddev = 1.0659540891647339 : f64} : (f64) -> !hi_spn.probability + %167 = "hi_spn.gaussian"(%arg5) {mean = -13.313312530517578 : f64, stddev = 1.2071006298065186 : f64} : (f64) -> !hi_spn.probability + %168 = "hi_spn.gaussian"(%arg6) {mean = -13.945999145507813 : f64, stddev = 1.0026148557662964 : f64} : (f64) -> !hi_spn.probability + %169 = "hi_spn.gaussian"(%arg7) {mean = -14.185303688049316 : f64, stddev = 0.64159488677978516 : f64} : (f64) -> !hi_spn.probability + %170 = "hi_spn.gaussian"(%arg8) {mean = -14.098962783813477 : f64, stddev = 0.81748545169830322 : f64} : (f64) -> !hi_spn.probability + %171 = "hi_spn.gaussian"(%arg9) {mean = -14.06093692779541 : f64, stddev = 1.0089191198348999 : f64} : (f64) -> !hi_spn.probability + %172 = "hi_spn.gaussian"(%arg10) {mean = -13.906991004943848 : f64, stddev = 1.4146256446838379 : f64} : (f64) -> !hi_spn.probability + %173 = "hi_spn.gaussian"(%arg11) {mean = -13.577060699462891 : f64, stddev = 1.8225405216217041 : f64} : (f64) -> !hi_spn.probability + %174 = "hi_spn.gaussian"(%arg12) {mean = -13.554561614990234 : f64, stddev = 1.6641085147857666 : f64} : (f64) -> !hi_spn.probability + %175 = "hi_spn.gaussian"(%arg13) {mean = -13.616582870483398 : f64, stddev = 1.3857955932617188 : f64} : (f64) -> !hi_spn.probability + %176 = "hi_spn.gaussian"(%arg14) {mean = -13.666928291320801 : f64, stddev = 1.0615460872650146 : f64} : (f64) -> !hi_spn.probability + %177 = "hi_spn.gaussian"(%arg15) {mean = -13.642898559570313 : f64, stddev = 0.8511195182800293 : f64} : (f64) -> !hi_spn.probability + %178 = "hi_spn.gaussian"(%arg16) {mean = -13.469781875610352 : f64, stddev = 0.51911056041717529 : f64} : (f64) -> !hi_spn.probability + %179 = "hi_spn.gaussian"(%arg17) {mean = -12.935782432556152 : f64, stddev = 0.94953042268753052 : f64} : (f64) -> !hi_spn.probability + %180 = "hi_spn.gaussian"(%arg18) {mean = -12.66943359375 : f64, stddev = 1.2893918752670288 : f64} : (f64) -> !hi_spn.probability + %181 = "hi_spn.gaussian"(%arg19) {mean = -12.092894554138184 : f64, stddev = 1.3546910285949707 : f64} : (f64) -> !hi_spn.probability + %182 = "hi_spn.gaussian"(%arg20) {mean = -11.510499000549316 : f64, stddev = 1.5285800695419312 : f64} : (f64) -> !hi_spn.probability + %183 = "hi_spn.gaussian"(%arg21) {mean = -11.633353233337402 : f64, stddev = 1.6629343032836914 : f64} : (f64) -> !hi_spn.probability + %184 = "hi_spn.gaussian"(%arg22) {mean = -12.151246070861816 : f64, stddev = 1.6099048852920532 : f64} : (f64) -> !hi_spn.probability + %185 = "hi_spn.gaussian"(%arg23) {mean = -12.633271217346191 : f64, stddev = 1.4571080207824707 : f64} : (f64) -> !hi_spn.probability + %186 = "hi_spn.gaussian"(%arg24) {mean = -13.208540916442871 : f64, stddev = 1.2702630758285522 : f64} : (f64) -> !hi_spn.probability + %187 = "hi_spn.gaussian"(%arg25) {mean = -13.329287528991699 : f64, stddev = 1.1586692333221436 : f64} : (f64) -> !hi_spn.probability + %188 = "hi_spn.product"(%162, %163, %164, %165, %166, %167, %168, %169, %170, %171, %172, %173, %174, %175, %176, %177, %178, %179, %180, %181, %182, %183, %184, %185, %186, %187) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %189 = "hi_spn.gaussian"(%arg0) {mean = -10.88200569152832 : f64, stddev = 1.1144530773162842 : f64} : (f64) -> !hi_spn.probability + %190 = "hi_spn.gaussian"(%arg1) {mean = -5.9429030418395996 : f64, stddev = 1.6525270938873291 : f64} : (f64) -> !hi_spn.probability + %191 = "hi_spn.gaussian"(%arg2) {mean = -3.9489202499389648 : f64, stddev = 1.7254819869995117 : f64} : (f64) -> !hi_spn.probability + %192 = "hi_spn.gaussian"(%arg3) {mean = -5.4274463653564453 : f64, stddev = 1.4490045309066772 : f64} : (f64) -> !hi_spn.probability + %193 = "hi_spn.gaussian"(%arg4) {mean = -5.9817910194396973 : f64, stddev = 1.7104581594467163 : f64} : (f64) -> !hi_spn.probability + %194 = "hi_spn.gaussian"(%arg5) {mean = -6.8412342071533203 : f64, stddev = 1.4506911039352417 : f64} : (f64) -> !hi_spn.probability + %195 = "hi_spn.gaussian"(%arg6) {mean = -7.2832598686218262 : f64, stddev = 1.5448834896087646 : f64} : (f64) -> !hi_spn.probability + %196 = "hi_spn.gaussian"(%arg7) {mean = -8.3739805221557617 : f64, stddev = 1.3223825693130493 : f64} : (f64) -> !hi_spn.probability + %197 = "hi_spn.gaussian"(%arg8) {mean = -8.6566429138183594 : f64, stddev = 1.3076304197311401 : f64} : (f64) -> !hi_spn.probability + %198 = "hi_spn.gaussian"(%arg9) {mean = -9.6723222732543945 : f64, stddev = 1.7742989063262939 : f64} : (f64) -> !hi_spn.probability + %199 = "hi_spn.gaussian"(%arg10) {mean = -10.747444152832031 : f64, stddev = 1.3550804853439331 : f64} : (f64) -> !hi_spn.probability + %200 = "hi_spn.gaussian"(%arg11) {mean = -11.370630264282227 : f64, stddev = 1.2625734806060791 : f64} : (f64) -> !hi_spn.probability + %201 = "hi_spn.gaussian"(%arg12) {mean = -11.436898231506348 : f64, stddev = 1.2430018186569214 : f64} : (f64) -> !hi_spn.probability + %202 = "hi_spn.gaussian"(%arg13) {mean = -11.599303245544434 : f64, stddev = 1.2874772548675537 : f64} : (f64) -> !hi_spn.probability + %203 = "hi_spn.gaussian"(%arg14) {mean = -12.156070709228516 : f64, stddev = 1.2028912305831909 : f64} : (f64) -> !hi_spn.probability + %204 = "hi_spn.gaussian"(%arg15) {mean = -11.510618209838867 : f64, stddev = 1.06419837474823 : f64} : (f64) -> !hi_spn.probability + %205 = "hi_spn.gaussian"(%arg16) {mean = -10.697308540344238 : f64, stddev = 1.3114354610443115 : f64} : (f64) -> !hi_spn.probability + %206 = "hi_spn.gaussian"(%arg17) {mean = -11.247462272644043 : f64, stddev = 1.2970254421234131 : f64} : (f64) -> !hi_spn.probability + %207 = "hi_spn.gaussian"(%arg18) {mean = -12.315916061401367 : f64, stddev = 1.0127811431884766 : f64} : (f64) -> !hi_spn.probability + %208 = "hi_spn.gaussian"(%arg19) {mean = -12.817195892333984 : f64, stddev = 1.3591409921646118 : f64} : (f64) -> !hi_spn.probability + %209 = "hi_spn.gaussian"(%arg20) {mean = -12.901086807250977 : f64, stddev = 1.3990134000778198 : f64} : (f64) -> !hi_spn.probability + %210 = "hi_spn.gaussian"(%arg21) {mean = -13.055700302124023 : f64, stddev = 1.1038774251937866 : f64} : (f64) -> !hi_spn.probability + %211 = "hi_spn.gaussian"(%arg22) {mean = -12.81117057800293 : f64, stddev = 1.1016981601715088 : f64} : (f64) -> !hi_spn.probability + %212 = "hi_spn.gaussian"(%arg23) {mean = -12.502376556396484 : f64, stddev = 1.2441240549087524 : f64} : (f64) -> !hi_spn.probability + %213 = "hi_spn.gaussian"(%arg24) {mean = -12.834017753601074 : f64, stddev = 1.2123509645462036 : f64} : (f64) -> !hi_spn.probability + %214 = "hi_spn.gaussian"(%arg25) {mean = -13.285651206970215 : f64, stddev = 1.1948655843734741 : f64} : (f64) -> !hi_spn.probability + %215 = "hi_spn.product"(%189, %190, %191, %192, %193, %194, %195, %196, %197, %198, %199, %200, %201, %202, %203, %204, %205, %206, %207, %208, %209, %210, %211, %212, %213, %214) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %216 = "hi_spn.gaussian"(%arg0) {mean = -10.872588157653809 : f64, stddev = 0.81842154264450073 : f64} : (f64) -> !hi_spn.probability + %217 = "hi_spn.gaussian"(%arg1) {mean = -7.6911544799804688 : f64, stddev = 1.8710019588470459 : f64} : (f64) -> !hi_spn.probability + %218 = "hi_spn.gaussian"(%arg2) {mean = -5.7478251457214355 : f64, stddev = 1.5534957647323608 : f64} : (f64) -> !hi_spn.probability + %219 = "hi_spn.gaussian"(%arg3) {mean = -7.1911287307739258 : f64, stddev = 1.4221434593200684 : f64} : (f64) -> !hi_spn.probability + %220 = "hi_spn.gaussian"(%arg4) {mean = -8.3651208877563477 : f64, stddev = 1.1502407789230347 : f64} : (f64) -> !hi_spn.probability + %221 = "hi_spn.gaussian"(%arg5) {mean = -9.5288200378417968 : f64, stddev = 1.2969111204147339 : f64} : (f64) -> !hi_spn.probability + %222 = "hi_spn.gaussian"(%arg6) {mean = -9.9916448593139648 : f64, stddev = 1.0849907398223877 : f64} : (f64) -> !hi_spn.probability + %223 = "hi_spn.gaussian"(%arg7) {mean = -10.819472312927246 : f64, stddev = 1.4610934257507324 : f64} : (f64) -> !hi_spn.probability + %224 = "hi_spn.gaussian"(%arg8) {mean = -11.124005317687988 : f64, stddev = 1.4117463827133179 : f64} : (f64) -> !hi_spn.probability + %225 = "hi_spn.gaussian"(%arg9) {mean = -11.649805068969727 : f64, stddev = 1.3213168382644653 : f64} : (f64) -> !hi_spn.probability + %226 = "hi_spn.gaussian"(%arg10) {mean = -12.269238471984863 : f64, stddev = 1.2612044811248779 : f64} : (f64) -> !hi_spn.probability + %227 = "hi_spn.gaussian"(%arg11) {mean = -12.896105766296387 : f64, stddev = 0.73286539316177368 : f64} : (f64) -> !hi_spn.probability + %228 = "hi_spn.gaussian"(%arg12) {mean = -12.894018173217773 : f64, stddev = 0.98627632856369019 : f64} : (f64) -> !hi_spn.probability + %229 = "hi_spn.gaussian"(%arg13) {mean = -12.750127792358398 : f64, stddev = 1.3473232984542847 : f64} : (f64) -> !hi_spn.probability + %230 = "hi_spn.gaussian"(%arg14) {mean = -13.271978378295898 : f64, stddev = 1.2734342813491821 : f64} : (f64) -> !hi_spn.probability + %231 = "hi_spn.gaussian"(%arg15) {mean = -13.029507637023926 : f64, stddev = 0.91352295875549316 : f64} : (f64) -> !hi_spn.probability + %232 = "hi_spn.gaussian"(%arg16) {mean = -12.742123603820801 : f64, stddev = 1.1711235046386719 : f64} : (f64) -> !hi_spn.probability + %233 = "hi_spn.gaussian"(%arg17) {mean = -13.264754295349121 : f64, stddev = 1.1187813282012939 : f64} : (f64) -> !hi_spn.probability + %234 = "hi_spn.gaussian"(%arg18) {mean = -13.806636810302734 : f64, stddev = 0.92139971256256103 : f64} : (f64) -> !hi_spn.probability + %235 = "hi_spn.gaussian"(%arg19) {mean = -13.954047203063965 : f64, stddev = 1.0700047016143799 : f64} : (f64) -> !hi_spn.probability + %236 = "hi_spn.gaussian"(%arg20) {mean = -13.978344917297363 : f64, stddev = 0.80982613563537598 : f64} : (f64) -> !hi_spn.probability + %237 = "hi_spn.gaussian"(%arg21) {mean = -14.087822914123535 : f64, stddev = 0.5956883430480957 : f64} : (f64) -> !hi_spn.probability + %238 = "hi_spn.gaussian"(%arg22) {mean = -14.248385429382324 : f64, stddev = 0.45404589176177979 : f64} : (f64) -> !hi_spn.probability + %239 = "hi_spn.gaussian"(%arg23) {mean = -14.329046249389648 : f64, stddev = 0.45627853274345398 : f64} : (f64) -> !hi_spn.probability + %240 = "hi_spn.gaussian"(%arg24) {mean = -14.275317192077637 : f64, stddev = 0.56344431638717651 : f64} : (f64) -> !hi_spn.probability + %241 = "hi_spn.gaussian"(%arg25) {mean = -14.357979774475098 : f64, stddev = 0.54228466749191284 : f64} : (f64) -> !hi_spn.probability + %242 = "hi_spn.product"(%216, %217, %218, %219, %220, %221, %222, %223, %224, %225, %226, %227, %228, %229, %230, %231, %232, %233, %234, %235, %236, %237, %238, %239, %240, %241) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %243 = "hi_spn.gaussian"(%arg0) {mean = -10.695660591125488 : f64, stddev = 1.3020710945129395 : f64} : (f64) -> !hi_spn.probability + %244 = "hi_spn.gaussian"(%arg1) {mean = -10.313096046447754 : f64, stddev = 2.0571777820587158 : f64} : (f64) -> !hi_spn.probability + %245 = "hi_spn.gaussian"(%arg2) {mean = -9.8485498428344727 : f64, stddev = 2.2806837558746338 : f64} : (f64) -> !hi_spn.probability + %246 = "hi_spn.gaussian"(%arg3) {mean = -10.321578979492188 : f64, stddev = 1.6761202812194824 : f64} : (f64) -> !hi_spn.probability + %247 = "hi_spn.gaussian"(%arg4) {mean = -10.248523712158203 : f64, stddev = 1.3539624214172363 : f64} : (f64) -> !hi_spn.probability + %248 = "hi_spn.gaussian"(%arg5) {mean = -10.062666893005371 : f64, stddev = 1.4846806526184082 : f64} : (f64) -> !hi_spn.probability + %249 = "hi_spn.gaussian"(%arg6) {mean = -10.198581695556641 : f64, stddev = 1.4289246797561646 : f64} : (f64) -> !hi_spn.probability + %250 = "hi_spn.gaussian"(%arg7) {mean = -10.273982048034668 : f64, stddev = 1.217682957649231 : f64} : (f64) -> !hi_spn.probability + %251 = "hi_spn.gaussian"(%arg8) {mean = -10.277658462524414 : f64, stddev = 1.1663384437561035 : f64} : (f64) -> !hi_spn.probability + %252 = "hi_spn.gaussian"(%arg9) {mean = -10.304953575134277 : f64, stddev = 0.98772013187408447 : f64} : (f64) -> !hi_spn.probability + %253 = "hi_spn.gaussian"(%arg10) {mean = -10.429068565368652 : f64, stddev = 1.0546449422836304 : f64} : (f64) -> !hi_spn.probability + %254 = "hi_spn.gaussian"(%arg11) {mean = -10.55842399597168 : f64, stddev = 0.95481038093566894 : f64} : (f64) -> !hi_spn.probability + %255 = "hi_spn.gaussian"(%arg12) {mean = -10.577434539794922 : f64, stddev = 1.0489656925201416 : f64} : (f64) -> !hi_spn.probability + %256 = "hi_spn.gaussian"(%arg13) {mean = -10.329004287719727 : f64, stddev = 1.0989531278610229 : f64} : (f64) -> !hi_spn.probability + %257 = "hi_spn.gaussian"(%arg14) {mean = -10.406105995178223 : f64, stddev = 1.1858857870101929 : f64} : (f64) -> !hi_spn.probability + %258 = "hi_spn.gaussian"(%arg15) {mean = -10.330059051513672 : f64, stddev = 1.2221370935440063 : f64} : (f64) -> !hi_spn.probability + %259 = "hi_spn.gaussian"(%arg16) {mean = -10.164320945739746 : f64, stddev = 1.4290332794189453 : f64} : (f64) -> !hi_spn.probability + %260 = "hi_spn.gaussian"(%arg17) {mean = -9.737710952758789 : f64, stddev = 1.3526196479797363 : f64} : (f64) -> !hi_spn.probability + %261 = "hi_spn.gaussian"(%arg18) {mean = -9.929692268371582 : f64, stddev = 1.2603549957275391 : f64} : (f64) -> !hi_spn.probability + %262 = "hi_spn.gaussian"(%arg19) {mean = -9.7228202819824218 : f64, stddev = 1.0962318181991577 : f64} : (f64) -> !hi_spn.probability + %263 = "hi_spn.gaussian"(%arg20) {mean = -9.347783088684082 : f64, stddev = 1.1326436996459961 : f64} : (f64) -> !hi_spn.probability + %264 = "hi_spn.gaussian"(%arg21) {mean = -9.3500709533691406 : f64, stddev = 1.400260329246521 : f64} : (f64) -> !hi_spn.probability + %265 = "hi_spn.gaussian"(%arg22) {mean = -9.0539531707763671 : f64, stddev = 1.8767688274383545 : f64} : (f64) -> !hi_spn.probability + %266 = "hi_spn.gaussian"(%arg23) {mean = -9.1726865768432617 : f64, stddev = 2.1557025909423828 : f64} : (f64) -> !hi_spn.probability + %267 = "hi_spn.gaussian"(%arg24) {mean = -9.3399791717529296 : f64, stddev = 2.1112713813781738 : f64} : (f64) -> !hi_spn.probability + %268 = "hi_spn.gaussian"(%arg25) {mean = -9.4149303436279296 : f64, stddev = 2.0512642860412598 : f64} : (f64) -> !hi_spn.probability + %269 = "hi_spn.product"(%243, %244, %245, %246, %247, %248, %249, %250, %251, %252, %253, %254, %255, %256, %257, %258, %259, %260, %261, %262, %263, %264, %265, %266, %267, %268) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %270 = "hi_spn.gaussian"(%arg0) {mean = -11.011561393737793 : f64, stddev = 1.3464548587799072 : f64} : (f64) -> !hi_spn.probability + %271 = "hi_spn.gaussian"(%arg1) {mean = -11.484539985656738 : f64, stddev = 1.2907588481903076 : f64} : (f64) -> !hi_spn.probability + %272 = "hi_spn.gaussian"(%arg2) {mean = -11.140031814575195 : f64, stddev = 1.2217087745666504 : f64} : (f64) -> !hi_spn.probability + %273 = "hi_spn.gaussian"(%arg3) {mean = -10.571351051330566 : f64, stddev = 1.2158372402191162 : f64} : (f64) -> !hi_spn.probability + %274 = "hi_spn.gaussian"(%arg4) {mean = -10.876766204833984 : f64, stddev = 1.232560396194458 : f64} : (f64) -> !hi_spn.probability + %275 = "hi_spn.gaussian"(%arg5) {mean = -10.769608497619629 : f64, stddev = 1.1533514261245728 : f64} : (f64) -> !hi_spn.probability + %276 = "hi_spn.gaussian"(%arg6) {mean = -10.752179145812988 : f64, stddev = 1.0717220306396484 : f64} : (f64) -> !hi_spn.probability + %277 = "hi_spn.gaussian"(%arg7) {mean = -10.852066040039063 : f64, stddev = 0.95335924625396728 : f64} : (f64) -> !hi_spn.probability + %278 = "hi_spn.gaussian"(%arg8) {mean = -10.849024772644043 : f64, stddev = 0.91154670715332031 : f64} : (f64) -> !hi_spn.probability + %279 = "hi_spn.gaussian"(%arg9) {mean = -11.169876098632813 : f64, stddev = 0.97656524181365966 : f64} : (f64) -> !hi_spn.probability + %280 = "hi_spn.gaussian"(%arg10) {mean = -11.761531829833984 : f64, stddev = 0.99030452966690063 : f64} : (f64) -> !hi_spn.probability + %281 = "hi_spn.gaussian"(%arg11) {mean = -11.99782657623291 : f64, stddev = 1.4022659063339233 : f64} : (f64) -> !hi_spn.probability + %282 = "hi_spn.gaussian"(%arg12) {mean = -11.502655982971191 : f64, stddev = 1.48883056640625 : f64} : (f64) -> !hi_spn.probability + %283 = "hi_spn.gaussian"(%arg13) {mean = -11.587900161743164 : f64, stddev = 1.1238560676574707 : f64} : (f64) -> !hi_spn.probability + %284 = "hi_spn.gaussian"(%arg14) {mean = -12.02272891998291 : f64, stddev = 1.2540545463562012 : f64} : (f64) -> !hi_spn.probability + %285 = "hi_spn.gaussian"(%arg15) {mean = -12.395491600036621 : f64, stddev = 0.92785847187042236 : f64} : (f64) -> !hi_spn.probability + %286 = "hi_spn.gaussian"(%arg16) {mean = -12.362612724304199 : f64, stddev = 0.89953285455703735 : f64} : (f64) -> !hi_spn.probability + %287 = "hi_spn.gaussian"(%arg17) {mean = -12.037958145141602 : f64, stddev = 0.89549624919891357 : f64} : (f64) -> !hi_spn.probability + %288 = "hi_spn.gaussian"(%arg18) {mean = -12.650986671447754 : f64, stddev = 0.71532058715820313 : f64} : (f64) -> !hi_spn.probability + %289 = "hi_spn.gaussian"(%arg19) {mean = -12.813834190368652 : f64, stddev = 0.93615669012069702 : f64} : (f64) -> !hi_spn.probability + %290 = "hi_spn.gaussian"(%arg20) {mean = -12.59834098815918 : f64, stddev = 0.97324097156524658 : f64} : (f64) -> !hi_spn.probability + %291 = "hi_spn.gaussian"(%arg21) {mean = -12.942283630371094 : f64, stddev = 1.1216138601303101 : f64} : (f64) -> !hi_spn.probability + %292 = "hi_spn.gaussian"(%arg22) {mean = -13.136520385742188 : f64, stddev = 1.253537654876709 : f64} : (f64) -> !hi_spn.probability + %293 = "hi_spn.gaussian"(%arg23) {mean = -13.372512817382813 : f64, stddev = 1.1953029632568359 : f64} : (f64) -> !hi_spn.probability + %294 = "hi_spn.gaussian"(%arg24) {mean = -13.476689338684082 : f64, stddev = 1.1636662483215332 : f64} : (f64) -> !hi_spn.probability + %295 = "hi_spn.gaussian"(%arg25) {mean = -13.603638648986816 : f64, stddev = 1.1050925254821777 : f64} : (f64) -> !hi_spn.probability + %296 = "hi_spn.product"(%270, %271, %272, %273, %274, %275, %276, %277, %278, %279, %280, %281, %282, %283, %284, %285, %286, %287, %288, %289, %290, %291, %292, %293, %294, %295) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %297 = "hi_spn.gaussian"(%arg0) {mean = -10.536477088928223 : f64, stddev = 1.573858380317688 : f64} : (f64) -> !hi_spn.probability + %298 = "hi_spn.gaussian"(%arg1) {mean = -10.785096168518066 : f64, stddev = 1.39241623878479 : f64} : (f64) -> !hi_spn.probability + %299 = "hi_spn.gaussian"(%arg2) {mean = -10.532585144042969 : f64, stddev = 1.1160157918930054 : f64} : (f64) -> !hi_spn.probability + %300 = "hi_spn.gaussian"(%arg3) {mean = -10.119543075561523 : f64, stddev = 1.2580939531326294 : f64} : (f64) -> !hi_spn.probability + %301 = "hi_spn.gaussian"(%arg4) {mean = -9.9083328247070313 : f64, stddev = 1.3616124391555786 : f64} : (f64) -> !hi_spn.probability + %302 = "hi_spn.gaussian"(%arg5) {mean = -9.4178047180175781 : f64, stddev = 1.3389015197753906 : f64} : (f64) -> !hi_spn.probability + %303 = "hi_spn.gaussian"(%arg6) {mean = -9.1357297897338867 : f64, stddev = 1.4087536334991455 : f64} : (f64) -> !hi_spn.probability + %304 = "hi_spn.gaussian"(%arg7) {mean = -9.0847587585449219 : f64, stddev = 1.1018122434616089 : f64} : (f64) -> !hi_spn.probability + %305 = "hi_spn.gaussian"(%arg8) {mean = -8.7986536026000977 : f64, stddev = 0.91034013032913208 : f64} : (f64) -> !hi_spn.probability + %306 = "hi_spn.gaussian"(%arg9) {mean = -9.1316843032836914 : f64, stddev = 0.88567811250686646 : f64} : (f64) -> !hi_spn.probability + %307 = "hi_spn.gaussian"(%arg10) {mean = -9.8028707504272461 : f64, stddev = 1.1550403833389282 : f64} : (f64) -> !hi_spn.probability + %308 = "hi_spn.gaussian"(%arg11) {mean = -9.7117681503295898 : f64, stddev = 1.4467626810073853 : f64} : (f64) -> !hi_spn.probability + %309 = "hi_spn.gaussian"(%arg12) {mean = -9.5572147369384765 : f64, stddev = 1.0591788291931152 : f64} : (f64) -> !hi_spn.probability + %310 = "hi_spn.gaussian"(%arg13) {mean = -10.337271690368652 : f64, stddev = 0.95927858352661133 : f64} : (f64) -> !hi_spn.probability + %311 = "hi_spn.gaussian"(%arg14) {mean = -11.307088851928711 : f64, stddev = 1.2965620756149292 : f64} : (f64) -> !hi_spn.probability + %312 = "hi_spn.gaussian"(%arg15) {mean = -11.347599029541016 : f64, stddev = 1.291256308555603 : f64} : (f64) -> !hi_spn.probability + %313 = "hi_spn.gaussian"(%arg16) {mean = -10.912355422973633 : f64, stddev = 0.95412164926528931 : f64} : (f64) -> !hi_spn.probability + %314 = "hi_spn.gaussian"(%arg17) {mean = -11.113116264343262 : f64, stddev = 0.85015624761581421 : f64} : (f64) -> !hi_spn.probability + %315 = "hi_spn.gaussian"(%arg18) {mean = -12.123809814453125 : f64, stddev = 0.90956407785415649 : f64} : (f64) -> !hi_spn.probability + %316 = "hi_spn.gaussian"(%arg19) {mean = -12.09030818939209 : f64, stddev = 0.78027832508087158 : f64} : (f64) -> !hi_spn.probability + %317 = "hi_spn.gaussian"(%arg20) {mean = -12.204034805297852 : f64, stddev = 0.86473739147186279 : f64} : (f64) -> !hi_spn.probability + %318 = "hi_spn.gaussian"(%arg21) {mean = -12.697591781616211 : f64, stddev = 1.0442938804626465 : f64} : (f64) -> !hi_spn.probability + %319 = "hi_spn.gaussian"(%arg22) {mean = -13.195867538452148 : f64, stddev = 1.1451160907745361 : f64} : (f64) -> !hi_spn.probability + %320 = "hi_spn.gaussian"(%arg23) {mean = -13.544260025024414 : f64, stddev = 1.0079716444015503 : f64} : (f64) -> !hi_spn.probability + %321 = "hi_spn.gaussian"(%arg24) {mean = -13.197010040283203 : f64, stddev = 0.90521913766860962 : f64} : (f64) -> !hi_spn.probability + %322 = "hi_spn.gaussian"(%arg25) {mean = -13.540982246398926 : f64, stddev = 1.0950673818588257 : f64} : (f64) -> !hi_spn.probability + %323 = "hi_spn.product"(%297, %298, %299, %300, %301, %302, %303, %304, %305, %306, %307, %308, %309, %310, %311, %312, %313, %314, %315, %316, %317, %318, %319, %320, %321, %322) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %324 = "hi_spn.gaussian"(%arg0) {mean = -9.4802875518798828 : f64, stddev = 1.2016661167144775 : f64} : (f64) -> !hi_spn.probability + %325 = "hi_spn.gaussian"(%arg1) {mean = -4.8333950042724609 : f64, stddev = 1.481621265411377 : f64} : (f64) -> !hi_spn.probability + %326 = "hi_spn.gaussian"(%arg2) {mean = -1.2478560209274292 : f64, stddev = 0.49835783243179321 : f64} : (f64) -> !hi_spn.probability + %327 = "hi_spn.gaussian"(%arg3) {mean = -2.4360332489013672 : f64, stddev = 1.2078313827514648 : f64} : (f64) -> !hi_spn.probability + %328 = "hi_spn.gaussian"(%arg4) {mean = -0.28888186812400818 : f64, stddev = 1.7325667142868042 : f64} : (f64) -> !hi_spn.probability + %329 = "hi_spn.gaussian"(%arg5) {mean = 0.31940388679504395 : f64, stddev = 1.3050998449325562 : f64} : (f64) -> !hi_spn.probability + %330 = "hi_spn.gaussian"(%arg6) {mean = -1.7177269458770752 : f64, stddev = 1.3838937282562256 : f64} : (f64) -> !hi_spn.probability + %331 = "hi_spn.gaussian"(%arg7) {mean = -2.1905002593994141 : f64, stddev = 1.4356948137283325 : f64} : (f64) -> !hi_spn.probability + %332 = "hi_spn.gaussian"(%arg8) {mean = -3.170802116394043 : f64, stddev = 1.344483494758606 : f64} : (f64) -> !hi_spn.probability + %333 = "hi_spn.gaussian"(%arg9) {mean = -4.0211906433105469 : f64, stddev = 1.1359008550643921 : f64} : (f64) -> !hi_spn.probability + %334 = "hi_spn.gaussian"(%arg10) {mean = -3.9940578937530518 : f64, stddev = 0.93107867240905762 : f64} : (f64) -> !hi_spn.probability + %335 = "hi_spn.gaussian"(%arg11) {mean = -3.5179727077484131 : f64, stddev = 1.1148824691772461 : f64} : (f64) -> !hi_spn.probability + %336 = "hi_spn.gaussian"(%arg12) {mean = -2.2015244960784912 : f64, stddev = 1.1211410760879517 : f64} : (f64) -> !hi_spn.probability + %337 = "hi_spn.gaussian"(%arg13) {mean = -2.3716576099395752 : f64, stddev = 1.1725243330001831 : f64} : (f64) -> !hi_spn.probability + %338 = "hi_spn.gaussian"(%arg14) {mean = -3.6844854354858398 : f64, stddev = 1.1407406330108643 : f64} : (f64) -> !hi_spn.probability + %339 = "hi_spn.gaussian"(%arg15) {mean = -6.8022894859313965 : f64, stddev = 1.4141651391983032 : f64} : (f64) -> !hi_spn.probability + %340 = "hi_spn.gaussian"(%arg16) {mean = -9.1092815399169921 : f64, stddev = 1.0012867450714111 : f64} : (f64) -> !hi_spn.probability + %341 = "hi_spn.gaussian"(%arg17) {mean = -10.110542297363281 : f64, stddev = 0.93105554580688477 : f64} : (f64) -> !hi_spn.probability + %342 = "hi_spn.gaussian"(%arg18) {mean = -10.058772087097168 : f64, stddev = 1.3378287553787231 : f64} : (f64) -> !hi_spn.probability + %343 = "hi_spn.gaussian"(%arg19) {mean = -9.347132682800293 : f64, stddev = 1.3808602094650269 : f64} : (f64) -> !hi_spn.probability + %344 = "hi_spn.gaussian"(%arg20) {mean = -8.9064846038818359 : f64, stddev = 1.6692960262298584 : f64} : (f64) -> !hi_spn.probability + %345 = "hi_spn.gaussian"(%arg21) {mean = -8.9264822006225586 : f64, stddev = 1.3320252895355225 : f64} : (f64) -> !hi_spn.probability + %346 = "hi_spn.gaussian"(%arg22) {mean = -9.623652458190918 : f64, stddev = 1.2031350135803223 : f64} : (f64) -> !hi_spn.probability + %347 = "hi_spn.gaussian"(%arg23) {mean = -12.11909008026123 : f64, stddev = 1.0474261045455933 : f64} : (f64) -> !hi_spn.probability + %348 = "hi_spn.gaussian"(%arg24) {mean = -12.752231597900391 : f64, stddev = 0.98192590475082397 : f64} : (f64) -> !hi_spn.probability + %349 = "hi_spn.gaussian"(%arg25) {mean = -11.177624702453613 : f64, stddev = 1.1557686328887939 : f64} : (f64) -> !hi_spn.probability + %350 = "hi_spn.product"(%324, %325, %326, %327, %328, %329, %330, %331, %332, %333, %334, %335, %336, %337, %338, %339, %340, %341, %342, %343, %344, %345, %346, %347, %348, %349) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %351 = "hi_spn.gaussian"(%arg0) {mean = -10.244661331176758 : f64, stddev = 1.5316895246505737 : f64} : (f64) -> !hi_spn.probability + %352 = "hi_spn.gaussian"(%arg1) {mean = -5.1192898750305176 : f64, stddev = 1.5770797729492188 : f64} : (f64) -> !hi_spn.probability + %353 = "hi_spn.gaussian"(%arg2) {mean = -1.3837145566940308 : f64, stddev = 0.59317773580551147 : f64} : (f64) -> !hi_spn.probability + %354 = "hi_spn.gaussian"(%arg3) {mean = -2.3226404190063477 : f64, stddev = 1.696422815322876 : f64} : (f64) -> !hi_spn.probability + %355 = "hi_spn.gaussian"(%arg4) {mean = -1.0260239839553833 : f64, stddev = 1.2376000881195068 : f64} : (f64) -> !hi_spn.probability + %356 = "hi_spn.gaussian"(%arg5) {mean = -0.54633957147598267 : f64, stddev = 1.1976592540740967 : f64} : (f64) -> !hi_spn.probability + %357 = "hi_spn.gaussian"(%arg6) {mean = -0.7690434455871582 : f64, stddev = 0.94166147708892822 : f64} : (f64) -> !hi_spn.probability + %358 = "hi_spn.gaussian"(%arg7) {mean = -1.4979550838470459 : f64, stddev = 1.220375657081604 : f64} : (f64) -> !hi_spn.probability + %359 = "hi_spn.gaussian"(%arg8) {mean = -2.0289249420166016 : f64, stddev = 0.7188180685043335 : f64} : (f64) -> !hi_spn.probability + %360 = "hi_spn.gaussian"(%arg9) {mean = -2.5779931545257568 : f64, stddev = 0.65686935186386108 : f64} : (f64) -> !hi_spn.probability + %361 = "hi_spn.gaussian"(%arg10) {mean = -1.7427757978439331 : f64, stddev = 0.95791494846343994 : f64} : (f64) -> !hi_spn.probability + %362 = "hi_spn.gaussian"(%arg11) {mean = -1.9735974073410034 : f64, stddev = 1.132967472076416 : f64} : (f64) -> !hi_spn.probability + %363 = "hi_spn.gaussian"(%arg12) {mean = -3.0738620758056641 : f64, stddev = 1.1309664249420166 : f64} : (f64) -> !hi_spn.probability + %364 = "hi_spn.gaussian"(%arg13) {mean = -4.2539763450622559 : f64, stddev = 1.1536637544631958 : f64} : (f64) -> !hi_spn.probability + %365 = "hi_spn.gaussian"(%arg14) {mean = -7.3452010154724121 : f64, stddev = 1.3234764337539673 : f64} : (f64) -> !hi_spn.probability + %366 = "hi_spn.gaussian"(%arg15) {mean = -9.9497861862182617 : f64, stddev = 0.98592114448547363 : f64} : (f64) -> !hi_spn.probability + %367 = "hi_spn.gaussian"(%arg16) {mean = -11.16349983215332 : f64, stddev = 0.86218988895416259 : f64} : (f64) -> !hi_spn.probability + %368 = "hi_spn.gaussian"(%arg17) {mean = -11.97676944732666 : f64, stddev = 0.86789566278457642 : f64} : (f64) -> !hi_spn.probability + %369 = "hi_spn.gaussian"(%arg18) {mean = -11.755769729614258 : f64, stddev = 0.84353435039520263 : f64} : (f64) -> !hi_spn.probability + %370 = "hi_spn.gaussian"(%arg19) {mean = -10.544710159301758 : f64, stddev = 1.020220160484314 : f64} : (f64) -> !hi_spn.probability + %371 = "hi_spn.gaussian"(%arg20) {mean = -9.1729812622070313 : f64, stddev = 0.94360077381134033 : f64} : (f64) -> !hi_spn.probability + %372 = "hi_spn.gaussian"(%arg21) {mean = -9.0027523040771484 : f64, stddev = 1.1082609891891479 : f64} : (f64) -> !hi_spn.probability + %373 = "hi_spn.gaussian"(%arg22) {mean = -9.9451274871826171 : f64, stddev = 1.415675163269043 : f64} : (f64) -> !hi_spn.probability + %374 = "hi_spn.gaussian"(%arg23) {mean = -12.941132545471191 : f64, stddev = 0.8447113037109375 : f64} : (f64) -> !hi_spn.probability + %375 = "hi_spn.gaussian"(%arg24) {mean = -13.412550926208496 : f64, stddev = 0.82930845022201538 : f64} : (f64) -> !hi_spn.probability + %376 = "hi_spn.gaussian"(%arg25) {mean = -11.277895927429199 : f64, stddev = 0.90383189916610718 : f64} : (f64) -> !hi_spn.probability + %377 = "hi_spn.product"(%351, %352, %353, %354, %355, %356, %357, %358, %359, %360, %361, %362, %363, %364, %365, %366, %367, %368, %369, %370, %371, %372, %373, %374, %375, %376) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %378 = "hi_spn.gaussian"(%arg0) {mean = -9.3945894241333008 : f64, stddev = 1.1968786716461182 : f64} : (f64) -> !hi_spn.probability + %379 = "hi_spn.gaussian"(%arg1) {mean = -4.5067868232727051 : f64, stddev = 0.92036736011505126 : f64} : (f64) -> !hi_spn.probability + %380 = "hi_spn.gaussian"(%arg2) {mean = -1.3128589391708374 : f64, stddev = 0.34669303894042969 : f64} : (f64) -> !hi_spn.probability + %381 = "hi_spn.gaussian"(%arg3) {mean = -2.8367643356323242 : f64, stddev = 0.92023324966430664 : f64} : (f64) -> !hi_spn.probability + %382 = "hi_spn.gaussian"(%arg4) {mean = 0.02826932817697525 : f64, stddev = 0.86912816762924194 : f64} : (f64) -> !hi_spn.probability + %383 = "hi_spn.gaussian"(%arg5) {mean = 0.42621350288391113 : f64, stddev = 0.97154659032821655 : f64} : (f64) -> !hi_spn.probability + %384 = "hi_spn.gaussian"(%arg6) {mean = 0.5310712456703186 : f64, stddev = 0.87946623563766479 : f64} : (f64) -> !hi_spn.probability + %385 = "hi_spn.gaussian"(%arg7) {mean = 0.549427330493927 : f64, stddev = 0.85752266645431519 : f64} : (f64) -> !hi_spn.probability + %386 = "hi_spn.gaussian"(%arg8) {mean = -0.54700803756713867 : f64, stddev = 0.90864527225494384 : f64} : (f64) -> !hi_spn.probability + %387 = "hi_spn.gaussian"(%arg9) {mean = -1.1817671060562134 : f64, stddev = 0.8781394362449646 : f64} : (f64) -> !hi_spn.probability + %388 = "hi_spn.gaussian"(%arg10) {mean = -1.0398674011230469 : f64, stddev = 0.68396532535552979 : f64} : (f64) -> !hi_spn.probability + %389 = "hi_spn.gaussian"(%arg11) {mean = -1.3924421072006226 : f64, stddev = 1.0113506317138672 : f64} : (f64) -> !hi_spn.probability + %390 = "hi_spn.gaussian"(%arg12) {mean = -2.6821010112762451 : f64, stddev = 1.0329777002334595 : f64} : (f64) -> !hi_spn.probability + %391 = "hi_spn.gaussian"(%arg13) {mean = -3.5581924915313721 : f64, stddev = 0.92293047904968262 : f64} : (f64) -> !hi_spn.probability + %392 = "hi_spn.gaussian"(%arg14) {mean = -5.728360652923584 : f64, stddev = 1.4798064231872559 : f64} : (f64) -> !hi_spn.probability + %393 = "hi_spn.gaussian"(%arg15) {mean = -8.7816715240478515 : f64, stddev = 0.8383527398109436 : f64} : (f64) -> !hi_spn.probability + %394 = "hi_spn.gaussian"(%arg16) {mean = -10.316637992858887 : f64, stddev = 0.73171240091323853 : f64} : (f64) -> !hi_spn.probability + %395 = "hi_spn.gaussian"(%arg17) {mean = -10.848739624023438 : f64, stddev = 0.66426253318786621 : f64} : (f64) -> !hi_spn.probability + %396 = "hi_spn.gaussian"(%arg18) {mean = -10.831462860107422 : f64, stddev = 0.65740138292312622 : f64} : (f64) -> !hi_spn.probability + %397 = "hi_spn.gaussian"(%arg19) {mean = -8.9818572998046875 : f64, stddev = 0.73356473445892334 : f64} : (f64) -> !hi_spn.probability + %398 = "hi_spn.gaussian"(%arg20) {mean = -8.0323762893676758 : f64, stddev = 0.70900267362594604 : f64} : (f64) -> !hi_spn.probability + %399 = "hi_spn.gaussian"(%arg21) {mean = -9.0871553421020508 : f64, stddev = 0.94885432720184326 : f64} : (f64) -> !hi_spn.probability + %400 = "hi_spn.gaussian"(%arg22) {mean = -10.239433288574219 : f64, stddev = 1.1698378324508667 : f64} : (f64) -> !hi_spn.probability + %401 = "hi_spn.gaussian"(%arg23) {mean = -12.538901329040527 : f64, stddev = 0.85491478443145751 : f64} : (f64) -> !hi_spn.probability + %402 = "hi_spn.gaussian"(%arg24) {mean = -12.358306884765625 : f64, stddev = 0.91896212100982666 : f64} : (f64) -> !hi_spn.probability + %403 = "hi_spn.gaussian"(%arg25) {mean = -10.276165962219238 : f64, stddev = 0.65556037425994873 : f64} : (f64) -> !hi_spn.probability + %404 = "hi_spn.product"(%378, %379, %380, %381, %382, %383, %384, %385, %386, %387, %388, %389, %390, %391, %392, %393, %394, %395, %396, %397, %398, %399, %400, %401, %402, %403) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %405 = "hi_spn.gaussian"(%arg0) {mean = -9.4288492202758789 : f64, stddev = 1.8143206834793091 : f64} : (f64) -> !hi_spn.probability + %406 = "hi_spn.gaussian"(%arg1) {mean = -4.2685337066650391 : f64, stddev = 0.93882954120635986 : f64} : (f64) -> !hi_spn.probability + %407 = "hi_spn.gaussian"(%arg2) {mean = -1.8543741703033447 : f64, stddev = 1.1039572954177856 : f64} : (f64) -> !hi_spn.probability + %408 = "hi_spn.gaussian"(%arg3) {mean = -2.9633190631866455 : f64, stddev = 1.0525058507919312 : f64} : (f64) -> !hi_spn.probability + %409 = "hi_spn.gaussian"(%arg4) {mean = -0.82315641641616821 : f64, stddev = 1.6483795642852783 : f64} : (f64) -> !hi_spn.probability + %410 = "hi_spn.gaussian"(%arg5) {mean = -0.75296282768249512 : f64, stddev = 1.6455702781677246 : f64} : (f64) -> !hi_spn.probability + %411 = "hi_spn.gaussian"(%arg6) {mean = -1.2378108501434326 : f64, stddev = 0.94668835401535034 : f64} : (f64) -> !hi_spn.probability + %412 = "hi_spn.gaussian"(%arg7) {mean = -1.8991039991378784 : f64, stddev = 0.85058051347732544 : f64} : (f64) -> !hi_spn.probability + %413 = "hi_spn.gaussian"(%arg8) {mean = -2.8065030574798584 : f64, stddev = 0.73722350597381592 : f64} : (f64) -> !hi_spn.probability + %414 = "hi_spn.gaussian"(%arg9) {mean = -3.8870956897735596 : f64, stddev = 1.1277675628662109 : f64} : (f64) -> !hi_spn.probability + %415 = "hi_spn.gaussian"(%arg10) {mean = -4.2394628524780273 : f64, stddev = 1.2287005186080933 : f64} : (f64) -> !hi_spn.probability + %416 = "hi_spn.gaussian"(%arg11) {mean = -4.243431568145752 : f64, stddev = 1.211699366569519 : f64} : (f64) -> !hi_spn.probability + %417 = "hi_spn.gaussian"(%arg12) {mean = -4.6843423843383789 : f64, stddev = 1.2625079154968262 : f64} : (f64) -> !hi_spn.probability + %418 = "hi_spn.gaussian"(%arg13) {mean = -5.8308682441711426 : f64, stddev = 1.5575803518295288 : f64} : (f64) -> !hi_spn.probability + %419 = "hi_spn.gaussian"(%arg14) {mean = -7.33270263671875 : f64, stddev = 1.192712664604187 : f64} : (f64) -> !hi_spn.probability + %420 = "hi_spn.gaussian"(%arg15) {mean = -7.3210272789001465 : f64, stddev = 1.0465099811553955 : f64} : (f64) -> !hi_spn.probability + %421 = "hi_spn.gaussian"(%arg16) {mean = -6.8135166168212891 : f64, stddev = 1.0390324592590332 : f64} : (f64) -> !hi_spn.probability + %422 = "hi_spn.gaussian"(%arg17) {mean = -7.7793421745300293 : f64, stddev = 0.97518610954284667 : f64} : (f64) -> !hi_spn.probability + %423 = "hi_spn.gaussian"(%arg18) {mean = -8.819615364074707 : f64, stddev = 0.81093412637710571 : f64} : (f64) -> !hi_spn.probability + %424 = "hi_spn.gaussian"(%arg19) {mean = -8.5019330978393555 : f64, stddev = 0.91923016309738159 : f64} : (f64) -> !hi_spn.probability + %425 = "hi_spn.gaussian"(%arg20) {mean = -8.9022274017333984 : f64, stddev = 1.092939019203186 : f64} : (f64) -> !hi_spn.probability + %426 = "hi_spn.gaussian"(%arg21) {mean = -9.8470964431762695 : f64, stddev = 1.3017936944961548 : f64} : (f64) -> !hi_spn.probability + %427 = "hi_spn.gaussian"(%arg22) {mean = -10.357172966003418 : f64, stddev = 1.7064738273620605 : f64} : (f64) -> !hi_spn.probability + %428 = "hi_spn.gaussian"(%arg23) {mean = -10.793742179870605 : f64, stddev = 1.5534816980361938 : f64} : (f64) -> !hi_spn.probability + %429 = "hi_spn.gaussian"(%arg24) {mean = -11.999658584594727 : f64, stddev = 1.2650492191314697 : f64} : (f64) -> !hi_spn.probability + %430 = "hi_spn.gaussian"(%arg25) {mean = -10.905712127685547 : f64, stddev = 0.93530386686325073 : f64} : (f64) -> !hi_spn.probability + %431 = "hi_spn.product"(%405, %406, %407, %408, %409, %410, %411, %412, %413, %414, %415, %416, %417, %418, %419, %420, %421, %422, %423, %424, %425, %426, %427, %428, %429, %430) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %432 = "hi_spn.gaussian"(%arg0) {mean = -9.153386116027832 : f64, stddev = 1.4733750820159912 : f64} : (f64) -> !hi_spn.probability + %433 = "hi_spn.gaussian"(%arg1) {mean = -3.8400142192840576 : f64, stddev = 0.46866366267204285 : f64} : (f64) -> !hi_spn.probability + %434 = "hi_spn.gaussian"(%arg2) {mean = -1.40309739112854 : f64, stddev = 0.56098437309265137 : f64} : (f64) -> !hi_spn.probability + %435 = "hi_spn.gaussian"(%arg3) {mean = -3.2416553497314453 : f64, stddev = 0.82255464792251587 : f64} : (f64) -> !hi_spn.probability + %436 = "hi_spn.gaussian"(%arg4) {mean = -0.28104966878890991 : f64, stddev = 0.78045332431793212 : f64} : (f64) -> !hi_spn.probability + %437 = "hi_spn.gaussian"(%arg5) {mean = -0.58018600940704346 : f64, stddev = 1.198671817779541 : f64} : (f64) -> !hi_spn.probability + %438 = "hi_spn.gaussian"(%arg6) {mean = 0.62100893259048462 : f64, stddev = 0.88521146774291992 : f64} : (f64) -> !hi_spn.probability + %439 = "hi_spn.gaussian"(%arg7) {mean = 0.012893327511847019 : f64, stddev = 0.78984761238098145 : f64} : (f64) -> !hi_spn.probability + %440 = "hi_spn.gaussian"(%arg8) {mean = -0.4524359405040741 : f64, stddev = 1.5798650979995728 : f64} : (f64) -> !hi_spn.probability + %441 = "hi_spn.gaussian"(%arg9) {mean = -1.902164101600647 : f64, stddev = 1.214685320854187 : f64} : (f64) -> !hi_spn.probability + %442 = "hi_spn.gaussian"(%arg10) {mean = -2.2024378776550293 : f64, stddev = 1.068615198135376 : f64} : (f64) -> !hi_spn.probability + %443 = "hi_spn.gaussian"(%arg11) {mean = -1.830341100692749 : f64, stddev = 1.0227217674255371 : f64} : (f64) -> !hi_spn.probability + %444 = "hi_spn.gaussian"(%arg12) {mean = -2.9235310554504395 : f64, stddev = 1.1306984424591064 : f64} : (f64) -> !hi_spn.probability + %445 = "hi_spn.gaussian"(%arg13) {mean = -4.7543563842773438 : f64, stddev = 1.1527180671691895 : f64} : (f64) -> !hi_spn.probability + %446 = "hi_spn.gaussian"(%arg14) {mean = -6.138516902923584 : f64, stddev = 1.0490621328353882 : f64} : (f64) -> !hi_spn.probability + %447 = "hi_spn.gaussian"(%arg15) {mean = -6.1210379600524902 : f64, stddev = 1.08812415599823 : f64} : (f64) -> !hi_spn.probability + %448 = "hi_spn.gaussian"(%arg16) {mean = -6.0255622863769531 : f64, stddev = 1.1006985902786255 : f64} : (f64) -> !hi_spn.probability + %449 = "hi_spn.gaussian"(%arg17) {mean = -6.9525771141052246 : f64, stddev = 1.3719327449798584 : f64} : (f64) -> !hi_spn.probability + %450 = "hi_spn.gaussian"(%arg18) {mean = -8.1175413131713867 : f64, stddev = 1.0676044225692749 : f64} : (f64) -> !hi_spn.probability + %451 = "hi_spn.gaussian"(%arg19) {mean = -7.2368388175964355 : f64, stddev = 0.74669939279556274 : f64} : (f64) -> !hi_spn.probability + %452 = "hi_spn.gaussian"(%arg20) {mean = -7.8616113662719727 : f64, stddev = 0.86588001251220703 : f64} : (f64) -> !hi_spn.probability + %453 = "hi_spn.gaussian"(%arg21) {mean = -8.9879474639892578 : f64, stddev = 0.9161456823348999 : f64} : (f64) -> !hi_spn.probability + %454 = "hi_spn.gaussian"(%arg22) {mean = -9.705348014831543 : f64, stddev = 1.0296834707260132 : f64} : (f64) -> !hi_spn.probability + %455 = "hi_spn.gaussian"(%arg23) {mean = -10.282757759094238 : f64, stddev = 1.1020412445068359 : f64} : (f64) -> !hi_spn.probability + %456 = "hi_spn.gaussian"(%arg24) {mean = -11.667238235473633 : f64, stddev = 0.77787995338439941 : f64} : (f64) -> !hi_spn.probability + %457 = "hi_spn.gaussian"(%arg25) {mean = -10.359124183654785 : f64, stddev = 0.85552448034286499 : f64} : (f64) -> !hi_spn.probability + %458 = "hi_spn.product"(%432, %433, %434, %435, %436, %437, %438, %439, %440, %441, %442, %443, %444, %445, %446, %447, %448, %449, %450, %451, %452, %453, %454, %455, %456, %457) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %459 = "hi_spn.gaussian"(%arg0) {mean = -9.0204505920410156 : f64, stddev = 1.3453449010848999 : f64} : (f64) -> !hi_spn.probability + %460 = "hi_spn.gaussian"(%arg1) {mean = -4.8780498504638672 : f64, stddev = 1.2612115144729614 : f64} : (f64) -> !hi_spn.probability + %461 = "hi_spn.gaussian"(%arg2) {mean = -1.2555062770843506 : f64, stddev = 0.44067305326461792 : f64} : (f64) -> !hi_spn.probability + %462 = "hi_spn.gaussian"(%arg3) {mean = -2.5377500057220459 : f64, stddev = 0.85863637924194336 : f64} : (f64) -> !hi_spn.probability + %463 = "hi_spn.gaussian"(%arg4) {mean = -0.11212816834449768 : f64, stddev = 1.5364323854446411 : f64} : (f64) -> !hi_spn.probability + %464 = "hi_spn.gaussian"(%arg5) {mean = 0.75262928009033203 : f64, stddev = 1.1319175958633423 : f64} : (f64) -> !hi_spn.probability + %465 = "hi_spn.gaussian"(%arg6) {mean = -0.94725441932678222 : f64, stddev = 1.0431869029998779 : f64} : (f64) -> !hi_spn.probability + %466 = "hi_spn.gaussian"(%arg7) {mean = -0.84202224016189575 : f64, stddev = 1.2158206701278687 : f64} : (f64) -> !hi_spn.probability + %467 = "hi_spn.gaussian"(%arg8) {mean = -1.1578606367111206 : f64, stddev = 1.0248656272888184 : f64} : (f64) -> !hi_spn.probability + %468 = "hi_spn.gaussian"(%arg9) {mean = -1.6426748037338257 : f64, stddev = 0.93990784883499146 : f64} : (f64) -> !hi_spn.probability + %469 = "hi_spn.gaussian"(%arg10) {mean = -3.2194743156433105 : f64, stddev = 1.0770282745361328 : f64} : (f64) -> !hi_spn.probability + %470 = "hi_spn.gaussian"(%arg11) {mean = -5.7863335609436035 : f64, stddev = 1.0948958396911621 : f64} : (f64) -> !hi_spn.probability + %471 = "hi_spn.gaussian"(%arg12) {mean = -7.810737133026123 : f64, stddev = 0.83867120742797852 : f64} : (f64) -> !hi_spn.probability + %472 = "hi_spn.gaussian"(%arg13) {mean = -8.7175331115722656 : f64, stddev = 0.77042508125305176 : f64} : (f64) -> !hi_spn.probability + %473 = "hi_spn.gaussian"(%arg14) {mean = -9.799311637878418 : f64, stddev = 0.69780623912811279 : f64} : (f64) -> !hi_spn.probability + %474 = "hi_spn.gaussian"(%arg15) {mean = -9.6670436859130859 : f64, stddev = 0.85277092456817626 : f64} : (f64) -> !hi_spn.probability + %475 = "hi_spn.gaussian"(%arg16) {mean = -9.0194339752197265 : f64, stddev = 1.1196832656860352 : f64} : (f64) -> !hi_spn.probability + %476 = "hi_spn.gaussian"(%arg17) {mean = -7.5995402336120605 : f64, stddev = 0.94487285614013671 : f64} : (f64) -> !hi_spn.probability + %477 = "hi_spn.gaussian"(%arg18) {mean = -7.928624153137207 : f64, stddev = 1.1966493129730225 : f64} : (f64) -> !hi_spn.probability + %478 = "hi_spn.gaussian"(%arg19) {mean = -7.6685009002685546 : f64, stddev = 0.90138554573059082 : f64} : (f64) -> !hi_spn.probability + %479 = "hi_spn.gaussian"(%arg20) {mean = -7.9303784370422363 : f64, stddev = 0.80610108375549316 : f64} : (f64) -> !hi_spn.probability + %480 = "hi_spn.gaussian"(%arg21) {mean = -8.5051107406616211 : f64, stddev = 0.89962869882583618 : f64} : (f64) -> !hi_spn.probability + %481 = "hi_spn.gaussian"(%arg22) {mean = -8.5786447525024414 : f64, stddev = 1.384386420249939 : f64} : (f64) -> !hi_spn.probability + %482 = "hi_spn.gaussian"(%arg23) {mean = -9.5228633880615234 : f64, stddev = 1.6073061227798462 : f64} : (f64) -> !hi_spn.probability + %483 = "hi_spn.gaussian"(%arg24) {mean = -11.566178321838379 : f64, stddev = 1.0442947149276733 : f64} : (f64) -> !hi_spn.probability + %484 = "hi_spn.gaussian"(%arg25) {mean = -11.021327972412109 : f64, stddev = 0.45648452639579773 : f64} : (f64) -> !hi_spn.probability + %485 = "hi_spn.product"(%459, %460, %461, %462, %463, %464, %465, %466, %467, %468, %469, %470, %471, %472, %473, %474, %475, %476, %477, %478, %479, %480, %481, %482, %483, %484) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %486 = "hi_spn.gaussian"(%arg0) {mean = -10.476468086242676 : f64, stddev = 1.3651977777481079 : f64} : (f64) -> !hi_spn.probability + %487 = "hi_spn.gaussian"(%arg1) {mean = -4.1006040573120117 : f64, stddev = 0.78400856256484985 : f64} : (f64) -> !hi_spn.probability + %488 = "hi_spn.gaussian"(%arg2) {mean = -2.2106492519378662 : f64, stddev = 0.67379695177078247 : f64} : (f64) -> !hi_spn.probability + %489 = "hi_spn.gaussian"(%arg3) {mean = -4.1093616485595703 : f64, stddev = 0.78447991609573364 : f64} : (f64) -> !hi_spn.probability + %490 = "hi_spn.gaussian"(%arg4) {mean = -2.0314867496490479 : f64, stddev = 0.8128814697265625 : f64} : (f64) -> !hi_spn.probability + %491 = "hi_spn.gaussian"(%arg5) {mean = -2.4630053043365479 : f64, stddev = 0.50839203596115112 : f64} : (f64) -> !hi_spn.probability + %492 = "hi_spn.gaussian"(%arg6) {mean = -0.28948631882667542 : f64, stddev = 0.75648003816604614 : f64} : (f64) -> !hi_spn.probability + %493 = "hi_spn.gaussian"(%arg7) {mean = -0.58655941486358643 : f64, stddev = 0.55669635534286499 : f64} : (f64) -> !hi_spn.probability + %494 = "hi_spn.gaussian"(%arg8) {mean = -0.49795010685920715 : f64, stddev = 0.89552021026611328 : f64} : (f64) -> !hi_spn.probability + %495 = "hi_spn.gaussian"(%arg9) {mean = -1.4567451477050781 : f64, stddev = 0.95348000526428222 : f64} : (f64) -> !hi_spn.probability + %496 = "hi_spn.gaussian"(%arg10) {mean = -1.8157626390457153 : f64, stddev = 0.86164307594299316 : f64} : (f64) -> !hi_spn.probability + %497 = "hi_spn.gaussian"(%arg11) {mean = -3.3574516773223877 : f64, stddev = 1.1278407573699951 : f64} : (f64) -> !hi_spn.probability + %498 = "hi_spn.gaussian"(%arg12) {mean = -5.9180998802185059 : f64, stddev = 1.0012882947921753 : f64} : (f64) -> !hi_spn.probability + %499 = "hi_spn.gaussian"(%arg13) {mean = -7.2939929962158203 : f64, stddev = 0.78831362724304199 : f64} : (f64) -> !hi_spn.probability + %500 = "hi_spn.gaussian"(%arg14) {mean = -8.4359264373779296 : f64, stddev = 0.66446506977081299 : f64} : (f64) -> !hi_spn.probability + %501 = "hi_spn.gaussian"(%arg15) {mean = -8.2950983047485352 : f64, stddev = 0.72655582427978516 : f64} : (f64) -> !hi_spn.probability + %502 = "hi_spn.gaussian"(%arg16) {mean = -8.6237173080444336 : f64, stddev = 0.69902801513671875 : f64} : (f64) -> !hi_spn.probability + %503 = "hi_spn.gaussian"(%arg17) {mean = -7.651552677154541 : f64, stddev = 1.2274740934371948 : f64} : (f64) -> !hi_spn.probability + %504 = "hi_spn.gaussian"(%arg18) {mean = -6.9707551002502441 : f64, stddev = 0.67151403427124023 : f64} : (f64) -> !hi_spn.probability + %505 = "hi_spn.gaussian"(%arg19) {mean = -6.990541934967041 : f64, stddev = 0.91399347782135009 : f64} : (f64) -> !hi_spn.probability + %506 = "hi_spn.gaussian"(%arg20) {mean = -7.0826106071472168 : f64, stddev = 0.6609991192817688 : f64} : (f64) -> !hi_spn.probability + %507 = "hi_spn.gaussian"(%arg21) {mean = -9.092233657836914 : f64, stddev = 0.96371352672576904 : f64} : (f64) -> !hi_spn.probability + %508 = "hi_spn.gaussian"(%arg22) {mean = -8.4635486602783203 : f64, stddev = 0.843517005443573 : f64} : (f64) -> !hi_spn.probability + %509 = "hi_spn.gaussian"(%arg23) {mean = -9.4607295989990234 : f64, stddev = 1.4316180944442749 : f64} : (f64) -> !hi_spn.probability + %510 = "hi_spn.gaussian"(%arg24) {mean = -11.996702194213867 : f64, stddev = 1.0481323003768921 : f64} : (f64) -> !hi_spn.probability + %511 = "hi_spn.gaussian"(%arg25) {mean = -11.253396034240723 : f64, stddev = 0.84694206714630126 : f64} : (f64) -> !hi_spn.probability + %512 = "hi_spn.product"(%486, %487, %488, %489, %490, %491, %492, %493, %494, %495, %496, %497, %498, %499, %500, %501, %502, %503, %504, %505, %506, %507, %508, %509, %510, %511) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %513 = "hi_spn.gaussian"(%arg0) {mean = -9.1537408828735352 : f64, stddev = 1.3647329807281494 : f64} : (f64) -> !hi_spn.probability + %514 = "hi_spn.gaussian"(%arg1) {mean = -4.1984939575195313 : f64, stddev = 0.90464174747467041 : f64} : (f64) -> !hi_spn.probability + %515 = "hi_spn.gaussian"(%arg2) {mean = -1.3003273010253906 : f64, stddev = 0.49154952168464661 : f64} : (f64) -> !hi_spn.probability + %516 = "hi_spn.gaussian"(%arg3) {mean = -2.9839680194854736 : f64, stddev = 0.84153890609741211 : f64} : (f64) -> !hi_spn.probability + %517 = "hi_spn.gaussian"(%arg4) {mean = -0.32501798868179321 : f64, stddev = 0.76971489191055298 : f64} : (f64) -> !hi_spn.probability + %518 = "hi_spn.gaussian"(%arg5) {mean = -0.18823693692684174 : f64, stddev = 1.2353429794311523 : f64} : (f64) -> !hi_spn.probability + %519 = "hi_spn.gaussian"(%arg6) {mean = 0.65303677320480347 : f64, stddev = 0.63579827547073364 : f64} : (f64) -> !hi_spn.probability + %520 = "hi_spn.gaussian"(%arg7) {mean = 0.5496094822883606 : f64, stddev = 0.89253050088882446 : f64} : (f64) -> !hi_spn.probability + %521 = "hi_spn.gaussian"(%arg8) {mean = 0.25877198576927185 : f64, stddev = 1.3025667667388916 : f64} : (f64) -> !hi_spn.probability + %522 = "hi_spn.gaussian"(%arg9) {mean = -0.83687865734100341 : f64, stddev = 1.0308387279510498 : f64} : (f64) -> !hi_spn.probability + %523 = "hi_spn.gaussian"(%arg10) {mean = -1.1256893873214722 : f64, stddev = 0.7142026424407959 : f64} : (f64) -> !hi_spn.probability + %524 = "hi_spn.gaussian"(%arg11) {mean = -2.6882007122039795 : f64, stddev = 1.3497539758682251 : f64} : (f64) -> !hi_spn.probability + %525 = "hi_spn.gaussian"(%arg12) {mean = -5.2889409065246582 : f64, stddev = 0.75630968809127808 : f64} : (f64) -> !hi_spn.probability + %526 = "hi_spn.gaussian"(%arg13) {mean = -6.3619189262390137 : f64, stddev = 0.64617186784744263 : f64} : (f64) -> !hi_spn.probability + %527 = "hi_spn.gaussian"(%arg14) {mean = -7.6186404228210449 : f64, stddev = 0.81008929014205933 : f64} : (f64) -> !hi_spn.probability + %528 = "hi_spn.gaussian"(%arg15) {mean = -7.6665201187133789 : f64, stddev = 1.1533548831939697 : f64} : (f64) -> !hi_spn.probability + %529 = "hi_spn.gaussian"(%arg16) {mean = -7.0475645065307617 : f64, stddev = 1.101231575012207 : f64} : (f64) -> !hi_spn.probability + %530 = "hi_spn.gaussian"(%arg17) {mean = -6.3889970779418945 : f64, stddev = 1.2492735385894775 : f64} : (f64) -> !hi_spn.probability + %531 = "hi_spn.gaussian"(%arg18) {mean = -7.1448163986206055 : f64, stddev = 1.2098462581634521 : f64} : (f64) -> !hi_spn.probability + %532 = "hi_spn.gaussian"(%arg19) {mean = -6.9379415512084961 : f64, stddev = 0.95443522930145263 : f64} : (f64) -> !hi_spn.probability + %533 = "hi_spn.gaussian"(%arg20) {mean = -6.9723100662231445 : f64, stddev = 0.85347449779510498 : f64} : (f64) -> !hi_spn.probability + %534 = "hi_spn.gaussian"(%arg21) {mean = -8.2871971130371094 : f64, stddev = 0.92305731773376465 : f64} : (f64) -> !hi_spn.probability + %535 = "hi_spn.gaussian"(%arg22) {mean = -8.8254022598266602 : f64, stddev = 1.0520280599594116 : f64} : (f64) -> !hi_spn.probability + %536 = "hi_spn.gaussian"(%arg23) {mean = -9.9435014724731445 : f64, stddev = 1.1024428606033325 : f64} : (f64) -> !hi_spn.probability + %537 = "hi_spn.gaussian"(%arg24) {mean = -11.73024845123291 : f64, stddev = 0.99935430288314819 : f64} : (f64) -> !hi_spn.probability + %538 = "hi_spn.gaussian"(%arg25) {mean = -10.479912757873535 : f64, stddev = 1.0445784330368042 : f64} : (f64) -> !hi_spn.probability + %539 = "hi_spn.product"(%513, %514, %515, %516, %517, %518, %519, %520, %521, %522, %523, %524, %525, %526, %527, %528, %529, %530, %531, %532, %533, %534, %535, %536, %537, %538) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %540 = "hi_spn.gaussian"(%arg0) {mean = -8.7692985534667969 : f64, stddev = 1.5315520763397217 : f64} : (f64) -> !hi_spn.probability + %541 = "hi_spn.gaussian"(%arg1) {mean = -6.1080245971679688 : f64, stddev = 1.5822528600692749 : f64} : (f64) -> !hi_spn.probability + %542 = "hi_spn.gaussian"(%arg2) {mean = -0.8311806321144104 : f64, stddev = 0.56920838356018066 : f64} : (f64) -> !hi_spn.probability + %543 = "hi_spn.gaussian"(%arg3) {mean = -1.4660282135009766 : f64, stddev = 1.3357874155044556 : f64} : (f64) -> !hi_spn.probability + %544 = "hi_spn.gaussian"(%arg4) {mean = -1.8440208435058594 : f64, stddev = 1.7737078666687012 : f64} : (f64) -> !hi_spn.probability + %545 = "hi_spn.gaussian"(%arg5) {mean = 0.92526417970657349 : f64, stddev = 1.371618390083313 : f64} : (f64) -> !hi_spn.probability + %546 = "hi_spn.gaussian"(%arg6) {mean = 0.0046593393199145794 : f64, stddev = 1.2753709554672241 : f64} : (f64) -> !hi_spn.probability + %547 = "hi_spn.gaussian"(%arg7) {mean = 0.95336782932281494 : f64, stddev = 1.166822075843811 : f64} : (f64) -> !hi_spn.probability + %548 = "hi_spn.gaussian"(%arg8) {mean = -0.21680940687656403 : f64, stddev = 1.4069637060165405 : f64} : (f64) -> !hi_spn.probability + %549 = "hi_spn.gaussian"(%arg9) {mean = -1.6752274036407471 : f64, stddev = 1.0960190296173096 : f64} : (f64) -> !hi_spn.probability + %550 = "hi_spn.gaussian"(%arg10) {mean = -3.2760834693908691 : f64, stddev = 1.1606998443603516 : f64} : (f64) -> !hi_spn.probability + %551 = "hi_spn.gaussian"(%arg11) {mean = -3.5717382431030273 : f64, stddev = 1.3427019119262695 : f64} : (f64) -> !hi_spn.probability + %552 = "hi_spn.gaussian"(%arg12) {mean = -2.9794425964355469 : f64, stddev = 1.4287483692169189 : f64} : (f64) -> !hi_spn.probability + %553 = "hi_spn.gaussian"(%arg13) {mean = -2.5298645496368408 : f64, stddev = 1.3399711847305298 : f64} : (f64) -> !hi_spn.probability + %554 = "hi_spn.gaussian"(%arg14) {mean = -3.2623114585876465 : f64, stddev = 1.5035918951034546 : f64} : (f64) -> !hi_spn.probability + %555 = "hi_spn.gaussian"(%arg15) {mean = -4.2148947715759277 : f64, stddev = 0.84420418739318848 : f64} : (f64) -> !hi_spn.probability + %556 = "hi_spn.gaussian"(%arg16) {mean = -3.6074843406677246 : f64, stddev = 0.74930191040039063 : f64} : (f64) -> !hi_spn.probability + %557 = "hi_spn.gaussian"(%arg17) {mean = -3.4165441989898682 : f64, stddev = 0.76345783472061157 : f64} : (f64) -> !hi_spn.probability + %558 = "hi_spn.gaussian"(%arg18) {mean = -5.0396256446838379 : f64, stddev = 0.99241995811462402 : f64} : (f64) -> !hi_spn.probability + %559 = "hi_spn.gaussian"(%arg19) {mean = -5.4429912567138672 : f64, stddev = 0.7015453577041626 : f64} : (f64) -> !hi_spn.probability + %560 = "hi_spn.gaussian"(%arg20) {mean = -5.2176666259765625 : f64, stddev = 1.0081747770309448 : f64} : (f64) -> !hi_spn.probability + %561 = "hi_spn.gaussian"(%arg21) {mean = -5.9505085945129395 : f64, stddev = 0.90125596523284912 : f64} : (f64) -> !hi_spn.probability + %562 = "hi_spn.gaussian"(%arg22) {mean = -6.3531856536865234 : f64, stddev = 0.87796205282211304 : f64} : (f64) -> !hi_spn.probability + %563 = "hi_spn.gaussian"(%arg23) {mean = -7.8645100593566895 : f64, stddev = 1.0376784801483154 : f64} : (f64) -> !hi_spn.probability + %564 = "hi_spn.gaussian"(%arg24) {mean = -10.197491645812988 : f64, stddev = 1.1562715768814087 : f64} : (f64) -> !hi_spn.probability + %565 = "hi_spn.gaussian"(%arg25) {mean = -10.325466156005859 : f64, stddev = 1.0395025014877319 : f64} : (f64) -> !hi_spn.probability + %566 = "hi_spn.product"(%540, %541, %542, %543, %544, %545, %546, %547, %548, %549, %550, %551, %552, %553, %554, %555, %556, %557, %558, %559, %560, %561, %562, %563, %564, %565) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %567 = "hi_spn.gaussian"(%arg0) {mean = -9.9981927871704102 : f64, stddev = 1.349079966545105 : f64} : (f64) -> !hi_spn.probability + %568 = "hi_spn.gaussian"(%arg1) {mean = -9.5111093521118164 : f64, stddev = 1.0804729461669922 : f64} : (f64) -> !hi_spn.probability + %569 = "hi_spn.gaussian"(%arg2) {mean = -0.4326484203338623 : f64, stddev = 0.11743561178445816 : f64} : (f64) -> !hi_spn.probability + %570 = "hi_spn.gaussian"(%arg3) {mean = 0.010107521899044514 : f64, stddev = 0.41620826721191406 : f64} : (f64) -> !hi_spn.probability + %571 = "hi_spn.gaussian"(%arg4) {mean = -6.3572807312011719 : f64, stddev = 1.4624013900756836 : f64} : (f64) -> !hi_spn.probability + %572 = "hi_spn.gaussian"(%arg5) {mean = 0.41814729571342468 : f64, stddev = 0.84322875738143921 : f64} : (f64) -> !hi_spn.probability + %573 = "hi_spn.gaussian"(%arg6) {mean = -0.42156770825386047 : f64, stddev = 1.0614259243011475 : f64} : (f64) -> !hi_spn.probability + %574 = "hi_spn.gaussian"(%arg7) {mean = -1.0652281045913696 : f64, stddev = 1.6961284875869751 : f64} : (f64) -> !hi_spn.probability + %575 = "hi_spn.gaussian"(%arg8) {mean = -1.1810282468795776 : f64, stddev = 1.0225800275802612 : f64} : (f64) -> !hi_spn.probability + %576 = "hi_spn.gaussian"(%arg9) {mean = -3.3692586421966553 : f64, stddev = 1.2080914974212646 : f64} : (f64) -> !hi_spn.probability + %577 = "hi_spn.gaussian"(%arg10) {mean = -4.1349349021911621 : f64, stddev = 0.60587596893310547 : f64} : (f64) -> !hi_spn.probability + %578 = "hi_spn.gaussian"(%arg11) {mean = -4.2974543571472168 : f64, stddev = 0.4851706326007843 : f64} : (f64) -> !hi_spn.probability + %579 = "hi_spn.gaussian"(%arg12) {mean = -2.4741356372833252 : f64, stddev = 0.63358306884765625 : f64} : (f64) -> !hi_spn.probability + %580 = "hi_spn.gaussian"(%arg13) {mean = -2.4334352016448975 : f64, stddev = 1.0984925031661987 : f64} : (f64) -> !hi_spn.probability + %581 = "hi_spn.gaussian"(%arg14) {mean = -3.4565970897674561 : f64, stddev = 1.2904980182647705 : f64} : (f64) -> !hi_spn.probability + %582 = "hi_spn.gaussian"(%arg15) {mean = -4.7016549110412598 : f64, stddev = 0.82111191749572754 : f64} : (f64) -> !hi_spn.probability + %583 = "hi_spn.gaussian"(%arg16) {mean = -6.5498580932617188 : f64, stddev = 0.90290820598602294 : f64} : (f64) -> !hi_spn.probability + %584 = "hi_spn.gaussian"(%arg17) {mean = -6.8462090492248535 : f64, stddev = 1.3959685564041138 : f64} : (f64) -> !hi_spn.probability + %585 = "hi_spn.gaussian"(%arg18) {mean = -7.4271683692932129 : f64, stddev = 1.5763394832611084 : f64} : (f64) -> !hi_spn.probability + %586 = "hi_spn.gaussian"(%arg19) {mean = -7.0357718467712402 : f64, stddev = 0.771068274974823 : f64} : (f64) -> !hi_spn.probability + %587 = "hi_spn.gaussian"(%arg20) {mean = -7.0562090873718262 : f64, stddev = 0.72667789459228516 : f64} : (f64) -> !hi_spn.probability + %588 = "hi_spn.gaussian"(%arg21) {mean = -7.519690990447998 : f64, stddev = 1.0827898979187012 : f64} : (f64) -> !hi_spn.probability + %589 = "hi_spn.gaussian"(%arg22) {mean = -8.1989831924438477 : f64, stddev = 0.77813369035720825 : f64} : (f64) -> !hi_spn.probability + %590 = "hi_spn.gaussian"(%arg23) {mean = -10.648179054260254 : f64, stddev = 1.4898028373718262 : f64} : (f64) -> !hi_spn.probability + %591 = "hi_spn.gaussian"(%arg24) {mean = -12.422126770019531 : f64, stddev = 0.54856288433074951 : f64} : (f64) -> !hi_spn.probability + %592 = "hi_spn.gaussian"(%arg25) {mean = -10.197339057922363 : f64, stddev = 0.77346521615982056 : f64} : (f64) -> !hi_spn.probability + %593 = "hi_spn.product"(%567, %568, %569, %570, %571, %572, %573, %574, %575, %576, %577, %578, %579, %580, %581, %582, %583, %584, %585, %586, %587, %588, %589, %590, %591, %592) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %594 = "hi_spn.gaussian"(%arg0) {mean = -8.3035554885864258 : f64, stddev = 1.4264081716537476 : f64} : (f64) -> !hi_spn.probability + %595 = "hi_spn.gaussian"(%arg1) {mean = -5.7859644889831543 : f64, stddev = 1.1462522745132446 : f64} : (f64) -> !hi_spn.probability + %596 = "hi_spn.gaussian"(%arg2) {mean = -0.73140090703964233 : f64, stddev = 0.58304047584533691 : f64} : (f64) -> !hi_spn.probability + %597 = "hi_spn.gaussian"(%arg3) {mean = -1.1587828397750854 : f64, stddev = 0.91656625270843505 : f64} : (f64) -> !hi_spn.probability + %598 = "hi_spn.gaussian"(%arg4) {mean = -1.3571137189865112 : f64, stddev = 1.4835728406906128 : f64} : (f64) -> !hi_spn.probability + %599 = "hi_spn.gaussian"(%arg5) {mean = 1.1117973327636719 : f64, stddev = 1.0996159315109253 : f64} : (f64) -> !hi_spn.probability + %600 = "hi_spn.gaussian"(%arg6) {mean = -0.32646733522415161 : f64, stddev = 1.3229366540908813 : f64} : (f64) -> !hi_spn.probability + %601 = "hi_spn.gaussian"(%arg7) {mean = 0.16027800738811493 : f64, stddev = 1.2367702722549438 : f64} : (f64) -> !hi_spn.probability + %602 = "hi_spn.gaussian"(%arg8) {mean = -1.5991113185882568 : f64, stddev = 1.1161046028137207 : f64} : (f64) -> !hi_spn.probability + %603 = "hi_spn.gaussian"(%arg9) {mean = -2.561234712600708 : f64, stddev = 1.0401123762130737 : f64} : (f64) -> !hi_spn.probability + %604 = "hi_spn.gaussian"(%arg10) {mean = -3.5093762874603271 : f64, stddev = 1.389326810836792 : f64} : (f64) -> !hi_spn.probability + %605 = "hi_spn.gaussian"(%arg11) {mean = -3.6842494010925293 : f64, stddev = 1.1795589923858643 : f64} : (f64) -> !hi_spn.probability + %606 = "hi_spn.gaussian"(%arg12) {mean = -3.6213102340698242 : f64, stddev = 1.0385367870330811 : f64} : (f64) -> !hi_spn.probability + %607 = "hi_spn.gaussian"(%arg13) {mean = -3.9341332912445068 : f64, stddev = 1.5357104539871216 : f64} : (f64) -> !hi_spn.probability + %608 = "hi_spn.gaussian"(%arg14) {mean = -5.4853053092956543 : f64, stddev = 1.3057036399841309 : f64} : (f64) -> !hi_spn.probability + %609 = "hi_spn.gaussian"(%arg15) {mean = -5.8680596351623535 : f64, stddev = 0.99688857793807983 : f64} : (f64) -> !hi_spn.probability + %610 = "hi_spn.gaussian"(%arg16) {mean = -5.2930760383605957 : f64, stddev = 1.0633838176727295 : f64} : (f64) -> !hi_spn.probability + %611 = "hi_spn.gaussian"(%arg17) {mean = -5.3858251571655273 : f64, stddev = 0.85936248302459716 : f64} : (f64) -> !hi_spn.probability + %612 = "hi_spn.gaussian"(%arg18) {mean = -6.6426081657409668 : f64, stddev = 1.0150829553604126 : f64} : (f64) -> !hi_spn.probability + %613 = "hi_spn.gaussian"(%arg19) {mean = -6.7648916244506836 : f64, stddev = 0.79391300678253173 : f64} : (f64) -> !hi_spn.probability + %614 = "hi_spn.gaussian"(%arg20) {mean = -6.5834479331970215 : f64, stddev = 0.93375498056411743 : f64} : (f64) -> !hi_spn.probability + %615 = "hi_spn.gaussian"(%arg21) {mean = -7.2036385536193848 : f64, stddev = 0.94570732116699218 : f64} : (f64) -> !hi_spn.probability + %616 = "hi_spn.gaussian"(%arg22) {mean = -7.9912042617797852 : f64, stddev = 0.97631841897964478 : f64} : (f64) -> !hi_spn.probability + %617 = "hi_spn.gaussian"(%arg23) {mean = -8.9483375549316406 : f64, stddev = 0.91292828321456909 : f64} : (f64) -> !hi_spn.probability + %618 = "hi_spn.gaussian"(%arg24) {mean = -10.805872917175293 : f64, stddev = 0.92005360126495361 : f64} : (f64) -> !hi_spn.probability + %619 = "hi_spn.gaussian"(%arg25) {mean = -10.409214019775391 : f64, stddev = 0.82885313034057617 : f64} : (f64) -> !hi_spn.probability + %620 = "hi_spn.product"(%594, %595, %596, %597, %598, %599, %600, %601, %602, %603, %604, %605, %606, %607, %608, %609, %610, %611, %612, %613, %614, %615, %616, %617, %618, %619) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %621 = "hi_spn.gaussian"(%arg0) {mean = -10.204464912414551 : f64, stddev = 1.6020863056182861 : f64} : (f64) -> !hi_spn.probability + %622 = "hi_spn.gaussian"(%arg1) {mean = -3.8893156051635742 : f64, stddev = 0.5583304762840271 : f64} : (f64) -> !hi_spn.probability + %623 = "hi_spn.gaussian"(%arg2) {mean = -2.0201916694641113 : f64, stddev = 0.87866860628128052 : f64} : (f64) -> !hi_spn.probability + %624 = "hi_spn.gaussian"(%arg3) {mean = -3.6782877445220947 : f64, stddev = 0.60904550552368164 : f64} : (f64) -> !hi_spn.probability + %625 = "hi_spn.gaussian"(%arg4) {mean = -1.2081284523010254 : f64, stddev = 0.98104596138000488 : f64} : (f64) -> !hi_spn.probability + %626 = "hi_spn.gaussian"(%arg5) {mean = -1.8895267248153687 : f64, stddev = 0.99456137418746948 : f64} : (f64) -> !hi_spn.probability + %627 = "hi_spn.gaussian"(%arg6) {mean = -0.31599321961402893 : f64, stddev = 1.4036113023757935 : f64} : (f64) -> !hi_spn.probability + %628 = "hi_spn.gaussian"(%arg7) {mean = -0.47833892703056335 : f64, stddev = 0.83540689945220947 : f64} : (f64) -> !hi_spn.probability + %629 = "hi_spn.gaussian"(%arg8) {mean = -0.95180541276931763 : f64, stddev = 1.065526008605957 : f64} : (f64) -> !hi_spn.probability + %630 = "hi_spn.gaussian"(%arg9) {mean = -2.5686800479888916 : f64, stddev = 0.73564785718917847 : f64} : (f64) -> !hi_spn.probability + %631 = "hi_spn.gaussian"(%arg10) {mean = -3.1979820728302002 : f64, stddev = 0.94251245260238647 : f64} : (f64) -> !hi_spn.probability + %632 = "hi_spn.gaussian"(%arg11) {mean = -3.4310882091522217 : f64, stddev = 1.4012502431869507 : f64} : (f64) -> !hi_spn.probability + %633 = "hi_spn.gaussian"(%arg12) {mean = -3.146794319152832 : f64, stddev = 1.0862197875976563 : f64} : (f64) -> !hi_spn.probability + %634 = "hi_spn.gaussian"(%arg13) {mean = -3.5825841426849365 : f64, stddev = 1.0464397668838501 : f64} : (f64) -> !hi_spn.probability + %635 = "hi_spn.gaussian"(%arg14) {mean = -5.0083832740783691 : f64, stddev = 1.6265349388122559 : f64} : (f64) -> !hi_spn.probability + %636 = "hi_spn.gaussian"(%arg15) {mean = -6.0216951370239258 : f64, stddev = 1.3344634771347046 : f64} : (f64) -> !hi_spn.probability + %637 = "hi_spn.gaussian"(%arg16) {mean = -5.8631892204284668 : f64, stddev = 1.1985464096069336 : f64} : (f64) -> !hi_spn.probability + %638 = "hi_spn.gaussian"(%arg17) {mean = -5.4431085586547852 : f64, stddev = 0.82594609260559082 : f64} : (f64) -> !hi_spn.probability + %639 = "hi_spn.gaussian"(%arg18) {mean = -6.669257640838623 : f64, stddev = 0.87101781368255615 : f64} : (f64) -> !hi_spn.probability + %640 = "hi_spn.gaussian"(%arg19) {mean = -7.1576075553894043 : f64, stddev = 0.80675035715103149 : f64} : (f64) -> !hi_spn.probability + %641 = "hi_spn.gaussian"(%arg20) {mean = -6.5607175827026367 : f64, stddev = 0.99137264490127563 : f64} : (f64) -> !hi_spn.probability + %642 = "hi_spn.gaussian"(%arg21) {mean = -7.4074254035949707 : f64, stddev = 1.0610796213150024 : f64} : (f64) -> !hi_spn.probability + %643 = "hi_spn.gaussian"(%arg22) {mean = -7.2190847396850586 : f64, stddev = 0.75069218873977661 : f64} : (f64) -> !hi_spn.probability + %644 = "hi_spn.gaussian"(%arg23) {mean = -8.429936408996582 : f64, stddev = 1.3490469455718994 : f64} : (f64) -> !hi_spn.probability + %645 = "hi_spn.gaussian"(%arg24) {mean = -11.17424488067627 : f64, stddev = 0.96131938695907593 : f64} : (f64) -> !hi_spn.probability + %646 = "hi_spn.gaussian"(%arg25) {mean = -9.9179468154907227 : f64, stddev = 0.83977603912353515 : f64} : (f64) -> !hi_spn.probability + %647 = "hi_spn.product"(%621, %622, %623, %624, %625, %626, %627, %628, %629, %630, %631, %632, %633, %634, %635, %636, %637, %638, %639, %640, %641, %642, %643, %644, %645, %646) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %648 = "hi_spn.gaussian"(%arg0) {mean = -8.9104881286621094 : f64, stddev = 1.2407795190811157 : f64} : (f64) -> !hi_spn.probability + %649 = "hi_spn.gaussian"(%arg1) {mean = -4.7912893295288086 : f64, stddev = 1.3421698808670044 : f64} : (f64) -> !hi_spn.probability + %650 = "hi_spn.gaussian"(%arg2) {mean = -1.2381263971328735 : f64, stddev = 0.97720080614089966 : f64} : (f64) -> !hi_spn.probability + %651 = "hi_spn.gaussian"(%arg3) {mean = -1.9825401306152344 : f64, stddev = 1.0127644538879395 : f64} : (f64) -> !hi_spn.probability + %652 = "hi_spn.gaussian"(%arg4) {mean = -1.1328252553939819 : f64, stddev = 2.2940671443939209 : f64} : (f64) -> !hi_spn.probability + %653 = "hi_spn.gaussian"(%arg5) {mean = -0.5301283597946167 : f64, stddev = 1.6068472862243652 : f64} : (f64) -> !hi_spn.probability + %654 = "hi_spn.gaussian"(%arg6) {mean = -2.9863975048065186 : f64, stddev = 1.1283366680145264 : f64} : (f64) -> !hi_spn.probability + %655 = "hi_spn.gaussian"(%arg7) {mean = -3.56972336769104 : f64, stddev = 1.0562425851821899 : f64} : (f64) -> !hi_spn.probability + %656 = "hi_spn.gaussian"(%arg8) {mean = -4.7440629005432129 : f64, stddev = 1.0046619176864624 : f64} : (f64) -> !hi_spn.probability + %657 = "hi_spn.gaussian"(%arg9) {mean = -5.6077818870544434 : f64, stddev = 1.0343097448348999 : f64} : (f64) -> !hi_spn.probability + %658 = "hi_spn.gaussian"(%arg10) {mean = -6.6057672500610352 : f64, stddev = 0.77828651666641235 : f64} : (f64) -> !hi_spn.probability + %659 = "hi_spn.gaussian"(%arg11) {mean = -7.3964743614196777 : f64, stddev = 0.77072250843048095 : f64} : (f64) -> !hi_spn.probability + %660 = "hi_spn.gaussian"(%arg12) {mean = -7.2408137321472168 : f64, stddev = 0.85538417100906372 : f64} : (f64) -> !hi_spn.probability + %661 = "hi_spn.gaussian"(%arg13) {mean = -6.7544369697570801 : f64, stddev = 0.89315718412399292 : f64} : (f64) -> !hi_spn.probability + %662 = "hi_spn.gaussian"(%arg14) {mean = -4.9838619232177734 : f64, stddev = 1.0191832780838013 : f64} : (f64) -> !hi_spn.probability + %663 = "hi_spn.gaussian"(%arg15) {mean = -3.9145212173461914 : f64, stddev = 0.83778947591781616 : f64} : (f64) -> !hi_spn.probability + %664 = "hi_spn.gaussian"(%arg16) {mean = -4.2840766906738281 : f64, stddev = 0.91526001691818237 : f64} : (f64) -> !hi_spn.probability + %665 = "hi_spn.gaussian"(%arg17) {mean = -4.2897839546203613 : f64, stddev = 0.7030068039894104 : f64} : (f64) -> !hi_spn.probability + %666 = "hi_spn.gaussian"(%arg18) {mean = -5.8863673210144043 : f64, stddev = 0.80484467744827271 : f64} : (f64) -> !hi_spn.probability + %667 = "hi_spn.gaussian"(%arg19) {mean = -6.3534946441650391 : f64, stddev = 1.0250194072723389 : f64} : (f64) -> !hi_spn.probability + %668 = "hi_spn.gaussian"(%arg20) {mean = -6.1748285293579102 : f64, stddev = 1.0809991359710693 : f64} : (f64) -> !hi_spn.probability + %669 = "hi_spn.gaussian"(%arg21) {mean = -6.649622917175293 : f64, stddev = 0.96549338102340698 : f64} : (f64) -> !hi_spn.probability + %670 = "hi_spn.gaussian"(%arg22) {mean = -7.4645318984985352 : f64, stddev = 1.1257213354110718 : f64} : (f64) -> !hi_spn.probability + %671 = "hi_spn.gaussian"(%arg23) {mean = -9.1433000564575195 : f64, stddev = 1.1664202213287354 : f64} : (f64) -> !hi_spn.probability + %672 = "hi_spn.gaussian"(%arg24) {mean = -11.460670471191406 : f64, stddev = 1.1801751852035522 : f64} : (f64) -> !hi_spn.probability + %673 = "hi_spn.gaussian"(%arg25) {mean = -10.52480411529541 : f64, stddev = 0.96083921194076538 : f64} : (f64) -> !hi_spn.probability + %674 = "hi_spn.product"(%648, %649, %650, %651, %652, %653, %654, %655, %656, %657, %658, %659, %660, %661, %662, %663, %664, %665, %666, %667, %668, %669, %670, %671, %672, %673) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %675 = "hi_spn.gaussian"(%arg0) {mean = -9.7251415252685546 : f64, stddev = 1.6073542833328247 : f64} : (f64) -> !hi_spn.probability + %676 = "hi_spn.gaussian"(%arg1) {mean = -4.9133510589599609 : f64, stddev = 1.3696430921554565 : f64} : (f64) -> !hi_spn.probability + %677 = "hi_spn.gaussian"(%arg2) {mean = -1.4497207403182983 : f64, stddev = 0.87673693895339966 : f64} : (f64) -> !hi_spn.probability + %678 = "hi_spn.gaussian"(%arg3) {mean = -2.0057938098907471 : f64, stddev = 1.1309043169021606 : f64} : (f64) -> !hi_spn.probability + %679 = "hi_spn.gaussian"(%arg4) {mean = -0.9948994517326355 : f64, stddev = 1.4527140855789185 : f64} : (f64) -> !hi_spn.probability + %680 = "hi_spn.gaussian"(%arg5) {mean = -0.0098668904975056648 : f64, stddev = 1.2156569957733154 : f64} : (f64) -> !hi_spn.probability + %681 = "hi_spn.gaussian"(%arg6) {mean = -1.388715386390686 : f64, stddev = 1.1291501522064209 : f64} : (f64) -> !hi_spn.probability + %682 = "hi_spn.gaussian"(%arg7) {mean = -2.0123498439788818 : f64, stddev = 1.027299165725708 : f64} : (f64) -> !hi_spn.probability + %683 = "hi_spn.gaussian"(%arg8) {mean = -3.6880393028259277 : f64, stddev = 1.1262346506118774 : f64} : (f64) -> !hi_spn.probability + %684 = "hi_spn.gaussian"(%arg9) {mean = -4.7770075798034668 : f64, stddev = 0.69989395141601563 : f64} : (f64) -> !hi_spn.probability + %685 = "hi_spn.gaussian"(%arg10) {mean = -5.5736570358276367 : f64, stddev = 0.70573288202285767 : f64} : (f64) -> !hi_spn.probability + %686 = "hi_spn.gaussian"(%arg11) {mean = -5.9974842071533203 : f64, stddev = 0.75162220001220703 : f64} : (f64) -> !hi_spn.probability + %687 = "hi_spn.gaussian"(%arg12) {mean = -4.8669891357421875 : f64, stddev = 1.1740869283676147 : f64} : (f64) -> !hi_spn.probability + %688 = "hi_spn.gaussian"(%arg13) {mean = -3.9618053436279297 : f64, stddev = 1.0474263429641724 : f64} : (f64) -> !hi_spn.probability + %689 = "hi_spn.gaussian"(%arg14) {mean = -4.8217320442199707 : f64, stddev = 1.2642390727996826 : f64} : (f64) -> !hi_spn.probability + %690 = "hi_spn.gaussian"(%arg15) {mean = -5.7867317199707031 : f64, stddev = 1.20456862449646 : f64} : (f64) -> !hi_spn.probability + %691 = "hi_spn.gaussian"(%arg16) {mean = -5.4635353088378906 : f64, stddev = 1.0963999032974243 : f64} : (f64) -> !hi_spn.probability + %692 = "hi_spn.gaussian"(%arg17) {mean = -5.9494080543518066 : f64, stddev = 1.2840547561645508 : f64} : (f64) -> !hi_spn.probability + %693 = "hi_spn.gaussian"(%arg18) {mean = -7.3937587738037109 : f64, stddev = 1.2171931266784668 : f64} : (f64) -> !hi_spn.probability + %694 = "hi_spn.gaussian"(%arg19) {mean = -7.3455724716186523 : f64, stddev = 1.1192160844802856 : f64} : (f64) -> !hi_spn.probability + %695 = "hi_spn.gaussian"(%arg20) {mean = -7.7751250267028809 : f64, stddev = 1.254682183265686 : f64} : (f64) -> !hi_spn.probability + %696 = "hi_spn.gaussian"(%arg21) {mean = -8.0876932144165039 : f64, stddev = 1.142391562461853 : f64} : (f64) -> !hi_spn.probability + %697 = "hi_spn.gaussian"(%arg22) {mean = -9.1555747985839844 : f64, stddev = 0.95152676105499267 : f64} : (f64) -> !hi_spn.probability + %698 = "hi_spn.gaussian"(%arg23) {mean = -10.220481872558594 : f64, stddev = 0.99923872947692871 : f64} : (f64) -> !hi_spn.probability + %699 = "hi_spn.gaussian"(%arg24) {mean = -12.120240211486816 : f64, stddev = 0.85913735628128052 : f64} : (f64) -> !hi_spn.probability + %700 = "hi_spn.gaussian"(%arg25) {mean = -11.011944770812988 : f64, stddev = 0.99873799085617065 : f64} : (f64) -> !hi_spn.probability + %701 = "hi_spn.product"(%675, %676, %677, %678, %679, %680, %681, %682, %683, %684, %685, %686, %687, %688, %689, %690, %691, %692, %693, %694, %695, %696, %697, %698, %699, %700) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %702 = "hi_spn.gaussian"(%arg0) {mean = -8.9069051742553711 : f64, stddev = 1.4147145748138428 : f64} : (f64) -> !hi_spn.probability + %703 = "hi_spn.gaussian"(%arg1) {mean = -4.8122344017028809 : f64, stddev = 1.4748255014419556 : f64} : (f64) -> !hi_spn.probability + %704 = "hi_spn.gaussian"(%arg2) {mean = -1.7176178693771362 : f64, stddev = 1.0098350048065186 : f64} : (f64) -> !hi_spn.probability + %705 = "hi_spn.gaussian"(%arg3) {mean = -1.9232354164123535 : f64, stddev = 1.2849118709564209 : f64} : (f64) -> !hi_spn.probability + %706 = "hi_spn.gaussian"(%arg4) {mean = -1.3586279153823853 : f64, stddev = 1.4348475933074951 : f64} : (f64) -> !hi_spn.probability + %707 = "hi_spn.gaussian"(%arg5) {mean = -0.97650361061096191 : f64, stddev = 1.3677494525909424 : f64} : (f64) -> !hi_spn.probability + %708 = "hi_spn.gaussian"(%arg6) {mean = -1.9844210147857666 : f64, stddev = 1.0846155881881714 : f64} : (f64) -> !hi_spn.probability + %709 = "hi_spn.gaussian"(%arg7) {mean = -3.1613943576812744 : f64, stddev = 1.2197260856628418 : f64} : (f64) -> !hi_spn.probability + %710 = "hi_spn.gaussian"(%arg8) {mean = -4.1783685684204102 : f64, stddev = 0.91401422023773193 : f64} : (f64) -> !hi_spn.probability + %711 = "hi_spn.gaussian"(%arg9) {mean = -5.2987546920776367 : f64, stddev = 0.84328430891036987 : f64} : (f64) -> !hi_spn.probability + %712 = "hi_spn.gaussian"(%arg10) {mean = -5.8906431198120117 : f64, stddev = 0.69786202907562256 : f64} : (f64) -> !hi_spn.probability + %713 = "hi_spn.gaussian"(%arg11) {mean = -6.1243467330932617 : f64, stddev = 0.87674009799957275 : f64} : (f64) -> !hi_spn.probability + %714 = "hi_spn.gaussian"(%arg12) {mean = -5.3981232643127441 : f64, stddev = 0.94147020578384399 : f64} : (f64) -> !hi_spn.probability + %715 = "hi_spn.gaussian"(%arg13) {mean = -4.7278761863708496 : f64, stddev = 0.97161221504211425 : f64} : (f64) -> !hi_spn.probability + %716 = "hi_spn.gaussian"(%arg14) {mean = -5.9369235038757324 : f64, stddev = 1.1867256164550781 : f64} : (f64) -> !hi_spn.probability + %717 = "hi_spn.gaussian"(%arg15) {mean = -7.3701410293579102 : f64, stddev = 1.0420466661453247 : f64} : (f64) -> !hi_spn.probability + %718 = "hi_spn.gaussian"(%arg16) {mean = -6.6716737747192383 : f64, stddev = 1.0859712362289429 : f64} : (f64) -> !hi_spn.probability + %719 = "hi_spn.gaussian"(%arg17) {mean = -5.9181299209594727 : f64, stddev = 0.81934303045272827 : f64} : (f64) -> !hi_spn.probability + %720 = "hi_spn.gaussian"(%arg18) {mean = -7.5805239677429199 : f64, stddev = 0.65694856643676758 : f64} : (f64) -> !hi_spn.probability + %721 = "hi_spn.gaussian"(%arg19) {mean = -8.1077966690063477 : f64, stddev = 1.0762103796005249 : f64} : (f64) -> !hi_spn.probability + %722 = "hi_spn.gaussian"(%arg20) {mean = -7.6604113578796387 : f64, stddev = 0.86569845676422119 : f64} : (f64) -> !hi_spn.probability + %723 = "hi_spn.gaussian"(%arg21) {mean = -7.7094779014587402 : f64, stddev = 1.0633842945098877 : f64} : (f64) -> !hi_spn.probability + %724 = "hi_spn.gaussian"(%arg22) {mean = -7.7834830284118652 : f64, stddev = 0.74442803859710693 : f64} : (f64) -> !hi_spn.probability + %725 = "hi_spn.gaussian"(%arg23) {mean = -8.1353664398193359 : f64, stddev = 1.0096304416656494 : f64} : (f64) -> !hi_spn.probability + %726 = "hi_spn.gaussian"(%arg24) {mean = -9.9486246109008789 : f64, stddev = 1.1339542865753174 : f64} : (f64) -> !hi_spn.probability + %727 = "hi_spn.gaussian"(%arg25) {mean = -10.53874397277832 : f64, stddev = 1.3633646965026855 : f64} : (f64) -> !hi_spn.probability + %728 = "hi_spn.product"(%702, %703, %704, %705, %706, %707, %708, %709, %710, %711, %712, %713, %714, %715, %716, %717, %718, %719, %720, %721, %722, %723, %724, %725, %726, %727) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %729 = "hi_spn.gaussian"(%arg0) {mean = -10.626448631286621 : f64, stddev = 1.3535375595092773 : f64} : (f64) -> !hi_spn.probability + %730 = "hi_spn.gaussian"(%arg1) {mean = -4.9930663108825684 : f64, stddev = 1.2566756010055542 : f64} : (f64) -> !hi_spn.probability + %731 = "hi_spn.gaussian"(%arg2) {mean = -2.795074462890625 : f64, stddev = 1.004554271697998 : f64} : (f64) -> !hi_spn.probability + %732 = "hi_spn.gaussian"(%arg3) {mean = -4.1691288948059082 : f64, stddev = 1.101131796836853 : f64} : (f64) -> !hi_spn.probability + %733 = "hi_spn.gaussian"(%arg4) {mean = -2.5265030860900879 : f64, stddev = 1.5360026359558105 : f64} : (f64) -> !hi_spn.probability + %734 = "hi_spn.gaussian"(%arg5) {mean = -2.6002805233001709 : f64, stddev = 1.6389932632446289 : f64} : (f64) -> !hi_spn.probability + %735 = "hi_spn.gaussian"(%arg6) {mean = -3.6623227596282959 : f64, stddev = 1.6795462369918823 : f64} : (f64) -> !hi_spn.probability + %736 = "hi_spn.gaussian"(%arg7) {mean = -4.3177437782287598 : f64, stddev = 1.9227278232574463 : f64} : (f64) -> !hi_spn.probability + %737 = "hi_spn.gaussian"(%arg8) {mean = -4.8729019165039063 : f64, stddev = 1.5416054725646973 : f64} : (f64) -> !hi_spn.probability + %738 = "hi_spn.gaussian"(%arg9) {mean = -5.9028677940368652 : f64, stddev = 1.2555408477783203 : f64} : (f64) -> !hi_spn.probability + %739 = "hi_spn.gaussian"(%arg10) {mean = -7.5546765327453613 : f64, stddev = 0.97685593366622925 : f64} : (f64) -> !hi_spn.probability + %740 = "hi_spn.gaussian"(%arg11) {mean = -8.9893970489501953 : f64, stddev = 1.0144381523132324 : f64} : (f64) -> !hi_spn.probability + %741 = "hi_spn.gaussian"(%arg12) {mean = -10.060663223266602 : f64, stddev = 1.3323405981063843 : f64} : (f64) -> !hi_spn.probability + %742 = "hi_spn.gaussian"(%arg13) {mean = -11.011593818664551 : f64, stddev = 1.1708763837814331 : f64} : (f64) -> !hi_spn.probability + %743 = "hi_spn.gaussian"(%arg14) {mean = -11.776839256286621 : f64, stddev = 0.9046136736869812 : f64} : (f64) -> !hi_spn.probability + %744 = "hi_spn.gaussian"(%arg15) {mean = -11.659098625183105 : f64, stddev = 1.0695267915725708 : f64} : (f64) -> !hi_spn.probability + %745 = "hi_spn.gaussian"(%arg16) {mean = -11.659463882446289 : f64, stddev = 1.2668837308883667 : f64} : (f64) -> !hi_spn.probability + %746 = "hi_spn.gaussian"(%arg17) {mean = -11.007206916809082 : f64, stddev = 1.1497730016708374 : f64} : (f64) -> !hi_spn.probability + %747 = "hi_spn.gaussian"(%arg18) {mean = -10.421265602111816 : f64, stddev = 1.3350493907928467 : f64} : (f64) -> !hi_spn.probability + %748 = "hi_spn.gaussian"(%arg19) {mean = -10.924421310424805 : f64, stddev = 1.5174006223678589 : f64} : (f64) -> !hi_spn.probability + %749 = "hi_spn.gaussian"(%arg20) {mean = -10.377594947814941 : f64, stddev = 1.1411463022232056 : f64} : (f64) -> !hi_spn.probability + %750 = "hi_spn.gaussian"(%arg21) {mean = -10.87409782409668 : f64, stddev = 1.1356991529464722 : f64} : (f64) -> !hi_spn.probability + %751 = "hi_spn.gaussian"(%arg22) {mean = -9.9779891967773438 : f64, stddev = 1.114973783493042 : f64} : (f64) -> !hi_spn.probability + %752 = "hi_spn.gaussian"(%arg23) {mean = -10.446820259094238 : f64, stddev = 1.3415701389312744 : f64} : (f64) -> !hi_spn.probability + %753 = "hi_spn.gaussian"(%arg24) {mean = -12.710579872131348 : f64, stddev = 1.0317741632461548 : f64} : (f64) -> !hi_spn.probability + %754 = "hi_spn.gaussian"(%arg25) {mean = -12.227673530578613 : f64, stddev = 0.91354125738143921 : f64} : (f64) -> !hi_spn.probability + %755 = "hi_spn.product"(%729, %730, %731, %732, %733, %734, %735, %736, %737, %738, %739, %740, %741, %742, %743, %744, %745, %746, %747, %748, %749, %750, %751, %752, %753, %754) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %756 = "hi_spn.gaussian"(%arg0) {mean = -9.6814069747924805 : f64, stddev = 1.9111077785491943 : f64} : (f64) -> !hi_spn.probability + %757 = "hi_spn.gaussian"(%arg1) {mean = -4.8533101081848145 : f64, stddev = 1.1461453437805176 : f64} : (f64) -> !hi_spn.probability + %758 = "hi_spn.gaussian"(%arg2) {mean = -2.9722335338592529 : f64, stddev = 1.0362757444381714 : f64} : (f64) -> !hi_spn.probability + %759 = "hi_spn.gaussian"(%arg3) {mean = -3.5680413246154785 : f64, stddev = 1.5056160688400269 : f64} : (f64) -> !hi_spn.probability + %760 = "hi_spn.gaussian"(%arg4) {mean = -2.3838784694671631 : f64, stddev = 1.4592957496643066 : f64} : (f64) -> !hi_spn.probability + %761 = "hi_spn.gaussian"(%arg5) {mean = -2.2435345649719238 : f64, stddev = 1.1607135534286499 : f64} : (f64) -> !hi_spn.probability + %762 = "hi_spn.gaussian"(%arg6) {mean = -2.4707129001617432 : f64, stddev = 0.95704185962677001 : f64} : (f64) -> !hi_spn.probability + %763 = "hi_spn.gaussian"(%arg7) {mean = -3.5692412853240967 : f64, stddev = 0.95319283008575439 : f64} : (f64) -> !hi_spn.probability + %764 = "hi_spn.gaussian"(%arg8) {mean = -4.0828475952148438 : f64, stddev = 0.81794565916061401 : f64} : (f64) -> !hi_spn.probability + %765 = "hi_spn.gaussian"(%arg9) {mean = -4.8434739112854004 : f64, stddev = 1.041487455368042 : f64} : (f64) -> !hi_spn.probability + %766 = "hi_spn.gaussian"(%arg10) {mean = -5.1835207939147949 : f64, stddev = 1.2820824384689331 : f64} : (f64) -> !hi_spn.probability + %767 = "hi_spn.gaussian"(%arg11) {mean = -5.7133607864379883 : f64, stddev = 1.3266124725341797 : f64} : (f64) -> !hi_spn.probability + %768 = "hi_spn.gaussian"(%arg12) {mean = -6.655754566192627 : f64, stddev = 1.2050929069519043 : f64} : (f64) -> !hi_spn.probability + %769 = "hi_spn.gaussian"(%arg13) {mean = -7.9018731117248535 : f64, stddev = 1.2177575826644897 : f64} : (f64) -> !hi_spn.probability + %770 = "hi_spn.gaussian"(%arg14) {mean = -9.271662712097168 : f64, stddev = 0.69999289512634277 : f64} : (f64) -> !hi_spn.probability + %771 = "hi_spn.gaussian"(%arg15) {mean = -9.3606767654418945 : f64, stddev = 0.95105248689651489 : f64} : (f64) -> !hi_spn.probability + %772 = "hi_spn.gaussian"(%arg16) {mean = -8.7183160781860352 : f64, stddev = 1.2053486108779907 : f64} : (f64) -> !hi_spn.probability + %773 = "hi_spn.gaussian"(%arg17) {mean = -8.7672090530395508 : f64, stddev = 0.85045510530471802 : f64} : (f64) -> !hi_spn.probability + %774 = "hi_spn.gaussian"(%arg18) {mean = -9.5070924758911133 : f64, stddev = 0.62979328632354736 : f64} : (f64) -> !hi_spn.probability + %775 = "hi_spn.gaussian"(%arg19) {mean = -10.053318977355957 : f64, stddev = 1.0457770824432373 : f64} : (f64) -> !hi_spn.probability + %776 = "hi_spn.gaussian"(%arg20) {mean = -9.6750011444091796 : f64, stddev = 0.83575439453125 : f64} : (f64) -> !hi_spn.probability + %777 = "hi_spn.gaussian"(%arg21) {mean = -9.8804206848144531 : f64, stddev = 1.0302900075912476 : f64} : (f64) -> !hi_spn.probability + %778 = "hi_spn.gaussian"(%arg22) {mean = -9.6952419281005859 : f64, stddev = 1.4331678152084351 : f64} : (f64) -> !hi_spn.probability + %779 = "hi_spn.gaussian"(%arg23) {mean = -9.9766674041748046 : f64, stddev = 1.7196415662765503 : f64} : (f64) -> !hi_spn.probability + %780 = "hi_spn.gaussian"(%arg24) {mean = -11.346095085144043 : f64, stddev = 1.3947709798812866 : f64} : (f64) -> !hi_spn.probability + %781 = "hi_spn.gaussian"(%arg25) {mean = -11.206250190734863 : f64, stddev = 0.97774547338485718 : f64} : (f64) -> !hi_spn.probability + %782 = "hi_spn.product"(%756, %757, %758, %759, %760, %761, %762, %763, %764, %765, %766, %767, %768, %769, %770, %771, %772, %773, %774, %775, %776, %777, %778, %779, %780, %781) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %783 = "hi_spn.gaussian"(%arg0) {mean = -9.8093748092651367 : f64, stddev = 1.60984206199646 : f64} : (f64) -> !hi_spn.probability + %784 = "hi_spn.gaussian"(%arg1) {mean = -4.4710850715637207 : f64, stddev = 0.67405211925506592 : f64} : (f64) -> !hi_spn.probability + %785 = "hi_spn.gaussian"(%arg2) {mean = -1.9430066347122192 : f64, stddev = 0.8056831955909729 : f64} : (f64) -> !hi_spn.probability + %786 = "hi_spn.gaussian"(%arg3) {mean = -3.2178990840911865 : f64, stddev = 0.87899678945541382 : f64} : (f64) -> !hi_spn.probability + %787 = "hi_spn.gaussian"(%arg4) {mean = -0.3972700834274292 : f64, stddev = 1.2791483402252197 : f64} : (f64) -> !hi_spn.probability + %788 = "hi_spn.gaussian"(%arg5) {mean = -0.21696287393569946 : f64, stddev = 1.062208890914917 : f64} : (f64) -> !hi_spn.probability + %789 = "hi_spn.gaussian"(%arg6) {mean = -1.7810295820236206 : f64, stddev = 1.0984369516372681 : f64} : (f64) -> !hi_spn.probability + %790 = "hi_spn.gaussian"(%arg7) {mean = -2.0153028964996338 : f64, stddev = 1.0543609857559204 : f64} : (f64) -> !hi_spn.probability + %791 = "hi_spn.gaussian"(%arg8) {mean = -2.0518224239349365 : f64, stddev = 0.97714114189147949 : f64} : (f64) -> !hi_spn.probability + %792 = "hi_spn.gaussian"(%arg9) {mean = -3.5302813053131104 : f64, stddev = 0.8511124849319458 : f64} : (f64) -> !hi_spn.probability + %793 = "hi_spn.gaussian"(%arg10) {mean = -5.5197649002075195 : f64, stddev = 1.1828925609588623 : f64} : (f64) -> !hi_spn.probability + %794 = "hi_spn.gaussian"(%arg11) {mean = -7.8433918952941895 : f64, stddev = 0.93302077054977417 : f64} : (f64) -> !hi_spn.probability + %795 = "hi_spn.gaussian"(%arg12) {mean = -9.4301156997680664 : f64, stddev = 0.6484944224357605 : f64} : (f64) -> !hi_spn.probability + %796 = "hi_spn.gaussian"(%arg13) {mean = -10.277225494384766 : f64, stddev = 0.71469396352767944 : f64} : (f64) -> !hi_spn.probability + %797 = "hi_spn.gaussian"(%arg14) {mean = -10.934865951538086 : f64, stddev = 0.66158908605575562 : f64} : (f64) -> !hi_spn.probability + %798 = "hi_spn.gaussian"(%arg15) {mean = -10.914000511169434 : f64, stddev = 0.82890325784683228 : f64} : (f64) -> !hi_spn.probability + %799 = "hi_spn.gaussian"(%arg16) {mean = -10.349800109863281 : f64, stddev = 1.0774797201156616 : f64} : (f64) -> !hi_spn.probability + %800 = "hi_spn.gaussian"(%arg17) {mean = -9.2753219604492188 : f64, stddev = 0.9993625283241272 : f64} : (f64) -> !hi_spn.probability + %801 = "hi_spn.gaussian"(%arg18) {mean = -8.7646818161010742 : f64, stddev = 1.3473575115203857 : f64} : (f64) -> !hi_spn.probability + %802 = "hi_spn.gaussian"(%arg19) {mean = -8.8910846710205078 : f64, stddev = 0.96669775247573853 : f64} : (f64) -> !hi_spn.probability + %803 = "hi_spn.gaussian"(%arg20) {mean = -8.7942171096801758 : f64, stddev = 1.1913256645202637 : f64} : (f64) -> !hi_spn.probability + %804 = "hi_spn.gaussian"(%arg21) {mean = -10.075502395629883 : f64, stddev = 1.504949688911438 : f64} : (f64) -> !hi_spn.probability + %805 = "hi_spn.gaussian"(%arg22) {mean = -9.6366281509399414 : f64, stddev = 1.5902148485183716 : f64} : (f64) -> !hi_spn.probability + %806 = "hi_spn.gaussian"(%arg23) {mean = -10.366800308227539 : f64, stddev = 1.3276265859603882 : f64} : (f64) -> !hi_spn.probability + %807 = "hi_spn.gaussian"(%arg24) {mean = -12.23831844329834 : f64, stddev = 0.88488131761550903 : f64} : (f64) -> !hi_spn.probability + %808 = "hi_spn.gaussian"(%arg25) {mean = -11.33403205871582 : f64, stddev = 0.94600272178649902 : f64} : (f64) -> !hi_spn.probability + %809 = "hi_spn.product"(%783, %784, %785, %786, %787, %788, %789, %790, %791, %792, %793, %794, %795, %796, %797, %798, %799, %800, %801, %802, %803, %804, %805, %806, %807, %808) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %810 = "hi_spn.gaussian"(%arg0) {mean = -10.428524017333984 : f64, stddev = 1.2039626836776733 : f64} : (f64) -> !hi_spn.probability + %811 = "hi_spn.gaussian"(%arg1) {mean = -4.8005132675170898 : f64, stddev = 0.60425221920013428 : f64} : (f64) -> !hi_spn.probability + %812 = "hi_spn.gaussian"(%arg2) {mean = -1.7056089639663696 : f64, stddev = 0.77893060445785522 : f64} : (f64) -> !hi_spn.probability + %813 = "hi_spn.gaussian"(%arg3) {mean = -2.7625248432159424 : f64, stddev = 0.97498071193695068 : f64} : (f64) -> !hi_spn.probability + %814 = "hi_spn.gaussian"(%arg4) {mean = -0.58657383918762207 : f64, stddev = 0.80343109369277954 : f64} : (f64) -> !hi_spn.probability + %815 = "hi_spn.gaussian"(%arg5) {mean = -0.47867530584335327 : f64, stddev = 0.98265993595123291 : f64} : (f64) -> !hi_spn.probability + %816 = "hi_spn.gaussian"(%arg6) {mean = -2.658158540725708 : f64, stddev = 0.96323513984680175 : f64} : (f64) -> !hi_spn.probability + %817 = "hi_spn.gaussian"(%arg7) {mean = -2.3708949089050293 : f64, stddev = 1.0669902563095093 : f64} : (f64) -> !hi_spn.probability + %818 = "hi_spn.gaussian"(%arg8) {mean = -3.8972570896148682 : f64, stddev = 1.894842267036438 : f64} : (f64) -> !hi_spn.probability + %819 = "hi_spn.gaussian"(%arg9) {mean = -4.0222196578979492 : f64, stddev = 2.174738883972168 : f64} : (f64) -> !hi_spn.probability + %820 = "hi_spn.gaussian"(%arg10) {mean = -4.793734073638916 : f64, stddev = 2.2236061096191406 : f64} : (f64) -> !hi_spn.probability + %821 = "hi_spn.gaussian"(%arg11) {mean = -6.3218216896057129 : f64, stddev = 2.6918065547943115 : f64} : (f64) -> !hi_spn.probability + %822 = "hi_spn.gaussian"(%arg12) {mean = -7.8773069381713867 : f64, stddev = 2.0607681274414063 : f64} : (f64) -> !hi_spn.probability + %823 = "hi_spn.gaussian"(%arg13) {mean = -8.9604358673095703 : f64, stddev = 2.0187506675720215 : f64} : (f64) -> !hi_spn.probability + %824 = "hi_spn.gaussian"(%arg14) {mean = -10.637974739074707 : f64, stddev = 1.6277683973312378 : f64} : (f64) -> !hi_spn.probability + %825 = "hi_spn.gaussian"(%arg15) {mean = -11.691089630126953 : f64, stddev = 1.4333341121673584 : f64} : (f64) -> !hi_spn.probability + %826 = "hi_spn.gaussian"(%arg16) {mean = -12.62342357635498 : f64, stddev = 0.95259028673171997 : f64} : (f64) -> !hi_spn.probability + %827 = "hi_spn.gaussian"(%arg17) {mean = -13.144018173217773 : f64, stddev = 0.8991936445236206 : f64} : (f64) -> !hi_spn.probability + %828 = "hi_spn.gaussian"(%arg18) {mean = -13.304936408996582 : f64, stddev = 0.78223001956939697 : f64} : (f64) -> !hi_spn.probability + %829 = "hi_spn.gaussian"(%arg19) {mean = -12.739057540893555 : f64, stddev = 0.95756226778030396 : f64} : (f64) -> !hi_spn.probability + %830 = "hi_spn.gaussian"(%arg20) {mean = -11.31569766998291 : f64, stddev = 1.1008472442626953 : f64} : (f64) -> !hi_spn.probability + %831 = "hi_spn.gaussian"(%arg21) {mean = -11.27239990234375 : f64, stddev = 1.1140607595443726 : f64} : (f64) -> !hi_spn.probability + %832 = "hi_spn.gaussian"(%arg22) {mean = -12.766929626464844 : f64, stddev = 1.2298176288604736 : f64} : (f64) -> !hi_spn.probability + %833 = "hi_spn.gaussian"(%arg23) {mean = -14.051721572875977 : f64, stddev = 0.62666803598403931 : f64} : (f64) -> !hi_spn.probability + %834 = "hi_spn.gaussian"(%arg24) {mean = -13.54560661315918 : f64, stddev = 0.80309677124023438 : f64} : (f64) -> !hi_spn.probability + %835 = "hi_spn.gaussian"(%arg25) {mean = -11.836686134338379 : f64, stddev = 1.0280946493148804 : f64} : (f64) -> !hi_spn.probability + %836 = "hi_spn.product"(%810, %811, %812, %813, %814, %815, %816, %817, %818, %819, %820, %821, %822, %823, %824, %825, %826, %827, %828, %829, %830, %831, %832, %833, %834, %835) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %837 = "hi_spn.gaussian"(%arg0) {mean = -10.220756530761719 : f64, stddev = 1.269687294960022 : f64} : (f64) -> !hi_spn.probability + %838 = "hi_spn.gaussian"(%arg1) {mean = -4.830296516418457 : f64, stddev = 1.2894247770309448 : f64} : (f64) -> !hi_spn.probability + %839 = "hi_spn.gaussian"(%arg2) {mean = -2.711517333984375 : f64, stddev = 1.3992751836776733 : f64} : (f64) -> !hi_spn.probability + %840 = "hi_spn.gaussian"(%arg3) {mean = -3.985832691192627 : f64, stddev = 1.2450056076049805 : f64} : (f64) -> !hi_spn.probability + %841 = "hi_spn.gaussian"(%arg4) {mean = -2.3078665733337402 : f64, stddev = 2.1152658462524414 : f64} : (f64) -> !hi_spn.probability + %842 = "hi_spn.gaussian"(%arg5) {mean = -2.6697187423706055 : f64, stddev = 1.8269482851028442 : f64} : (f64) -> !hi_spn.probability + %843 = "hi_spn.gaussian"(%arg6) {mean = -3.6070373058319092 : f64, stddev = 1.1772325038909912 : f64} : (f64) -> !hi_spn.probability + %844 = "hi_spn.gaussian"(%arg7) {mean = -4.3406071662902832 : f64, stddev = 1.0189328193664551 : f64} : (f64) -> !hi_spn.probability + %845 = "hi_spn.gaussian"(%arg8) {mean = -4.7574715614318848 : f64, stddev = 1.0132516622543335 : f64} : (f64) -> !hi_spn.probability + %846 = "hi_spn.gaussian"(%arg9) {mean = -5.6476778984069824 : f64, stddev = 0.99200183153152466 : f64} : (f64) -> !hi_spn.probability + %847 = "hi_spn.gaussian"(%arg10) {mean = -5.5133571624755859 : f64, stddev = 1.1960428953170776 : f64} : (f64) -> !hi_spn.probability + %848 = "hi_spn.gaussian"(%arg11) {mean = -5.1300930976867676 : f64, stddev = 1.1877732276916504 : f64} : (f64) -> !hi_spn.probability + %849 = "hi_spn.gaussian"(%arg12) {mean = -5.1579627990722656 : f64, stddev = 1.3107385635375977 : f64} : (f64) -> !hi_spn.probability + %850 = "hi_spn.gaussian"(%arg13) {mean = -6.030118465423584 : f64, stddev = 1.5362046957015991 : f64} : (f64) -> !hi_spn.probability + %851 = "hi_spn.gaussian"(%arg14) {mean = -7.7390952110290527 : f64, stddev = 1.5368119478225708 : f64} : (f64) -> !hi_spn.probability + %852 = "hi_spn.gaussian"(%arg15) {mean = -10.220649719238281 : f64, stddev = 1.5343526601791382 : f64} : (f64) -> !hi_spn.probability + %853 = "hi_spn.gaussian"(%arg16) {mean = -10.98398494720459 : f64, stddev = 1.7683655023574829 : f64} : (f64) -> !hi_spn.probability + %854 = "hi_spn.gaussian"(%arg17) {mean = -11.664264678955078 : f64, stddev = 1.5676519870758057 : f64} : (f64) -> !hi_spn.probability + %855 = "hi_spn.gaussian"(%arg18) {mean = -11.84843635559082 : f64, stddev = 1.4938062429428101 : f64} : (f64) -> !hi_spn.probability + %856 = "hi_spn.gaussian"(%arg19) {mean = -11.573271751403809 : f64, stddev = 1.5986725091934204 : f64} : (f64) -> !hi_spn.probability + %857 = "hi_spn.gaussian"(%arg20) {mean = -11.375916481018066 : f64, stddev = 1.6578778028488159 : f64} : (f64) -> !hi_spn.probability + %858 = "hi_spn.gaussian"(%arg21) {mean = -11.044497489929199 : f64, stddev = 1.6201143264770508 : f64} : (f64) -> !hi_spn.probability + %859 = "hi_spn.gaussian"(%arg22) {mean = -11.694368362426758 : f64, stddev = 1.5433932542800903 : f64} : (f64) -> !hi_spn.probability + %860 = "hi_spn.gaussian"(%arg23) {mean = -13.058230400085449 : f64, stddev = 1.1935220956802368 : f64} : (f64) -> !hi_spn.probability + %861 = "hi_spn.gaussian"(%arg24) {mean = -13.361722946166992 : f64, stddev = 1.2640531063079834 : f64} : (f64) -> !hi_spn.probability + %862 = "hi_spn.gaussian"(%arg25) {mean = -12.735746383666992 : f64, stddev = 1.2274200916290283 : f64} : (f64) -> !hi_spn.probability + %863 = "hi_spn.product"(%837, %838, %839, %840, %841, %842, %843, %844, %845, %846, %847, %848, %849, %850, %851, %852, %853, %854, %855, %856, %857, %858, %859, %860, %861, %862) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %864 = "hi_spn.gaussian"(%arg0) {mean = -9.3214378356933593 : f64, stddev = 1.3451398611068726 : f64} : (f64) -> !hi_spn.probability + %865 = "hi_spn.gaussian"(%arg1) {mean = -5.2790961265563965 : f64, stddev = 1.6741771697998047 : f64} : (f64) -> !hi_spn.probability + %866 = "hi_spn.gaussian"(%arg2) {mean = -2.1202664375305176 : f64, stddev = 1.0750863552093506 : f64} : (f64) -> !hi_spn.probability + %867 = "hi_spn.gaussian"(%arg3) {mean = -2.5271093845367432 : f64, stddev = 1.1379309892654419 : f64} : (f64) -> !hi_spn.probability + %868 = "hi_spn.gaussian"(%arg4) {mean = -2.6375362873077393 : f64, stddev = 1.6175582408905029 : f64} : (f64) -> !hi_spn.probability + %869 = "hi_spn.gaussian"(%arg5) {mean = -1.8790634870529175 : f64, stddev = 1.2665959596633911 : f64} : (f64) -> !hi_spn.probability + %870 = "hi_spn.gaussian"(%arg6) {mean = -3.4772517681121826 : f64, stddev = 1.3788390159606934 : f64} : (f64) -> !hi_spn.probability + %871 = "hi_spn.gaussian"(%arg7) {mean = -4.6901941299438477 : f64, stddev = 1.2669582366943359 : f64} : (f64) -> !hi_spn.probability + %872 = "hi_spn.gaussian"(%arg8) {mean = -5.6875925064086914 : f64, stddev = 1.1460007429122925 : f64} : (f64) -> !hi_spn.probability + %873 = "hi_spn.gaussian"(%arg9) {mean = -6.9031453132629395 : f64, stddev = 0.86353880167007446 : f64} : (f64) -> !hi_spn.probability + %874 = "hi_spn.gaussian"(%arg10) {mean = -7.6546249389648438 : f64, stddev = 0.8104943037033081 : f64} : (f64) -> !hi_spn.probability + %875 = "hi_spn.gaussian"(%arg11) {mean = -8.0403594970703125 : f64, stddev = 1.2155488729476929 : f64} : (f64) -> !hi_spn.probability + %876 = "hi_spn.gaussian"(%arg12) {mean = -7.0720438957214355 : f64, stddev = 1.4507648944854736 : f64} : (f64) -> !hi_spn.probability + %877 = "hi_spn.gaussian"(%arg13) {mean = -6.2170372009277344 : f64, stddev = 1.2106430530548096 : f64} : (f64) -> !hi_spn.probability + %878 = "hi_spn.gaussian"(%arg14) {mean = -6.4435162544250488 : f64, stddev = 1.0974704027175903 : f64} : (f64) -> !hi_spn.probability + %879 = "hi_spn.gaussian"(%arg15) {mean = -7.1544451713562012 : f64, stddev = 1.046581506729126 : f64} : (f64) -> !hi_spn.probability + %880 = "hi_spn.gaussian"(%arg16) {mean = -6.9875087738037109 : f64, stddev = 1.1035842895507813 : f64} : (f64) -> !hi_spn.probability + %881 = "hi_spn.gaussian"(%arg17) {mean = -7.4790472984313965 : f64, stddev = 1.2008309364318848 : f64} : (f64) -> !hi_spn.probability + %882 = "hi_spn.gaussian"(%arg18) {mean = -8.7852764129638671 : f64, stddev = 1.2997614145278931 : f64} : (f64) -> !hi_spn.probability + %883 = "hi_spn.gaussian"(%arg19) {mean = -9.3296527862548828 : f64, stddev = 1.1600536108016968 : f64} : (f64) -> !hi_spn.probability + %884 = "hi_spn.gaussian"(%arg20) {mean = -9.6210813522338867 : f64, stddev = 1.1075938940048218 : f64} : (f64) -> !hi_spn.probability + %885 = "hi_spn.gaussian"(%arg21) {mean = -9.681161880493164 : f64, stddev = 1.0292993783950806 : f64} : (f64) -> !hi_spn.probability + %886 = "hi_spn.gaussian"(%arg22) {mean = -10.422371864318848 : f64, stddev = 1.1513679027557373 : f64} : (f64) -> !hi_spn.probability + %887 = "hi_spn.gaussian"(%arg23) {mean = -11.261441230773926 : f64, stddev = 1.0259401798248291 : f64} : (f64) -> !hi_spn.probability + %888 = "hi_spn.gaussian"(%arg24) {mean = -12.423006057739258 : f64, stddev = 1.0023843050003052 : f64} : (f64) -> !hi_spn.probability + %889 = "hi_spn.gaussian"(%arg25) {mean = -11.951251983642578 : f64, stddev = 0.79783016443252563 : f64} : (f64) -> !hi_spn.probability + %890 = "hi_spn.product"(%864, %865, %866, %867, %868, %869, %870, %871, %872, %873, %874, %875, %876, %877, %878, %879, %880, %881, %882, %883, %884, %885, %886, %887, %888, %889) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %891 = "hi_spn.gaussian"(%arg0) {mean = -9.4228515625 : f64, stddev = 1.0323067903518677 : f64} : (f64) -> !hi_spn.probability + %892 = "hi_spn.gaussian"(%arg1) {mean = -4.6211123466491699 : f64, stddev = 1.2912204265594482 : f64} : (f64) -> !hi_spn.probability + %893 = "hi_spn.gaussian"(%arg2) {mean = -2.2723679542541504 : f64, stddev = 1.3827084302902222 : f64} : (f64) -> !hi_spn.probability + %894 = "hi_spn.gaussian"(%arg3) {mean = -3.0209958553314209 : f64, stddev = 1.2571717500686646 : f64} : (f64) -> !hi_spn.probability + %895 = "hi_spn.gaussian"(%arg4) {mean = -2.06998610496521 : f64, stddev = 1.6390811204910278 : f64} : (f64) -> !hi_spn.probability + %896 = "hi_spn.gaussian"(%arg5) {mean = -2.1828310489654541 : f64, stddev = 1.4466178417205811 : f64} : (f64) -> !hi_spn.probability + %897 = "hi_spn.gaussian"(%arg6) {mean = -4.6011462211608887 : f64, stddev = 1.2203974723815918 : f64} : (f64) -> !hi_spn.probability + %898 = "hi_spn.gaussian"(%arg7) {mean = -5.3647317886352539 : f64, stddev = 1.0921448469161987 : f64} : (f64) -> !hi_spn.probability + %899 = "hi_spn.gaussian"(%arg8) {mean = -6.0953245162963867 : f64, stddev = 1.1261283159255981 : f64} : (f64) -> !hi_spn.probability + %900 = "hi_spn.gaussian"(%arg9) {mean = -6.4991641044616699 : f64, stddev = 1.1478918790817261 : f64} : (f64) -> !hi_spn.probability + %901 = "hi_spn.gaussian"(%arg10) {mean = -7.0230188369750977 : f64, stddev = 1.3185553550720215 : f64} : (f64) -> !hi_spn.probability + %902 = "hi_spn.gaussian"(%arg11) {mean = -7.2850451469421387 : f64, stddev = 1.5402446985244751 : f64} : (f64) -> !hi_spn.probability + %903 = "hi_spn.gaussian"(%arg12) {mean = -6.9771332740783691 : f64, stddev = 1.472931981086731 : f64} : (f64) -> !hi_spn.probability + %904 = "hi_spn.gaussian"(%arg13) {mean = -7.0290040969848633 : f64, stddev = 1.4514147043228149 : f64} : (f64) -> !hi_spn.probability + %905 = "hi_spn.gaussian"(%arg14) {mean = -7.7818036079406738 : f64, stddev = 1.3479229211807251 : f64} : (f64) -> !hi_spn.probability + %906 = "hi_spn.gaussian"(%arg15) {mean = -8.709686279296875 : f64, stddev = 1.093854546546936 : f64} : (f64) -> !hi_spn.probability + %907 = "hi_spn.gaussian"(%arg16) {mean = -8.3133516311645508 : f64, stddev = 1.1666648387908936 : f64} : (f64) -> !hi_spn.probability + %908 = "hi_spn.gaussian"(%arg17) {mean = -7.5990843772888184 : f64, stddev = 0.99834662675857544 : f64} : (f64) -> !hi_spn.probability + %909 = "hi_spn.gaussian"(%arg18) {mean = -8.7891874313354492 : f64, stddev = 0.80196368694305419 : f64} : (f64) -> !hi_spn.probability + %910 = "hi_spn.gaussian"(%arg19) {mean = -9.2691478729248046 : f64, stddev = 1.1922008991241455 : f64} : (f64) -> !hi_spn.probability + %911 = "hi_spn.gaussian"(%arg20) {mean = -9.0518321990966796 : f64, stddev = 0.84742933511734009 : f64} : (f64) -> !hi_spn.probability + %912 = "hi_spn.gaussian"(%arg21) {mean = -8.8434152603149414 : f64, stddev = 1.0089378356933594 : f64} : (f64) -> !hi_spn.probability + %913 = "hi_spn.gaussian"(%arg22) {mean = -8.6666898727416992 : f64, stddev = 1.0983072519302368 : f64} : (f64) -> !hi_spn.probability + %914 = "hi_spn.gaussian"(%arg23) {mean = -8.8150310516357421 : f64, stddev = 1.4879231452941895 : f64} : (f64) -> !hi_spn.probability + %915 = "hi_spn.gaussian"(%arg24) {mean = -9.7194318771362305 : f64, stddev = 1.0893490314483643 : f64} : (f64) -> !hi_spn.probability + %916 = "hi_spn.gaussian"(%arg25) {mean = -10.12714958190918 : f64, stddev = 1.2691963911056519 : f64} : (f64) -> !hi_spn.probability + %917 = "hi_spn.product"(%891, %892, %893, %894, %895, %896, %897, %898, %899, %900, %901, %902, %903, %904, %905, %906, %907, %908, %909, %910, %911, %912, %913, %914, %915, %916) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %918 = "hi_spn.gaussian"(%arg0) {mean = -10.436210632324219 : f64, stddev = 1.4779411554336548 : f64} : (f64) -> !hi_spn.probability + %919 = "hi_spn.gaussian"(%arg1) {mean = -4.0135188102722168 : f64, stddev = 0.92587637901306152 : f64} : (f64) -> !hi_spn.probability + %920 = "hi_spn.gaussian"(%arg2) {mean = -1.7208881378173828 : f64, stddev = 0.94762784242630005 : f64} : (f64) -> !hi_spn.probability + %921 = "hi_spn.gaussian"(%arg3) {mean = -3.1651289463043213 : f64, stddev = 0.81832879781723022 : f64} : (f64) -> !hi_spn.probability + %922 = "hi_spn.gaussian"(%arg4) {mean = -1.8401142358779907 : f64, stddev = 1.1011760234832764 : f64} : (f64) -> !hi_spn.probability + %923 = "hi_spn.gaussian"(%arg5) {mean = -2.6372189521789551 : f64, stddev = 0.71314060688018799 : f64} : (f64) -> !hi_spn.probability + %924 = "hi_spn.gaussian"(%arg6) {mean = -5.3622751235961914 : f64, stddev = 0.75845551490783691 : f64} : (f64) -> !hi_spn.probability + %925 = "hi_spn.gaussian"(%arg7) {mean = -6.2630615234375 : f64, stddev = 1.0034798383712769 : f64} : (f64) -> !hi_spn.probability + %926 = "hi_spn.gaussian"(%arg8) {mean = -6.9896492958068848 : f64, stddev = 0.99534767866134644 : f64} : (f64) -> !hi_spn.probability + %927 = "hi_spn.gaussian"(%arg9) {mean = -8.1565103530883789 : f64, stddev = 0.85185080766677856 : f64} : (f64) -> !hi_spn.probability + %928 = "hi_spn.gaussian"(%arg10) {mean = -9.2875986099243164 : f64, stddev = 0.6341976523399353 : f64} : (f64) -> !hi_spn.probability + %929 = "hi_spn.gaussian"(%arg11) {mean = -9.8726491928100586 : f64, stddev = 0.44093248248100281 : f64} : (f64) -> !hi_spn.probability + %930 = "hi_spn.gaussian"(%arg12) {mean = -10.007036209106445 : f64, stddev = 0.80108165740966796 : f64} : (f64) -> !hi_spn.probability + %931 = "hi_spn.gaussian"(%arg13) {mean = -9.7574748992919921 : f64, stddev = 0.97871679067611694 : f64} : (f64) -> !hi_spn.probability + %932 = "hi_spn.gaussian"(%arg14) {mean = -8.6751565933227539 : f64, stddev = 1.2305699586868286 : f64} : (f64) -> !hi_spn.probability + %933 = "hi_spn.gaussian"(%arg15) {mean = -6.661280632019043 : f64, stddev = 0.79650658369064331 : f64} : (f64) -> !hi_spn.probability + %934 = "hi_spn.gaussian"(%arg16) {mean = -5.997164249420166 : f64, stddev = 0.75940555334091187 : f64} : (f64) -> !hi_spn.probability + %935 = "hi_spn.gaussian"(%arg17) {mean = -6.2550802230834961 : f64, stddev = 0.87985718250274658 : f64} : (f64) -> !hi_spn.probability + %936 = "hi_spn.gaussian"(%arg18) {mean = -7.3322205543518066 : f64, stddev = 1.2232767343521118 : f64} : (f64) -> !hi_spn.probability + %937 = "hi_spn.gaussian"(%arg19) {mean = -7.7378687858581543 : f64, stddev = 0.85412430763244629 : f64} : (f64) -> !hi_spn.probability + %938 = "hi_spn.gaussian"(%arg20) {mean = -7.8974146842956543 : f64, stddev = 0.77754616737365723 : f64} : (f64) -> !hi_spn.probability + %939 = "hi_spn.gaussian"(%arg21) {mean = -8.5601415634155273 : f64, stddev = 1.1521861553192139 : f64} : (f64) -> !hi_spn.probability + %940 = "hi_spn.gaussian"(%arg22) {mean = -9.2366037368774414 : f64, stddev = 0.98268908262252808 : f64} : (f64) -> !hi_spn.probability + %941 = "hi_spn.gaussian"(%arg23) {mean = -11.197366714477539 : f64, stddev = 0.87608838081359863 : f64} : (f64) -> !hi_spn.probability + %942 = "hi_spn.gaussian"(%arg24) {mean = -12.91578197479248 : f64, stddev = 0.87993723154067993 : f64} : (f64) -> !hi_spn.probability + %943 = "hi_spn.gaussian"(%arg25) {mean = -11.93703556060791 : f64, stddev = 0.97124397754669189 : f64} : (f64) -> !hi_spn.probability + %944 = "hi_spn.product"(%918, %919, %920, %921, %922, %923, %924, %925, %926, %927, %928, %929, %930, %931, %932, %933, %934, %935, %936, %937, %938, %939, %940, %941, %942, %943) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %945 = "hi_spn.gaussian"(%arg0) {mean = -9.833597183227539 : f64, stddev = 1.0907855033874512 : f64} : (f64) -> !hi_spn.probability + %946 = "hi_spn.gaussian"(%arg1) {mean = -4.0390524864196777 : f64, stddev = 0.78612041473388671 : f64} : (f64) -> !hi_spn.probability + %947 = "hi_spn.gaussian"(%arg2) {mean = -1.4742909669876099 : f64, stddev = 0.68472737073898315 : f64} : (f64) -> !hi_spn.probability + %948 = "hi_spn.gaussian"(%arg3) {mean = -2.7696833610534668 : f64, stddev = 0.68950402736663818 : f64} : (f64) -> !hi_spn.probability + %949 = "hi_spn.gaussian"(%arg4) {mean = -1.4038190841674805 : f64, stddev = 1.3762784004211426 : f64} : (f64) -> !hi_spn.probability + %950 = "hi_spn.gaussian"(%arg5) {mean = -1.7416650056838989 : f64, stddev = 0.91569679975509644 : f64} : (f64) -> !hi_spn.probability + %951 = "hi_spn.gaussian"(%arg6) {mean = -4.9131836891174316 : f64, stddev = 0.75484305620193481 : f64} : (f64) -> !hi_spn.probability + %952 = "hi_spn.gaussian"(%arg7) {mean = -5.4134435653686523 : f64, stddev = 1.0748820304870605 : f64} : (f64) -> !hi_spn.probability + %953 = "hi_spn.gaussian"(%arg8) {mean = -6.2267675399780273 : f64, stddev = 0.84619265794754028 : f64} : (f64) -> !hi_spn.probability + %954 = "hi_spn.gaussian"(%arg9) {mean = -7.4962115287780762 : f64, stddev = 0.88376325368881226 : f64} : (f64) -> !hi_spn.probability + %955 = "hi_spn.gaussian"(%arg10) {mean = -8.263010025024414 : f64, stddev = 0.55605345964431763 : f64} : (f64) -> !hi_spn.probability + %956 = "hi_spn.gaussian"(%arg11) {mean = -8.7847108840942383 : f64, stddev = 0.52556300163269043 : f64} : (f64) -> !hi_spn.probability + %957 = "hi_spn.gaussian"(%arg12) {mean = -8.6225795745849609 : f64, stddev = 0.74376451969146729 : f64} : (f64) -> !hi_spn.probability + %958 = "hi_spn.gaussian"(%arg13) {mean = -8.0213594436645508 : f64, stddev = 0.90518307685852051 : f64} : (f64) -> !hi_spn.probability + %959 = "hi_spn.gaussian"(%arg14) {mean = -6.6528916358947754 : f64, stddev = 1.1090271472930908 : f64} : (f64) -> !hi_spn.probability + %960 = "hi_spn.gaussian"(%arg15) {mean = -4.7926692962646484 : f64, stddev = 0.74751162528991699 : f64} : (f64) -> !hi_spn.probability + %961 = "hi_spn.gaussian"(%arg16) {mean = -4.3798871040344238 : f64, stddev = 0.75730806589126587 : f64} : (f64) -> !hi_spn.probability + %962 = "hi_spn.gaussian"(%arg17) {mean = -4.7557125091552734 : f64, stddev = 0.53218239545822144 : f64} : (f64) -> !hi_spn.probability + %963 = "hi_spn.gaussian"(%arg18) {mean = -6.5564794540405273 : f64, stddev = 0.60670953989028931 : f64} : (f64) -> !hi_spn.probability + %964 = "hi_spn.gaussian"(%arg19) {mean = -6.9815449714660645 : f64, stddev = 0.95475029945373535 : f64} : (f64) -> !hi_spn.probability + %965 = "hi_spn.gaussian"(%arg20) {mean = -7.3663234710693359 : f64, stddev = 0.82087987661361694 : f64} : (f64) -> !hi_spn.probability + %966 = "hi_spn.gaussian"(%arg21) {mean = -7.5917763710021973 : f64, stddev = 0.94074583053588867 : f64} : (f64) -> !hi_spn.probability + %967 = "hi_spn.gaussian"(%arg22) {mean = -8.2853860855102539 : f64, stddev = 0.95664125680923462 : f64} : (f64) -> !hi_spn.probability + %968 = "hi_spn.gaussian"(%arg23) {mean = -10.092695236206055 : f64, stddev = 1.2007377147674561 : f64} : (f64) -> !hi_spn.probability + %969 = "hi_spn.gaussian"(%arg24) {mean = -12.154717445373535 : f64, stddev = 1.2859437465667725 : f64} : (f64) -> !hi_spn.probability + %970 = "hi_spn.gaussian"(%arg25) {mean = -11.675235748291016 : f64, stddev = 1.0932331085205078 : f64} : (f64) -> !hi_spn.probability + %971 = "hi_spn.product"(%945, %946, %947, %948, %949, %950, %951, %952, %953, %954, %955, %956, %957, %958, %959, %960, %961, %962, %963, %964, %965, %966, %967, %968, %969, %970) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %972 = "hi_spn.gaussian"(%arg0) {mean = -10.489375114440918 : f64, stddev = 1.226300835609436 : f64} : (f64) -> !hi_spn.probability + %973 = "hi_spn.gaussian"(%arg1) {mean = -9.1801462173461914 : f64, stddev = 1.7605600357055664 : f64} : (f64) -> !hi_spn.probability + %974 = "hi_spn.gaussian"(%arg2) {mean = -0.81491231918334961 : f64, stddev = 0.33945226669311523 : f64} : (f64) -> !hi_spn.probability + %975 = "hi_spn.gaussian"(%arg3) {mean = -0.83029347658157349 : f64, stddev = 0.49317523837089539 : f64} : (f64) -> !hi_spn.probability + %976 = "hi_spn.gaussian"(%arg4) {mean = -7.7032761573791504 : f64, stddev = 1.6647742986679077 : f64} : (f64) -> !hi_spn.probability + %977 = "hi_spn.gaussian"(%arg5) {mean = -3.469184398651123 : f64, stddev = 1.0174826383590698 : f64} : (f64) -> !hi_spn.probability + %978 = "hi_spn.gaussian"(%arg6) {mean = -5.1385297775268555 : f64, stddev = 1.0903304815292358 : f64} : (f64) -> !hi_spn.probability + %979 = "hi_spn.gaussian"(%arg7) {mean = -5.9208335876464844 : f64, stddev = 1.0827924013137817 : f64} : (f64) -> !hi_spn.probability + %980 = "hi_spn.gaussian"(%arg8) {mean = -6.8205480575561523 : f64, stddev = 0.94009685516357421 : f64} : (f64) -> !hi_spn.probability + %981 = "hi_spn.gaussian"(%arg9) {mean = -7.4760260581970215 : f64, stddev = 1.5458405017852783 : f64} : (f64) -> !hi_spn.probability + %982 = "hi_spn.gaussian"(%arg10) {mean = -9.0852432250976563 : f64, stddev = 0.9363328218460083 : f64} : (f64) -> !hi_spn.probability + %983 = "hi_spn.gaussian"(%arg11) {mean = -9.6024837493896484 : f64, stddev = 0.89487618207931519 : f64} : (f64) -> !hi_spn.probability + %984 = "hi_spn.gaussian"(%arg12) {mean = -9.7949123382568359 : f64, stddev = 1.3379267454147339 : f64} : (f64) -> !hi_spn.probability + %985 = "hi_spn.gaussian"(%arg13) {mean = -8.8436574935913086 : f64, stddev = 1.3269118070602417 : f64} : (f64) -> !hi_spn.probability + %986 = "hi_spn.gaussian"(%arg14) {mean = -9.3430652618408203 : f64, stddev = 1.3181419372558594 : f64} : (f64) -> !hi_spn.probability + %987 = "hi_spn.gaussian"(%arg15) {mean = -8.0937795639038086 : f64, stddev = 1.0623131990432739 : f64} : (f64) -> !hi_spn.probability + %988 = "hi_spn.gaussian"(%arg16) {mean = -7.2502899169921875 : f64, stddev = 1.1114747524261475 : f64} : (f64) -> !hi_spn.probability + %989 = "hi_spn.gaussian"(%arg17) {mean = -8.3913412094116211 : f64, stddev = 1.0131533145904541 : f64} : (f64) -> !hi_spn.probability + %990 = "hi_spn.gaussian"(%arg18) {mean = -10.440448760986328 : f64, stddev = 1.3407052755355835 : f64} : (f64) -> !hi_spn.probability + %991 = "hi_spn.gaussian"(%arg19) {mean = -11.747071266174316 : f64, stddev = 1.0393321514129639 : f64} : (f64) -> !hi_spn.probability + %992 = "hi_spn.gaussian"(%arg20) {mean = -12.200018882751465 : f64, stddev = 0.93863022327423095 : f64} : (f64) -> !hi_spn.probability + %993 = "hi_spn.gaussian"(%arg21) {mean = -10.82451343536377 : f64, stddev = 0.82789593935012817 : f64} : (f64) -> !hi_spn.probability + %994 = "hi_spn.gaussian"(%arg22) {mean = -11.226709365844727 : f64, stddev = 0.85984188318252563 : f64} : (f64) -> !hi_spn.probability + %995 = "hi_spn.gaussian"(%arg23) {mean = -12.170046806335449 : f64, stddev = 0.64912641048431396 : f64} : (f64) -> !hi_spn.probability + %996 = "hi_spn.gaussian"(%arg24) {mean = -13.209172248840332 : f64, stddev = 0.52552169561386108 : f64} : (f64) -> !hi_spn.probability + %997 = "hi_spn.gaussian"(%arg25) {mean = -12.816998481750488 : f64, stddev = 1.0517578125 : f64} : (f64) -> !hi_spn.probability + %998 = "hi_spn.product"(%972, %973, %974, %975, %976, %977, %978, %979, %980, %981, %982, %983, %984, %985, %986, %987, %988, %989, %990, %991, %992, %993, %994, %995, %996, %997) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %999 = "hi_spn.gaussian"(%arg0) {mean = -10.819424629211426 : f64, stddev = 1.6672060489654541 : f64} : (f64) -> !hi_spn.probability + %1000 = "hi_spn.gaussian"(%arg1) {mean = -4.6306056976318359 : f64, stddev = 0.65040570497512817 : f64} : (f64) -> !hi_spn.probability + %1001 = "hi_spn.gaussian"(%arg2) {mean = -2.5265374183654785 : f64, stddev = 0.85881149768829345 : f64} : (f64) -> !hi_spn.probability + %1002 = "hi_spn.gaussian"(%arg3) {mean = -4.3654990196228027 : f64, stddev = 1.2949390411376953 : f64} : (f64) -> !hi_spn.probability + %1003 = "hi_spn.gaussian"(%arg4) {mean = -4.0098605155944824 : f64, stddev = 0.73030763864517212 : f64} : (f64) -> !hi_spn.probability + %1004 = "hi_spn.gaussian"(%arg5) {mean = -4.7656607627868652 : f64, stddev = 0.7860444188117981 : f64} : (f64) -> !hi_spn.probability + %1005 = "hi_spn.gaussian"(%arg6) {mean = -5.4859867095947266 : f64, stddev = 1.1014890670776367 : f64} : (f64) -> !hi_spn.probability + %1006 = "hi_spn.gaussian"(%arg7) {mean = -6.5465569496154785 : f64, stddev = 1.0494014024734497 : f64} : (f64) -> !hi_spn.probability + %1007 = "hi_spn.gaussian"(%arg8) {mean = -7.0204353332519531 : f64, stddev = 1.2330511808395386 : f64} : (f64) -> !hi_spn.probability + %1008 = "hi_spn.gaussian"(%arg9) {mean = -8.8022174835205078 : f64, stddev = 1.2973226308822632 : f64} : (f64) -> !hi_spn.probability + %1009 = "hi_spn.gaussian"(%arg10) {mean = -9.832362174987793 : f64, stddev = 1.0624798536300659 : f64} : (f64) -> !hi_spn.probability + %1010 = "hi_spn.gaussian"(%arg11) {mean = -10.014893531799316 : f64, stddev = 1.4548715353012085 : f64} : (f64) -> !hi_spn.probability + %1011 = "hi_spn.gaussian"(%arg12) {mean = -9.663395881652832 : f64, stddev = 1.4171836376190186 : f64} : (f64) -> !hi_spn.probability + %1012 = "hi_spn.gaussian"(%arg13) {mean = -9.7386913299560546 : f64, stddev = 1.3236730098724365 : f64} : (f64) -> !hi_spn.probability + %1013 = "hi_spn.gaussian"(%arg14) {mean = -10.56541633605957 : f64, stddev = 1.0107494592666626 : f64} : (f64) -> !hi_spn.probability + %1014 = "hi_spn.gaussian"(%arg15) {mean = -9.5219125747680664 : f64, stddev = 1.0399661064147949 : f64} : (f64) -> !hi_spn.probability + %1015 = "hi_spn.gaussian"(%arg16) {mean = -8.566990852355957 : f64, stddev = 1.3697303533554077 : f64} : (f64) -> !hi_spn.probability + %1016 = "hi_spn.gaussian"(%arg17) {mean = -8.9433822631835938 : f64, stddev = 1.112363338470459 : f64} : (f64) -> !hi_spn.probability + %1017 = "hi_spn.gaussian"(%arg18) {mean = -10.231456756591797 : f64, stddev = 1.2360868453979492 : f64} : (f64) -> !hi_spn.probability + %1018 = "hi_spn.gaussian"(%arg19) {mean = -11.765486717224121 : f64, stddev = 1.7491947412490845 : f64} : (f64) -> !hi_spn.probability + %1019 = "hi_spn.gaussian"(%arg20) {mean = -11.70713996887207 : f64, stddev = 1.7856770753860474 : f64} : (f64) -> !hi_spn.probability + %1020 = "hi_spn.gaussian"(%arg21) {mean = -11.096717834472656 : f64, stddev = 1.4486944675445557 : f64} : (f64) -> !hi_spn.probability + %1021 = "hi_spn.gaussian"(%arg22) {mean = -11.03333854675293 : f64, stddev = 1.1830134391784668 : f64} : (f64) -> !hi_spn.probability + %1022 = "hi_spn.gaussian"(%arg23) {mean = -11.091206550598145 : f64, stddev = 1.1412117481231689 : f64} : (f64) -> !hi_spn.probability + %1023 = "hi_spn.gaussian"(%arg24) {mean = -12.257561683654785 : f64, stddev = 1.2616052627563477 : f64} : (f64) -> !hi_spn.probability + %1024 = "hi_spn.gaussian"(%arg25) {mean = -12.690559387207031 : f64, stddev = 1.4887253046035767 : f64} : (f64) -> !hi_spn.probability + %1025 = "hi_spn.product"(%999, %1000, %1001, %1002, %1003, %1004, %1005, %1006, %1007, %1008, %1009, %1010, %1011, %1012, %1013, %1014, %1015, %1016, %1017, %1018, %1019, %1020, %1021, %1022, %1023, %1024) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %1026 = "hi_spn.gaussian"(%arg0) {mean = -9.8382110595703125 : f64, stddev = 1.5572248697280884 : f64} : (f64) -> !hi_spn.probability + %1027 = "hi_spn.gaussian"(%arg1) {mean = -5.2357525825500488 : f64, stddev = 1.2814018726348877 : f64} : (f64) -> !hi_spn.probability + %1028 = "hi_spn.gaussian"(%arg2) {mean = -2.6223793029785156 : f64, stddev = 1.4195970296859741 : f64} : (f64) -> !hi_spn.probability + %1029 = "hi_spn.gaussian"(%arg3) {mean = -3.8391854763031006 : f64, stddev = 1.2860488891601563 : f64} : (f64) -> !hi_spn.probability + %1030 = "hi_spn.gaussian"(%arg4) {mean = -4.197566032409668 : f64, stddev = 1.4915007352828979 : f64} : (f64) -> !hi_spn.probability + %1031 = "hi_spn.gaussian"(%arg5) {mean = -4.3215475082397461 : f64, stddev = 1.5362021923065186 : f64} : (f64) -> !hi_spn.probability + %1032 = "hi_spn.gaussian"(%arg6) {mean = -5.0924887657165527 : f64, stddev = 1.2099570035934448 : f64} : (f64) -> !hi_spn.probability + %1033 = "hi_spn.gaussian"(%arg7) {mean = -5.7551736831665039 : f64, stddev = 1.0074242353439331 : f64} : (f64) -> !hi_spn.probability + %1034 = "hi_spn.gaussian"(%arg8) {mean = -6.3504877090454102 : f64, stddev = 0.92476552724838257 : f64} : (f64) -> !hi_spn.probability + %1035 = "hi_spn.gaussian"(%arg9) {mean = -7.2732639312744141 : f64, stddev = 1.0113298892974854 : f64} : (f64) -> !hi_spn.probability + %1036 = "hi_spn.gaussian"(%arg10) {mean = -7.5051403045654296 : f64, stddev = 1.333922266960144 : f64} : (f64) -> !hi_spn.probability + %1037 = "hi_spn.gaussian"(%arg11) {mean = -7.6550216674804688 : f64, stddev = 1.1054495573043823 : f64} : (f64) -> !hi_spn.probability + %1038 = "hi_spn.gaussian"(%arg12) {mean = -7.4450759887695313 : f64, stddev = 0.99781680107116699 : f64} : (f64) -> !hi_spn.probability + %1039 = "hi_spn.gaussian"(%arg13) {mean = -7.5736489295959473 : f64, stddev = 1.1368300914764404 : f64} : (f64) -> !hi_spn.probability + %1040 = "hi_spn.gaussian"(%arg14) {mean = -8.6893730163574219 : f64, stddev = 1.0895533561706543 : f64} : (f64) -> !hi_spn.probability + %1041 = "hi_spn.gaussian"(%arg15) {mean = -8.3181896209716796 : f64, stddev = 1.132673978805542 : f64} : (f64) -> !hi_spn.probability + %1042 = "hi_spn.gaussian"(%arg16) {mean = -7.9254050254821777 : f64, stddev = 1.337899923324585 : f64} : (f64) -> !hi_spn.probability + %1043 = "hi_spn.gaussian"(%arg17) {mean = -8.8909854888916015 : f64, stddev = 1.2344081401824951 : f64} : (f64) -> !hi_spn.probability + %1044 = "hi_spn.gaussian"(%arg18) {mean = -10.483150482177734 : f64, stddev = 1.3635133504867554 : f64} : (f64) -> !hi_spn.probability + %1045 = "hi_spn.gaussian"(%arg19) {mean = -10.840538024902344 : f64, stddev = 1.1403928995132446 : f64} : (f64) -> !hi_spn.probability + %1046 = "hi_spn.gaussian"(%arg20) {mean = -11.156883239746094 : f64, stddev = 1.3845118284225464 : f64} : (f64) -> !hi_spn.probability + %1047 = "hi_spn.gaussian"(%arg21) {mean = -10.875722885131836 : f64, stddev = 0.96912670135498046 : f64} : (f64) -> !hi_spn.probability + %1048 = "hi_spn.gaussian"(%arg22) {mean = -11.36088752746582 : f64, stddev = 1.0131925344467163 : f64} : (f64) -> !hi_spn.probability + %1049 = "hi_spn.gaussian"(%arg23) {mean = -11.798334121704102 : f64, stddev = 1.2232018709182739 : f64} : (f64) -> !hi_spn.probability + %1050 = "hi_spn.gaussian"(%arg24) {mean = -12.316329002380371 : f64, stddev = 1.3515380620956421 : f64} : (f64) -> !hi_spn.probability + %1051 = "hi_spn.gaussian"(%arg25) {mean = -12.124046325683594 : f64, stddev = 1.2149113416671753 : f64} : (f64) -> !hi_spn.probability + %1052 = "hi_spn.product"(%1026, %1027, %1028, %1029, %1030, %1031, %1032, %1033, %1034, %1035, %1036, %1037, %1038, %1039, %1040, %1041, %1042, %1043, %1044, %1045, %1046, %1047, %1048, %1049, %1050, %1051) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %1053 = "hi_spn.gaussian"(%arg0) {mean = -7.2309999465942383 : f64, stddev = 1.2445898056030273 : f64} : (f64) -> !hi_spn.probability + %1054 = "hi_spn.gaussian"(%arg1) {mean = -6.6493148803710938 : f64, stddev = 1.2343853712081909 : f64} : (f64) -> !hi_spn.probability + %1055 = "hi_spn.gaussian"(%arg2) {mean = -5.7628579139709473 : f64, stddev = 1.8869490623474121 : f64} : (f64) -> !hi_spn.probability + %1056 = "hi_spn.gaussian"(%arg3) {mean = -5.459449291229248 : f64, stddev = 1.7240660190582275 : f64} : (f64) -> !hi_spn.probability + %1057 = "hi_spn.gaussian"(%arg4) {mean = -5.2457284927368164 : f64, stddev = 1.1228725910186768 : f64} : (f64) -> !hi_spn.probability + %1058 = "hi_spn.gaussian"(%arg5) {mean = -4.7064261436462402 : f64, stddev = 1.1334860324859619 : f64} : (f64) -> !hi_spn.probability + %1059 = "hi_spn.gaussian"(%arg6) {mean = -4.8315496444702148 : f64, stddev = 1.0474543571472168 : f64} : (f64) -> !hi_spn.probability + %1060 = "hi_spn.gaussian"(%arg7) {mean = -5.0882172584533691 : f64, stddev = 0.94963991641998291 : f64} : (f64) -> !hi_spn.probability + %1061 = "hi_spn.gaussian"(%arg8) {mean = -5.2574496269226074 : f64, stddev = 0.91658270359039306 : f64} : (f64) -> !hi_spn.probability + %1062 = "hi_spn.gaussian"(%arg9) {mean = -5.6460046768188477 : f64, stddev = 1.280026912689209 : f64} : (f64) -> !hi_spn.probability + %1063 = "hi_spn.gaussian"(%arg10) {mean = -6.0984697341918945 : f64, stddev = 1.3769861459732056 : f64} : (f64) -> !hi_spn.probability + %1064 = "hi_spn.gaussian"(%arg11) {mean = -6.0819640159606934 : f64, stddev = 1.4242912530899048 : f64} : (f64) -> !hi_spn.probability + %1065 = "hi_spn.gaussian"(%arg12) {mean = -5.8782033920288086 : f64, stddev = 1.46324622631073 : f64} : (f64) -> !hi_spn.probability + %1066 = "hi_spn.gaussian"(%arg13) {mean = -5.5773954391479492 : f64, stddev = 1.4371429681777954 : f64} : (f64) -> !hi_spn.probability + %1067 = "hi_spn.gaussian"(%arg14) {mean = -5.3658294677734375 : f64, stddev = 1.2640963792800903 : f64} : (f64) -> !hi_spn.probability + %1068 = "hi_spn.gaussian"(%arg15) {mean = -5.5418314933776855 : f64, stddev = 1.3779838085174561 : f64} : (f64) -> !hi_spn.probability + %1069 = "hi_spn.gaussian"(%arg16) {mean = -5.5687675476074219 : f64, stddev = 1.2168639898300171 : f64} : (f64) -> !hi_spn.probability + %1070 = "hi_spn.gaussian"(%arg17) {mean = -5.2717933654785156 : f64, stddev = 1.0420016050338745 : f64} : (f64) -> !hi_spn.probability + %1071 = "hi_spn.gaussian"(%arg18) {mean = -5.5776395797729492 : f64, stddev = 0.9799433946609497 : f64} : (f64) -> !hi_spn.probability + %1072 = "hi_spn.gaussian"(%arg19) {mean = -5.4936866760253906 : f64, stddev = 1.2099307775497437 : f64} : (f64) -> !hi_spn.probability + %1073 = "hi_spn.gaussian"(%arg20) {mean = -5.1846704483032227 : f64, stddev = 1.1288354396820068 : f64} : (f64) -> !hi_spn.probability + %1074 = "hi_spn.gaussian"(%arg21) {mean = -5.2059946060180664 : f64, stddev = 1.112478494644165 : f64} : (f64) -> !hi_spn.probability + %1075 = "hi_spn.gaussian"(%arg22) {mean = -5.1526393890380859 : f64, stddev = 1.3980242013931274 : f64} : (f64) -> !hi_spn.probability + %1076 = "hi_spn.gaussian"(%arg23) {mean = -5.5041618347167969 : f64, stddev = 1.6479111909866333 : f64} : (f64) -> !hi_spn.probability + %1077 = "hi_spn.gaussian"(%arg24) {mean = -5.8385224342346191 : f64, stddev = 1.6687132120132446 : f64} : (f64) -> !hi_spn.probability + %1078 = "hi_spn.gaussian"(%arg25) {mean = -6.1566195487976074 : f64, stddev = 1.9301201105117798 : f64} : (f64) -> !hi_spn.probability + %1079 = "hi_spn.product"(%1053, %1054, %1055, %1056, %1057, %1058, %1059, %1060, %1061, %1062, %1063, %1064, %1065, %1066, %1067, %1068, %1069, %1070, %1071, %1072, %1073, %1074, %1075, %1076, %1077, %1078) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %1080 = "hi_spn.gaussian"(%arg0) {mean = -8.0718984603881836 : f64, stddev = 0.64068573713302612 : f64} : (f64) -> !hi_spn.probability + %1081 = "hi_spn.gaussian"(%arg1) {mean = -8.0720939636230469 : f64, stddev = 0.68794006109237671 : f64} : (f64) -> !hi_spn.probability + %1082 = "hi_spn.gaussian"(%arg2) {mean = -7.081303596496582 : f64, stddev = 1.4512956142425537 : f64} : (f64) -> !hi_spn.probability + %1083 = "hi_spn.gaussian"(%arg3) {mean = -6.5840411186218262 : f64, stddev = 1.4161738157272339 : f64} : (f64) -> !hi_spn.probability + %1084 = "hi_spn.gaussian"(%arg4) {mean = -6.8991994857788086 : f64, stddev = 0.66820621490478516 : f64} : (f64) -> !hi_spn.probability + %1085 = "hi_spn.gaussian"(%arg5) {mean = -6.815464973449707 : f64, stddev = 1.0969631671905518 : f64} : (f64) -> !hi_spn.probability + %1086 = "hi_spn.gaussian"(%arg6) {mean = -6.7508015632629395 : f64, stddev = 0.94350093603134155 : f64} : (f64) -> !hi_spn.probability + %1087 = "hi_spn.gaussian"(%arg7) {mean = -6.85614013671875 : f64, stddev = 0.93601173162460327 : f64} : (f64) -> !hi_spn.probability + %1088 = "hi_spn.gaussian"(%arg8) {mean = -7.2362642288208008 : f64, stddev = 0.76955103874206543 : f64} : (f64) -> !hi_spn.probability + %1089 = "hi_spn.gaussian"(%arg9) {mean = -7.8254504203796387 : f64, stddev = 0.7439727783203125 : f64} : (f64) -> !hi_spn.probability + %1090 = "hi_spn.gaussian"(%arg10) {mean = -8.3638067245483398 : f64, stddev = 0.70645099878311157 : f64} : (f64) -> !hi_spn.probability + %1091 = "hi_spn.gaussian"(%arg11) {mean = -8.6499233245849609 : f64, stddev = 0.54317241907119751 : f64} : (f64) -> !hi_spn.probability + %1092 = "hi_spn.gaussian"(%arg12) {mean = -8.3376340866088867 : f64, stddev = 0.69814634323120117 : f64} : (f64) -> !hi_spn.probability + %1093 = "hi_spn.gaussian"(%arg13) {mean = -7.3728580474853516 : f64, stddev = 0.9181816577911377 : f64} : (f64) -> !hi_spn.probability + %1094 = "hi_spn.gaussian"(%arg14) {mean = -6.1138381958007813 : f64, stddev = 0.89531338214874267 : f64} : (f64) -> !hi_spn.probability + %1095 = "hi_spn.gaussian"(%arg15) {mean = -6.0433230400085449 : f64, stddev = 1.3080294132232666 : f64} : (f64) -> !hi_spn.probability + %1096 = "hi_spn.gaussian"(%arg16) {mean = -4.8985066413879395 : f64, stddev = 1.165818452835083 : f64} : (f64) -> !hi_spn.probability + %1097 = "hi_spn.gaussian"(%arg17) {mean = -4.0946245193481445 : f64, stddev = 1.0064244270324707 : f64} : (f64) -> !hi_spn.probability + %1098 = "hi_spn.gaussian"(%arg18) {mean = -4.5328187942504883 : f64, stddev = 1.1166538000106812 : f64} : (f64) -> !hi_spn.probability + %1099 = "hi_spn.gaussian"(%arg19) {mean = -3.6615931987762451 : f64, stddev = 1.2922987937927246 : f64} : (f64) -> !hi_spn.probability + %1100 = "hi_spn.gaussian"(%arg20) {mean = -4.2720804214477539 : f64, stddev = 1.5321072340011597 : f64} : (f64) -> !hi_spn.probability + %1101 = "hi_spn.gaussian"(%arg21) {mean = -5.359041690826416 : f64, stddev = 1.3452335596084595 : f64} : (f64) -> !hi_spn.probability + %1102 = "hi_spn.gaussian"(%arg22) {mean = -5.5917234420776367 : f64, stddev = 1.2890455722808838 : f64} : (f64) -> !hi_spn.probability + %1103 = "hi_spn.gaussian"(%arg23) {mean = -5.7564973831176758 : f64, stddev = 1.2081470489501953 : f64} : (f64) -> !hi_spn.probability + %1104 = "hi_spn.gaussian"(%arg24) {mean = -6.1401138305664063 : f64, stddev = 1.4194154739379883 : f64} : (f64) -> !hi_spn.probability + %1105 = "hi_spn.gaussian"(%arg25) {mean = -6.6150164604187012 : f64, stddev = 1.3413540124893188 : f64} : (f64) -> !hi_spn.probability + %1106 = "hi_spn.product"(%1080, %1081, %1082, %1083, %1084, %1085, %1086, %1087, %1088, %1089, %1090, %1091, %1092, %1093, %1094, %1095, %1096, %1097, %1098, %1099, %1100, %1101, %1102, %1103, %1104, %1105) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %1107 = "hi_spn.gaussian"(%arg0) {mean = -8.1327724456787109 : f64, stddev = 0.83897727727890015 : f64} : (f64) -> !hi_spn.probability + %1108 = "hi_spn.gaussian"(%arg1) {mean = -7.4625945091247559 : f64, stddev = 0.98968738317489624 : f64} : (f64) -> !hi_spn.probability + %1109 = "hi_spn.gaussian"(%arg2) {mean = -6.7246294021606445 : f64, stddev = 0.96315348148345947 : f64} : (f64) -> !hi_spn.probability + %1110 = "hi_spn.gaussian"(%arg3) {mean = -6.5153493881225586 : f64, stddev = 0.75170618295669556 : f64} : (f64) -> !hi_spn.probability + %1111 = "hi_spn.gaussian"(%arg4) {mean = -6.0564794540405273 : f64, stddev = 0.66891670227050781 : f64} : (f64) -> !hi_spn.probability + %1112 = "hi_spn.gaussian"(%arg5) {mean = -5.8513154983520508 : f64, stddev = 0.66089785099029541 : f64} : (f64) -> !hi_spn.probability + %1113 = "hi_spn.gaussian"(%arg6) {mean = -5.9307498931884766 : f64, stddev = 0.71004027128219604 : f64} : (f64) -> !hi_spn.probability + %1114 = "hi_spn.gaussian"(%arg7) {mean = -6.2762465476989746 : f64, stddev = 0.68832004070281982 : f64} : (f64) -> !hi_spn.probability + %1115 = "hi_spn.gaussian"(%arg8) {mean = -6.3094396591186523 : f64, stddev = 0.68908232450485229 : f64} : (f64) -> !hi_spn.probability + %1116 = "hi_spn.gaussian"(%arg9) {mean = -6.7853331565856934 : f64, stddev = 0.81109845638275146 : f64} : (f64) -> !hi_spn.probability + %1117 = "hi_spn.gaussian"(%arg10) {mean = -7.6216001510620117 : f64, stddev = 0.60689467191696167 : f64} : (f64) -> !hi_spn.probability + %1118 = "hi_spn.gaussian"(%arg11) {mean = -7.5946106910705566 : f64, stddev = 0.65252119302749634 : f64} : (f64) -> !hi_spn.probability + %1119 = "hi_spn.gaussian"(%arg12) {mean = -6.7727460861206055 : f64, stddev = 0.77765560150146484 : f64} : (f64) -> !hi_spn.probability + %1120 = "hi_spn.gaussian"(%arg13) {mean = -5.3121771812438965 : f64, stddev = 1.0217887163162231 : f64} : (f64) -> !hi_spn.probability + %1121 = "hi_spn.gaussian"(%arg14) {mean = -4.3809232711791992 : f64, stddev = 1.0800201892852783 : f64} : (f64) -> !hi_spn.probability + %1122 = "hi_spn.gaussian"(%arg15) {mean = -3.306704044342041 : f64, stddev = 1.2345993518829346 : f64} : (f64) -> !hi_spn.probability + %1123 = "hi_spn.gaussian"(%arg16) {mean = -2.4655814170837402 : f64, stddev = 0.99901485443115234 : f64} : (f64) -> !hi_spn.probability + %1124 = "hi_spn.gaussian"(%arg17) {mean = -2.4478905200958252 : f64, stddev = 1.0119580030441284 : f64} : (f64) -> !hi_spn.probability + %1125 = "hi_spn.gaussian"(%arg18) {mean = -3.2528014183044434 : f64, stddev = 0.91763502359390259 : f64} : (f64) -> !hi_spn.probability + %1126 = "hi_spn.gaussian"(%arg19) {mean = -3.3298881053924561 : f64, stddev = 1.1470904350280762 : f64} : (f64) -> !hi_spn.probability + %1127 = "hi_spn.gaussian"(%arg20) {mean = -4.0067887306213379 : f64, stddev = 1.0368081331253052 : f64} : (f64) -> !hi_spn.probability + %1128 = "hi_spn.gaussian"(%arg21) {mean = -4.3828825950622559 : f64, stddev = 0.82735782861709595 : f64} : (f64) -> !hi_spn.probability + %1129 = "hi_spn.gaussian"(%arg22) {mean = -4.418696403503418 : f64, stddev = 0.8494982123374939 : f64} : (f64) -> !hi_spn.probability + %1130 = "hi_spn.gaussian"(%arg23) {mean = -4.5085997581481934 : f64, stddev = 0.87574118375778198 : f64} : (f64) -> !hi_spn.probability + %1131 = "hi_spn.gaussian"(%arg24) {mean = -4.8956670761108398 : f64, stddev = 0.85967069864273071 : f64} : (f64) -> !hi_spn.probability + %1132 = "hi_spn.gaussian"(%arg25) {mean = -5.6702165603637695 : f64, stddev = 0.86872106790542603 : f64} : (f64) -> !hi_spn.probability + %1133 = "hi_spn.product"(%1107, %1108, %1109, %1110, %1111, %1112, %1113, %1114, %1115, %1116, %1117, %1118, %1119, %1120, %1121, %1122, %1123, %1124, %1125, %1126, %1127, %1128, %1129, %1130, %1131, %1132) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %1134 = "hi_spn.gaussian"(%arg0) {mean = -8.1351470947265625 : f64, stddev = 1.2107608318328857 : f64} : (f64) -> !hi_spn.probability + %1135 = "hi_spn.gaussian"(%arg1) {mean = -7.6069998741149902 : f64, stddev = 0.98783421516418457 : f64} : (f64) -> !hi_spn.probability + %1136 = "hi_spn.gaussian"(%arg2) {mean = -7.2112936973571777 : f64, stddev = 1.4660775661468506 : f64} : (f64) -> !hi_spn.probability + %1137 = "hi_spn.gaussian"(%arg3) {mean = -7.4928908348083496 : f64, stddev = 1.232511043548584 : f64} : (f64) -> !hi_spn.probability + %1138 = "hi_spn.gaussian"(%arg4) {mean = -7.4281439781188965 : f64, stddev = 0.74590414762496948 : f64} : (f64) -> !hi_spn.probability + %1139 = "hi_spn.gaussian"(%arg5) {mean = -6.8674616813659668 : f64, stddev = 0.7836916446685791 : f64} : (f64) -> !hi_spn.probability + %1140 = "hi_spn.gaussian"(%arg6) {mean = -6.6300444602966309 : f64, stddev = 0.82512605190277099 : f64} : (f64) -> !hi_spn.probability + %1141 = "hi_spn.gaussian"(%arg7) {mean = -6.5386452674865723 : f64, stddev = 0.83634114265441895 : f64} : (f64) -> !hi_spn.probability + %1142 = "hi_spn.gaussian"(%arg8) {mean = -6.5623278617858887 : f64, stddev = 0.76402616500854492 : f64} : (f64) -> !hi_spn.probability + %1143 = "hi_spn.gaussian"(%arg9) {mean = -6.8173694610595703 : f64, stddev = 0.71194523572921753 : f64} : (f64) -> !hi_spn.probability + %1144 = "hi_spn.gaussian"(%arg10) {mean = -7.2219152450561523 : f64, stddev = 0.71198701858520508 : f64} : (f64) -> !hi_spn.probability + %1145 = "hi_spn.gaussian"(%arg11) {mean = -7.6279535293579102 : f64, stddev = 0.8509259819984436 : f64} : (f64) -> !hi_spn.probability + %1146 = "hi_spn.gaussian"(%arg12) {mean = -7.4050402641296387 : f64, stddev = 0.70776689052581787 : f64} : (f64) -> !hi_spn.probability + %1147 = "hi_spn.gaussian"(%arg13) {mean = -6.9807381629943848 : f64, stddev = 0.59256702661514282 : f64} : (f64) -> !hi_spn.probability + %1148 = "hi_spn.gaussian"(%arg14) {mean = -7.2108397483825684 : f64, stddev = 0.61555415391921997 : f64} : (f64) -> !hi_spn.probability + %1149 = "hi_spn.gaussian"(%arg15) {mean = -7.1595516204833984 : f64, stddev = 0.52552497386932373 : f64} : (f64) -> !hi_spn.probability + %1150 = "hi_spn.gaussian"(%arg16) {mean = -7.2618060111999512 : f64, stddev = 0.60964733362197876 : f64} : (f64) -> !hi_spn.probability + %1151 = "hi_spn.gaussian"(%arg17) {mean = -6.8453741073608398 : f64, stddev = 0.69571346044540405 : f64} : (f64) -> !hi_spn.probability + %1152 = "hi_spn.gaussian"(%arg18) {mean = -6.7002315521240234 : f64, stddev = 0.8325534462928772 : f64} : (f64) -> !hi_spn.probability + %1153 = "hi_spn.gaussian"(%arg19) {mean = -6.7711272239685059 : f64, stddev = 0.60424131155014038 : f64} : (f64) -> !hi_spn.probability + %1154 = "hi_spn.gaussian"(%arg20) {mean = -6.4360752105712891 : f64, stddev = 0.50771445035934448 : f64} : (f64) -> !hi_spn.probability + %1155 = "hi_spn.gaussian"(%arg21) {mean = -5.727851390838623 : f64, stddev = 0.63428497314453125 : f64} : (f64) -> !hi_spn.probability + %1156 = "hi_spn.gaussian"(%arg22) {mean = -4.7468819618225098 : f64, stddev = 0.95185911655426025 : f64} : (f64) -> !hi_spn.probability + %1157 = "hi_spn.gaussian"(%arg23) {mean = -3.7135066986083984 : f64, stddev = 1.1514450311660767 : f64} : (f64) -> !hi_spn.probability + %1158 = "hi_spn.gaussian"(%arg24) {mean = -3.3609490394592285 : f64, stddev = 1.1183294057846069 : f64} : (f64) -> !hi_spn.probability + %1159 = "hi_spn.gaussian"(%arg25) {mean = -3.1431469917297363 : f64, stddev = 1.1440502405166626 : f64} : (f64) -> !hi_spn.probability + %1160 = "hi_spn.product"(%1134, %1135, %1136, %1137, %1138, %1139, %1140, %1141, %1142, %1143, %1144, %1145, %1146, %1147, %1148, %1149, %1150, %1151, %1152, %1153, %1154, %1155, %1156, %1157, %1158, %1159) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %1161 = "hi_spn.gaussian"(%arg0) {mean = -8.5960350036621094 : f64, stddev = 0.94565409421920776 : f64} : (f64) -> !hi_spn.probability + %1162 = "hi_spn.gaussian"(%arg1) {mean = -8.3736753463745117 : f64, stddev = 0.84241259098052978 : f64} : (f64) -> !hi_spn.probability + %1163 = "hi_spn.gaussian"(%arg2) {mean = -7.7529253959655762 : f64, stddev = 1.0274713039398193 : f64} : (f64) -> !hi_spn.probability + %1164 = "hi_spn.gaussian"(%arg3) {mean = -7.4459090232849121 : f64, stddev = 0.96476179361343384 : f64} : (f64) -> !hi_spn.probability + %1165 = "hi_spn.gaussian"(%arg4) {mean = -6.955103874206543 : f64, stddev = 0.82510042190551758 : f64} : (f64) -> !hi_spn.probability + %1166 = "hi_spn.gaussian"(%arg5) {mean = -6.4802327156066895 : f64, stddev = 0.6795576810836792 : f64} : (f64) -> !hi_spn.probability + %1167 = "hi_spn.gaussian"(%arg6) {mean = -6.3315639495849609 : f64, stddev = 0.83499896526336669 : f64} : (f64) -> !hi_spn.probability + %1168 = "hi_spn.gaussian"(%arg7) {mean = -6.4603075981140137 : f64, stddev = 0.8735663890838623 : f64} : (f64) -> !hi_spn.probability + %1169 = "hi_spn.gaussian"(%arg8) {mean = -6.3615522384643555 : f64, stddev = 0.67139559984207153 : f64} : (f64) -> !hi_spn.probability + %1170 = "hi_spn.gaussian"(%arg9) {mean = -6.6965460777282715 : f64, stddev = 0.55126649141311646 : f64} : (f64) -> !hi_spn.probability + %1171 = "hi_spn.gaussian"(%arg10) {mean = -6.9876432418823242 : f64, stddev = 0.64887279272079468 : f64} : (f64) -> !hi_spn.probability + %1172 = "hi_spn.gaussian"(%arg11) {mean = -7.3160581588745117 : f64, stddev = 0.73588627576828003 : f64} : (f64) -> !hi_spn.probability + %1173 = "hi_spn.gaussian"(%arg12) {mean = -7.5721745491027832 : f64, stddev = 0.69569993019104004 : f64} : (f64) -> !hi_spn.probability + %1174 = "hi_spn.gaussian"(%arg13) {mean = -7.1877260208129883 : f64, stddev = 0.51203268766403198 : f64} : (f64) -> !hi_spn.probability + %1175 = "hi_spn.gaussian"(%arg14) {mean = -7.0640602111816406 : f64, stddev = 0.79380953311920166 : f64} : (f64) -> !hi_spn.probability + %1176 = "hi_spn.gaussian"(%arg15) {mean = -7.4718151092529296 : f64, stddev = 0.74522888660430908 : f64} : (f64) -> !hi_spn.probability + %1177 = "hi_spn.gaussian"(%arg16) {mean = -7.2574644088745117 : f64, stddev = 0.59343963861465454 : f64} : (f64) -> !hi_spn.probability + %1178 = "hi_spn.gaussian"(%arg17) {mean = -6.4826140403747559 : f64, stddev = 0.69413727521896362 : f64} : (f64) -> !hi_spn.probability + %1179 = "hi_spn.gaussian"(%arg18) {mean = -6.7211971282958984 : f64, stddev = 0.66545355319976807 : f64} : (f64) -> !hi_spn.probability + %1180 = "hi_spn.gaussian"(%arg19) {mean = -5.8741817474365234 : f64, stddev = 1.1997512578964233 : f64} : (f64) -> !hi_spn.probability + %1181 = "hi_spn.gaussian"(%arg20) {mean = -4.1347441673278809 : f64, stddev = 1.0272082090377808 : f64} : (f64) -> !hi_spn.probability + %1182 = "hi_spn.gaussian"(%arg21) {mean = -3.2810747623443604 : f64, stddev = 0.60014808177947998 : f64} : (f64) -> !hi_spn.probability + %1183 = "hi_spn.gaussian"(%arg22) {mean = -3.0261728763580322 : f64, stddev = 0.96363931894302368 : f64} : (f64) -> !hi_spn.probability + %1184 = "hi_spn.gaussian"(%arg23) {mean = -3.0812456607818604 : f64, stddev = 1.0443340539932251 : f64} : (f64) -> !hi_spn.probability + %1185 = "hi_spn.gaussian"(%arg24) {mean = -3.7295598983764648 : f64, stddev = 1.0644252300262451 : f64} : (f64) -> !hi_spn.probability + %1186 = "hi_spn.gaussian"(%arg25) {mean = -3.8976898193359375 : f64, stddev = 1.1002380847930908 : f64} : (f64) -> !hi_spn.probability + %1187 = "hi_spn.product"(%1161, %1162, %1163, %1164, %1165, %1166, %1167, %1168, %1169, %1170, %1171, %1172, %1173, %1174, %1175, %1176, %1177, %1178, %1179, %1180, %1181, %1182, %1183, %1184, %1185, %1186) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %1188 = "hi_spn.gaussian"(%arg0) {mean = -10.228626251220703 : f64, stddev = 0.48262864351272583 : f64} : (f64) -> !hi_spn.probability + %1189 = "hi_spn.gaussian"(%arg1) {mean = -9.4260520935058593 : f64, stddev = 1.0795828104019165 : f64} : (f64) -> !hi_spn.probability + %1190 = "hi_spn.gaussian"(%arg2) {mean = -9.3699159622192383 : f64, stddev = 1.5720652341842651 : f64} : (f64) -> !hi_spn.probability + %1191 = "hi_spn.gaussian"(%arg3) {mean = -9.8879709243774414 : f64, stddev = 0.65752583742141724 : f64} : (f64) -> !hi_spn.probability + %1192 = "hi_spn.gaussian"(%arg4) {mean = -8.6183042526245117 : f64, stddev = 0.25848942995071411 : f64} : (f64) -> !hi_spn.probability + %1193 = "hi_spn.gaussian"(%arg5) {mean = -9.1497011184692383 : f64, stddev = 0.94090962409973145 : f64} : (f64) -> !hi_spn.probability + %1194 = "hi_spn.gaussian"(%arg6) {mean = -9.6549367904663086 : f64, stddev = 0.53325551748275757 : f64} : (f64) -> !hi_spn.probability + %1195 = "hi_spn.gaussian"(%arg7) {mean = -10.142255783081055 : f64, stddev = 0.62614536285400391 : f64} : (f64) -> !hi_spn.probability + %1196 = "hi_spn.gaussian"(%arg8) {mean = -9.497675895690918 : f64, stddev = 0.28528979420661926 : f64} : (f64) -> !hi_spn.probability + %1197 = "hi_spn.gaussian"(%arg9) {mean = -9.802271842956543 : f64, stddev = 0.75829267501831055 : f64} : (f64) -> !hi_spn.probability + %1198 = "hi_spn.gaussian"(%arg10) {mean = -9.773712158203125 : f64, stddev = 0.59009057283401489 : f64} : (f64) -> !hi_spn.probability + %1199 = "hi_spn.gaussian"(%arg11) {mean = -9.9491195678710938 : f64, stddev = 0.49780485033988953 : f64} : (f64) -> !hi_spn.probability + %1200 = "hi_spn.gaussian"(%arg12) {mean = -9.0933647155761719 : f64, stddev = 0.82718503475189208 : f64} : (f64) -> !hi_spn.probability + %1201 = "hi_spn.gaussian"(%arg13) {mean = -8.4050445556640625 : f64, stddev = 1.2810466289520264 : f64} : (f64) -> !hi_spn.probability + %1202 = "hi_spn.gaussian"(%arg14) {mean = -7.3729300498962402 : f64, stddev = 1.3321777582168579 : f64} : (f64) -> !hi_spn.probability + %1203 = "hi_spn.gaussian"(%arg15) {mean = -6.4275321960449219 : f64, stddev = 0.84346389770507813 : f64} : (f64) -> !hi_spn.probability + %1204 = "hi_spn.gaussian"(%arg16) {mean = -4.1672978401184082 : f64, stddev = 1.0621676445007324 : f64} : (f64) -> !hi_spn.probability + %1205 = "hi_spn.gaussian"(%arg17) {mean = -3.3934333324432373 : f64, stddev = 1.0357874631881714 : f64} : (f64) -> !hi_spn.probability + %1206 = "hi_spn.gaussian"(%arg18) {mean = -3.5318877696990967 : f64, stddev = 0.91614431142807007 : f64} : (f64) -> !hi_spn.probability + %1207 = "hi_spn.gaussian"(%arg19) {mean = -3.6603190898895264 : f64, stddev = 1.0337003469467163 : f64} : (f64) -> !hi_spn.probability + %1208 = "hi_spn.gaussian"(%arg20) {mean = -4.2903323173522949 : f64, stddev = 0.91162919998168945 : f64} : (f64) -> !hi_spn.probability + %1209 = "hi_spn.gaussian"(%arg21) {mean = -4.5510520935058594 : f64, stddev = 0.16234958171844482 : f64} : (f64) -> !hi_spn.probability + %1210 = "hi_spn.gaussian"(%arg22) {mean = -4.4354791641235352 : f64, stddev = 0.1556677520275116 : f64} : (f64) -> !hi_spn.probability + %1211 = "hi_spn.gaussian"(%arg23) {mean = -4.5420160293579102 : f64, stddev = 0.10435239225625992 : f64} : (f64) -> !hi_spn.probability + %1212 = "hi_spn.gaussian"(%arg24) {mean = -4.7109465599060059 : f64, stddev = 0.83287334442138671 : f64} : (f64) -> !hi_spn.probability + %1213 = "hi_spn.gaussian"(%arg25) {mean = -4.9718303680419922 : f64, stddev = 0.85044741630554199 : f64} : (f64) -> !hi_spn.probability + %1214 = "hi_spn.product"(%1188, %1189, %1190, %1191, %1192, %1193, %1194, %1195, %1196, %1197, %1198, %1199, %1200, %1201, %1202, %1203, %1204, %1205, %1206, %1207, %1208, %1209, %1210, %1211, %1212, %1213) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %1215 = "hi_spn.gaussian"(%arg0) {mean = -9.3904399871826171 : f64, stddev = 1.152754545211792 : f64} : (f64) -> !hi_spn.probability + %1216 = "hi_spn.gaussian"(%arg1) {mean = -9.7540502548217773 : f64, stddev = 0.86461043357849121 : f64} : (f64) -> !hi_spn.probability + %1217 = "hi_spn.gaussian"(%arg2) {mean = -9.4713287353515625 : f64, stddev = 0.84429699182510376 : f64} : (f64) -> !hi_spn.probability + %1218 = "hi_spn.gaussian"(%arg3) {mean = -8.9653387069702148 : f64, stddev = 0.69941234588623047 : f64} : (f64) -> !hi_spn.probability + %1219 = "hi_spn.gaussian"(%arg4) {mean = -8.1735820770263671 : f64, stddev = 0.6002272367477417 : f64} : (f64) -> !hi_spn.probability + %1220 = "hi_spn.gaussian"(%arg5) {mean = -7.4526243209838867 : f64, stddev = 0.47542226314544678 : f64} : (f64) -> !hi_spn.probability + %1221 = "hi_spn.gaussian"(%arg6) {mean = -7.3608407974243164 : f64, stddev = 0.47880584001541138 : f64} : (f64) -> !hi_spn.probability + %1222 = "hi_spn.gaussian"(%arg7) {mean = -7.2663478851318359 : f64, stddev = 0.60803425312042236 : f64} : (f64) -> !hi_spn.probability + %1223 = "hi_spn.gaussian"(%arg8) {mean = -7.2425718307495117 : f64, stddev = 0.46909874677658081 : f64} : (f64) -> !hi_spn.probability + %1224 = "hi_spn.gaussian"(%arg9) {mean = -7.529088020324707 : f64, stddev = 0.62576359510421753 : f64} : (f64) -> !hi_spn.probability + %1225 = "hi_spn.gaussian"(%arg10) {mean = -7.6450953483581543 : f64, stddev = 0.6576501727104187 : f64} : (f64) -> !hi_spn.probability + %1226 = "hi_spn.gaussian"(%arg11) {mean = -7.895993709564209 : f64, stddev = 0.64979934692382813 : f64} : (f64) -> !hi_spn.probability + %1227 = "hi_spn.gaussian"(%arg12) {mean = -7.967918872833252 : f64, stddev = 0.54690402746200562 : f64} : (f64) -> !hi_spn.probability + %1228 = "hi_spn.gaussian"(%arg13) {mean = -7.715120792388916 : f64, stddev = 0.48729050159454346 : f64} : (f64) -> !hi_spn.probability + %1229 = "hi_spn.gaussian"(%arg14) {mean = -7.7423973083496094 : f64, stddev = 0.55065226554870605 : f64} : (f64) -> !hi_spn.probability + %1230 = "hi_spn.gaussian"(%arg15) {mean = -7.6820602416992188 : f64, stddev = 0.51393657922744751 : f64} : (f64) -> !hi_spn.probability + %1231 = "hi_spn.gaussian"(%arg16) {mean = -7.6381373405456543 : f64, stddev = 0.57497531175613403 : f64} : (f64) -> !hi_spn.probability + %1232 = "hi_spn.gaussian"(%arg17) {mean = -7.205955982208252 : f64, stddev = 0.5891653299331665 : f64} : (f64) -> !hi_spn.probability + %1233 = "hi_spn.gaussian"(%arg18) {mean = -7.0726304054260254 : f64, stddev = 0.51587694883346558 : f64} : (f64) -> !hi_spn.probability + %1234 = "hi_spn.gaussian"(%arg19) {mean = -7.014378547668457 : f64, stddev = 0.63250213861465454 : f64} : (f64) -> !hi_spn.probability + %1235 = "hi_spn.gaussian"(%arg20) {mean = -6.578834056854248 : f64, stddev = 0.71568793058395386 : f64} : (f64) -> !hi_spn.probability + %1236 = "hi_spn.gaussian"(%arg21) {mean = -5.7346096038818359 : f64, stddev = 1.1677343845367432 : f64} : (f64) -> !hi_spn.probability + %1237 = "hi_spn.gaussian"(%arg22) {mean = -4.4110879898071289 : f64, stddev = 1.3120131492614746 : f64} : (f64) -> !hi_spn.probability + %1238 = "hi_spn.gaussian"(%arg23) {mean = -3.6912524700164795 : f64, stddev = 1.3358936309814453 : f64} : (f64) -> !hi_spn.probability + %1239 = "hi_spn.gaussian"(%arg24) {mean = -3.2795472145080566 : f64, stddev = 1.1750563383102417 : f64} : (f64) -> !hi_spn.probability + %1240 = "hi_spn.gaussian"(%arg25) {mean = -2.9861128330230713 : f64, stddev = 1.1393617391586304 : f64} : (f64) -> !hi_spn.probability + %1241 = "hi_spn.product"(%1215, %1216, %1217, %1218, %1219, %1220, %1221, %1222, %1223, %1224, %1225, %1226, %1227, %1228, %1229, %1230, %1231, %1232, %1233, %1234, %1235, %1236, %1237, %1238, %1239, %1240) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %1242 = "hi_spn.gaussian"(%arg0) {mean = -10.538475036621094 : f64, stddev = 0.87019312381744384 : f64} : (f64) -> !hi_spn.probability + %1243 = "hi_spn.gaussian"(%arg1) {mean = -10.333244323730469 : f64, stddev = 0.81989151239395142 : f64} : (f64) -> !hi_spn.probability + %1244 = "hi_spn.gaussian"(%arg2) {mean = -9.7449064254760742 : f64, stddev = 0.93104016780853271 : f64} : (f64) -> !hi_spn.probability + %1245 = "hi_spn.gaussian"(%arg3) {mean = -9.3484325408935546 : f64, stddev = 0.94051861763000488 : f64} : (f64) -> !hi_spn.probability + %1246 = "hi_spn.gaussian"(%arg4) {mean = -9.0750856399536133 : f64, stddev = 0.89459359645843505 : f64} : (f64) -> !hi_spn.probability + %1247 = "hi_spn.gaussian"(%arg5) {mean = -8.4267511367797852 : f64, stddev = 0.87579178810119629 : f64} : (f64) -> !hi_spn.probability + %1248 = "hi_spn.gaussian"(%arg6) {mean = -8.4656181335449219 : f64, stddev = 0.87951827049255371 : f64} : (f64) -> !hi_spn.probability + %1249 = "hi_spn.gaussian"(%arg7) {mean = -8.0339326858520508 : f64, stddev = 0.70159304141998291 : f64} : (f64) -> !hi_spn.probability + %1250 = "hi_spn.gaussian"(%arg8) {mean = -8.1093721389770508 : f64, stddev = 0.8358035683631897 : f64} : (f64) -> !hi_spn.probability + %1251 = "hi_spn.gaussian"(%arg9) {mean = -8.256845474243164 : f64, stddev = 0.71503901481628418 : f64} : (f64) -> !hi_spn.probability + %1252 = "hi_spn.gaussian"(%arg10) {mean = -8.6761112213134765 : f64, stddev = 0.64327800273895264 : f64} : (f64) -> !hi_spn.probability + %1253 = "hi_spn.gaussian"(%arg11) {mean = -8.7061166763305664 : f64, stddev = 0.6480562686920166 : f64} : (f64) -> !hi_spn.probability + %1254 = "hi_spn.gaussian"(%arg12) {mean = -8.5889863967895508 : f64, stddev = 0.82020813226699829 : f64} : (f64) -> !hi_spn.probability + %1255 = "hi_spn.gaussian"(%arg13) {mean = -8.5061864852905273 : f64, stddev = 0.71188557147979736 : f64} : (f64) -> !hi_spn.probability + %1256 = "hi_spn.gaussian"(%arg14) {mean = -8.5804824829101563 : f64, stddev = 0.67343908548355103 : f64} : (f64) -> !hi_spn.probability + %1257 = "hi_spn.gaussian"(%arg15) {mean = -8.7928228378295898 : f64, stddev = 0.62495940923690796 : f64} : (f64) -> !hi_spn.probability + %1258 = "hi_spn.gaussian"(%arg16) {mean = -8.7564296722412109 : f64, stddev = 0.53966408967971802 : f64} : (f64) -> !hi_spn.probability + %1259 = "hi_spn.gaussian"(%arg17) {mean = -8.2335834503173828 : f64, stddev = 0.61673712730407715 : f64} : (f64) -> !hi_spn.probability + %1260 = "hi_spn.gaussian"(%arg18) {mean = -8.4317474365234375 : f64, stddev = 0.42848673462867737 : f64} : (f64) -> !hi_spn.probability + %1261 = "hi_spn.gaussian"(%arg19) {mean = -7.9238200187683105 : f64, stddev = 0.56645852327346802 : f64} : (f64) -> !hi_spn.probability + %1262 = "hi_spn.gaussian"(%arg20) {mean = -6.3985996246337891 : f64, stddev = 0.95271551609039306 : f64} : (f64) -> !hi_spn.probability + %1263 = "hi_spn.gaussian"(%arg21) {mean = -4.9638056755065918 : f64, stddev = 0.93421643972396851 : f64} : (f64) -> !hi_spn.probability + %1264 = "hi_spn.gaussian"(%arg22) {mean = -3.7508585453033447 : f64, stddev = 1.046027660369873 : f64} : (f64) -> !hi_spn.probability + %1265 = "hi_spn.gaussian"(%arg23) {mean = -3.0062263011932373 : f64, stddev = 0.72930556535720825 : f64} : (f64) -> !hi_spn.probability + %1266 = "hi_spn.gaussian"(%arg24) {mean = -3.3981304168701172 : f64, stddev = 0.64163941144943237 : f64} : (f64) -> !hi_spn.probability + %1267 = "hi_spn.gaussian"(%arg25) {mean = -3.5509719848632813 : f64, stddev = 0.67582345008850098 : f64} : (f64) -> !hi_spn.probability + %1268 = "hi_spn.product"(%1242, %1243, %1244, %1245, %1246, %1247, %1248, %1249, %1250, %1251, %1252, %1253, %1254, %1255, %1256, %1257, %1258, %1259, %1260, %1261, %1262, %1263, %1264, %1265, %1266, %1267) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %1269 = "hi_spn.gaussian"(%arg0) {mean = -8.2763452529907227 : f64, stddev = 1.5482563972473145 : f64} : (f64) -> !hi_spn.probability + %1270 = "hi_spn.gaussian"(%arg1) {mean = -7.9458565711975098 : f64, stddev = 1.4370685815811157 : f64} : (f64) -> !hi_spn.probability + %1271 = "hi_spn.gaussian"(%arg2) {mean = -7.5023512840270996 : f64, stddev = 1.1574099063873291 : f64} : (f64) -> !hi_spn.probability + %1272 = "hi_spn.gaussian"(%arg3) {mean = -7.0802135467529297 : f64, stddev = 1.1082210540771484 : f64} : (f64) -> !hi_spn.probability + %1273 = "hi_spn.gaussian"(%arg4) {mean = -6.6080574989318848 : f64, stddev = 0.93516576290130615 : f64} : (f64) -> !hi_spn.probability + %1274 = "hi_spn.gaussian"(%arg5) {mean = -6.0482759475708008 : f64, stddev = 1.0284839868545532 : f64} : (f64) -> !hi_spn.probability + %1275 = "hi_spn.gaussian"(%arg6) {mean = -6.048738956451416 : f64, stddev = 1.0235182046890259 : f64} : (f64) -> !hi_spn.probability + %1276 = "hi_spn.gaussian"(%arg7) {mean = -6.2141504287719727 : f64, stddev = 0.95114743709564208 : f64} : (f64) -> !hi_spn.probability + %1277 = "hi_spn.gaussian"(%arg8) {mean = -6.3661699295043945 : f64, stddev = 0.98208391666412353 : f64} : (f64) -> !hi_spn.probability + %1278 = "hi_spn.gaussian"(%arg9) {mean = -6.649263858795166 : f64, stddev = 0.96567648649215698 : f64} : (f64) -> !hi_spn.probability + %1279 = "hi_spn.gaussian"(%arg10) {mean = -6.6243577003479004 : f64, stddev = 1.1962635517120361 : f64} : (f64) -> !hi_spn.probability + %1280 = "hi_spn.gaussian"(%arg11) {mean = -6.501615047454834 : f64, stddev = 1.1897907257080078 : f64} : (f64) -> !hi_spn.probability + %1281 = "hi_spn.gaussian"(%arg12) {mean = -6.5107059478759766 : f64, stddev = 1.1074386835098267 : f64} : (f64) -> !hi_spn.probability + %1282 = "hi_spn.gaussian"(%arg13) {mean = -6.5365705490112305 : f64, stddev = 0.88953518867492676 : f64} : (f64) -> !hi_spn.probability + %1283 = "hi_spn.gaussian"(%arg14) {mean = -6.7228875160217285 : f64, stddev = 0.86779379844665527 : f64} : (f64) -> !hi_spn.probability + %1284 = "hi_spn.gaussian"(%arg15) {mean = -7.1269693374633789 : f64, stddev = 0.8477974534034729 : f64} : (f64) -> !hi_spn.probability + %1285 = "hi_spn.gaussian"(%arg16) {mean = -7.1350765228271484 : f64, stddev = 0.86106348037719727 : f64} : (f64) -> !hi_spn.probability + %1286 = "hi_spn.gaussian"(%arg17) {mean = -7.1070237159729004 : f64, stddev = 0.86179369688034058 : f64} : (f64) -> !hi_spn.probability + %1287 = "hi_spn.gaussian"(%arg18) {mean = -7.49188232421875 : f64, stddev = 0.80724263191223145 : f64} : (f64) -> !hi_spn.probability + %1288 = "hi_spn.gaussian"(%arg19) {mean = -7.6040186882019043 : f64, stddev = 0.8483319878578186 : f64} : (f64) -> !hi_spn.probability + %1289 = "hi_spn.gaussian"(%arg20) {mean = -7.6384525299072265 : f64, stddev = 0.84793817996978759 : f64} : (f64) -> !hi_spn.probability + %1290 = "hi_spn.gaussian"(%arg21) {mean = -7.7130618095397949 : f64, stddev = 0.98142987489700317 : f64} : (f64) -> !hi_spn.probability + %1291 = "hi_spn.gaussian"(%arg22) {mean = -7.9229326248168945 : f64, stddev = 0.90395647287368774 : f64} : (f64) -> !hi_spn.probability + %1292 = "hi_spn.gaussian"(%arg23) {mean = -8.0784845352172852 : f64, stddev = 0.76727509498596191 : f64} : (f64) -> !hi_spn.probability + %1293 = "hi_spn.gaussian"(%arg24) {mean = -8.1217403411865234 : f64, stddev = 0.88810628652572632 : f64} : (f64) -> !hi_spn.probability + %1294 = "hi_spn.gaussian"(%arg25) {mean = -8.3681907653808594 : f64, stddev = 1.0997027158737183 : f64} : (f64) -> !hi_spn.probability + %1295 = "hi_spn.product"(%1269, %1270, %1271, %1272, %1273, %1274, %1275, %1276, %1277, %1278, %1279, %1280, %1281, %1282, %1283, %1284, %1285, %1286, %1287, %1288, %1289, %1290, %1291, %1292, %1293, %1294) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %1296 = "hi_spn.gaussian"(%arg0) {mean = -8.0788431167602539 : f64, stddev = 1.7629274129867554 : f64} : (f64) -> !hi_spn.probability + %1297 = "hi_spn.gaussian"(%arg1) {mean = -5.8274893760681152 : f64, stddev = 1.4190887212753296 : f64} : (f64) -> !hi_spn.probability + %1298 = "hi_spn.gaussian"(%arg2) {mean = -3.7184379100799561 : f64, stddev = 1.2627127170562744 : f64} : (f64) -> !hi_spn.probability + %1299 = "hi_spn.gaussian"(%arg3) {mean = -4.2851533889770508 : f64, stddev = 1.4183335304260254 : f64} : (f64) -> !hi_spn.probability + %1300 = "hi_spn.gaussian"(%arg4) {mean = -4.6801080703735352 : f64, stddev = 1.5078637599945068 : f64} : (f64) -> !hi_spn.probability + %1301 = "hi_spn.gaussian"(%arg5) {mean = -4.7582817077636719 : f64, stddev = 1.1500116586685181 : f64} : (f64) -> !hi_spn.probability + %1302 = "hi_spn.gaussian"(%arg6) {mean = -5.8414387702941895 : f64, stddev = 0.96987110376358032 : f64} : (f64) -> !hi_spn.probability + %1303 = "hi_spn.gaussian"(%arg7) {mean = -6.5751662254333496 : f64, stddev = 0.92585963010787964 : f64} : (f64) -> !hi_spn.probability + %1304 = "hi_spn.gaussian"(%arg8) {mean = -7.0944614410400391 : f64, stddev = 1.0390374660491943 : f64} : (f64) -> !hi_spn.probability + %1305 = "hi_spn.gaussian"(%arg9) {mean = -7.7705574035644531 : f64, stddev = 0.96487277746200562 : f64} : (f64) -> !hi_spn.probability + %1306 = "hi_spn.gaussian"(%arg10) {mean = -8.3968572616577148 : f64, stddev = 1.0851093530654907 : f64} : (f64) -> !hi_spn.probability + %1307 = "hi_spn.gaussian"(%arg11) {mean = -8.4713306427001953 : f64, stddev = 1.0234142541885376 : f64} : (f64) -> !hi_spn.probability + %1308 = "hi_spn.gaussian"(%arg12) {mean = -8.0066690444946289 : f64, stddev = 1.0682411193847656 : f64} : (f64) -> !hi_spn.probability + %1309 = "hi_spn.gaussian"(%arg13) {mean = -7.1591887474060059 : f64, stddev = 0.99134224653244019 : f64} : (f64) -> !hi_spn.probability + %1310 = "hi_spn.gaussian"(%arg14) {mean = -6.8796572685241699 : f64, stddev = 1.1367703676223755 : f64} : (f64) -> !hi_spn.probability + %1311 = "hi_spn.gaussian"(%arg15) {mean = -7.233879566192627 : f64, stddev = 1.3663802146911621 : f64} : (f64) -> !hi_spn.probability + %1312 = "hi_spn.gaussian"(%arg16) {mean = -7.0371413230895996 : f64, stddev = 1.5991319417953491 : f64} : (f64) -> !hi_spn.probability + %1313 = "hi_spn.gaussian"(%arg17) {mean = -6.5239334106445313 : f64, stddev = 1.3880279064178467 : f64} : (f64) -> !hi_spn.probability + %1314 = "hi_spn.gaussian"(%arg18) {mean = -7.1891932487487793 : f64, stddev = 1.2469980716705322 : f64} : (f64) -> !hi_spn.probability + %1315 = "hi_spn.gaussian"(%arg19) {mean = -7.5768561363220215 : f64, stddev = 1.4959133863449097 : f64} : (f64) -> !hi_spn.probability + %1316 = "hi_spn.gaussian"(%arg20) {mean = -7.3721022605895996 : f64, stddev = 1.1623878479003906 : f64} : (f64) -> !hi_spn.probability + %1317 = "hi_spn.gaussian"(%arg21) {mean = -7.3525428771972656 : f64, stddev = 0.96901643276214599 : f64} : (f64) -> !hi_spn.probability + %1318 = "hi_spn.gaussian"(%arg22) {mean = -7.4475760459899902 : f64, stddev = 0.76724720001220703 : f64} : (f64) -> !hi_spn.probability + %1319 = "hi_spn.gaussian"(%arg23) {mean = -7.658205509185791 : f64, stddev = 0.90215820074081421 : f64} : (f64) -> !hi_spn.probability + %1320 = "hi_spn.gaussian"(%arg24) {mean = -8.0744352340698242 : f64, stddev = 1.1901699304580688 : f64} : (f64) -> !hi_spn.probability + %1321 = "hi_spn.gaussian"(%arg25) {mean = -8.3895692825317383 : f64, stddev = 1.4592515230178833 : f64} : (f64) -> !hi_spn.probability + %1322 = "hi_spn.product"(%1296, %1297, %1298, %1299, %1300, %1301, %1302, %1303, %1304, %1305, %1306, %1307, %1308, %1309, %1310, %1311, %1312, %1313, %1314, %1315, %1316, %1317, %1318, %1319, %1320, %1321) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %1323 = "hi_spn.gaussian"(%arg0) {mean = -9.1151790618896484 : f64, stddev = 1.8383872509002686 : f64} : (f64) -> !hi_spn.probability + %1324 = "hi_spn.gaussian"(%arg1) {mean = -8.3986024856567383 : f64, stddev = 1.7844225168228149 : f64} : (f64) -> !hi_spn.probability + %1325 = "hi_spn.gaussian"(%arg2) {mean = -7.4234485626220703 : f64, stddev = 2.3560254573822021 : f64} : (f64) -> !hi_spn.probability + %1326 = "hi_spn.gaussian"(%arg3) {mean = -7.3714137077331543 : f64, stddev = 1.8511563539505005 : f64} : (f64) -> !hi_spn.probability + %1327 = "hi_spn.gaussian"(%arg4) {mean = -7.5643291473388671 : f64, stddev = 1.3674472570419312 : f64} : (f64) -> !hi_spn.probability + %1328 = "hi_spn.gaussian"(%arg5) {mean = -7.6122360229492188 : f64, stddev = 1.0652170181274414 : f64} : (f64) -> !hi_spn.probability + %1329 = "hi_spn.gaussian"(%arg6) {mean = -7.7723932266235352 : f64, stddev = 1.088209867477417 : f64} : (f64) -> !hi_spn.probability + %1330 = "hi_spn.gaussian"(%arg7) {mean = -7.9320664405822754 : f64, stddev = 1.0915054082870483 : f64} : (f64) -> !hi_spn.probability + %1331 = "hi_spn.gaussian"(%arg8) {mean = -7.9642972946166992 : f64, stddev = 0.83227568864822388 : f64} : (f64) -> !hi_spn.probability + %1332 = "hi_spn.gaussian"(%arg9) {mean = -8.3301496505737305 : f64, stddev = 1.0040323734283447 : f64} : (f64) -> !hi_spn.probability + %1333 = "hi_spn.gaussian"(%arg10) {mean = -8.7318401336669921 : f64, stddev = 1.0766065120697021 : f64} : (f64) -> !hi_spn.probability + %1334 = "hi_spn.gaussian"(%arg11) {mean = -8.9305620193481445 : f64, stddev = 0.96655642986297607 : f64} : (f64) -> !hi_spn.probability + %1335 = "hi_spn.gaussian"(%arg12) {mean = -8.7896757125854492 : f64, stddev = 1.019780158996582 : f64} : (f64) -> !hi_spn.probability + %1336 = "hi_spn.gaussian"(%arg13) {mean = -8.7733983993530273 : f64, stddev = 1.0307799577713013 : f64} : (f64) -> !hi_spn.probability + %1337 = "hi_spn.gaussian"(%arg14) {mean = -8.9007492065429688 : f64, stddev = 1.190665602684021 : f64} : (f64) -> !hi_spn.probability + %1338 = "hi_spn.gaussian"(%arg15) {mean = -9.0362682342529296 : f64, stddev = 1.1130634546279907 : f64} : (f64) -> !hi_spn.probability + %1339 = "hi_spn.gaussian"(%arg16) {mean = -8.6109113693237305 : f64, stddev = 1.1620199680328369 : f64} : (f64) -> !hi_spn.probability + %1340 = "hi_spn.gaussian"(%arg17) {mean = -8.5171031951904296 : f64, stddev = 1.1569207906723022 : f64} : (f64) -> !hi_spn.probability + %1341 = "hi_spn.gaussian"(%arg18) {mean = -8.8785953521728515 : f64, stddev = 1.1959621906280518 : f64} : (f64) -> !hi_spn.probability + %1342 = "hi_spn.gaussian"(%arg19) {mean = -8.6213388442993164 : f64, stddev = 1.1882407665252686 : f64} : (f64) -> !hi_spn.probability + %1343 = "hi_spn.gaussian"(%arg20) {mean = -8.6017799377441406 : f64, stddev = 1.1764601469039917 : f64} : (f64) -> !hi_spn.probability + %1344 = "hi_spn.gaussian"(%arg21) {mean = -8.8107213973999023 : f64, stddev = 0.91693395376205444 : f64} : (f64) -> !hi_spn.probability + %1345 = "hi_spn.gaussian"(%arg22) {mean = -9.01708984375 : f64, stddev = 0.94207257032394409 : f64} : (f64) -> !hi_spn.probability + %1346 = "hi_spn.gaussian"(%arg23) {mean = -9.3141088485717773 : f64, stddev = 0.97628599405288696 : f64} : (f64) -> !hi_spn.probability + %1347 = "hi_spn.gaussian"(%arg24) {mean = -9.2799386978149414 : f64, stddev = 1.0461905002593994 : f64} : (f64) -> !hi_spn.probability + %1348 = "hi_spn.gaussian"(%arg25) {mean = -9.3268461227416992 : f64, stddev = 1.1838418245315552 : f64} : (f64) -> !hi_spn.probability + %1349 = "hi_spn.product"(%1323, %1324, %1325, %1326, %1327, %1328, %1329, %1330, %1331, %1332, %1333, %1334, %1335, %1336, %1337, %1338, %1339, %1340, %1341, %1342, %1343, %1344, %1345, %1346, %1347, %1348) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %1350 = "hi_spn.gaussian"(%arg0) {mean = -10.315433502197266 : f64, stddev = 1.3179805278778076 : f64} : (f64) -> !hi_spn.probability + %1351 = "hi_spn.gaussian"(%arg1) {mean = -8.1604814529418945 : f64, stddev = 2.2724642753601074 : f64} : (f64) -> !hi_spn.probability + %1352 = "hi_spn.gaussian"(%arg2) {mean = -7.1900372505187988 : f64, stddev = 2.9398491382598877 : f64} : (f64) -> !hi_spn.probability + %1353 = "hi_spn.gaussian"(%arg3) {mean = -7.713707447052002 : f64, stddev = 1.8990739583969116 : f64} : (f64) -> !hi_spn.probability + %1354 = "hi_spn.gaussian"(%arg4) {mean = -7.651547908782959 : f64, stddev = 1.8478991985321045 : f64} : (f64) -> !hi_spn.probability + %1355 = "hi_spn.gaussian"(%arg5) {mean = -7.7119650840759277 : f64, stddev = 1.3377139568328857 : f64} : (f64) -> !hi_spn.probability + %1356 = "hi_spn.gaussian"(%arg6) {mean = -8.2047338485717773 : f64, stddev = 1.2213213443756104 : f64} : (f64) -> !hi_spn.probability + %1357 = "hi_spn.gaussian"(%arg7) {mean = -8.2517843246459961 : f64, stddev = 0.93810057640075684 : f64} : (f64) -> !hi_spn.probability + %1358 = "hi_spn.gaussian"(%arg8) {mean = -8.4303188323974609 : f64, stddev = 1.0303676128387451 : f64} : (f64) -> !hi_spn.probability + %1359 = "hi_spn.gaussian"(%arg9) {mean = -8.8180484771728515 : f64, stddev = 0.87196367979049683 : f64} : (f64) -> !hi_spn.probability + %1360 = "hi_spn.gaussian"(%arg10) {mean = -8.9827346801757813 : f64, stddev = 0.8947300910949707 : f64} : (f64) -> !hi_spn.probability + %1361 = "hi_spn.gaussian"(%arg11) {mean = -9.2726545333862305 : f64, stddev = 1.0328786373138428 : f64} : (f64) -> !hi_spn.probability + %1362 = "hi_spn.gaussian"(%arg12) {mean = -9.3345098495483398 : f64, stddev = 0.78920960426330566 : f64} : (f64) -> !hi_spn.probability + %1363 = "hi_spn.gaussian"(%arg13) {mean = -9.2127161026000977 : f64, stddev = 0.70764654874801636 : f64} : (f64) -> !hi_spn.probability + %1364 = "hi_spn.gaussian"(%arg14) {mean = -9.2117757797241211 : f64, stddev = 0.98102712631225586 : f64} : (f64) -> !hi_spn.probability + %1365 = "hi_spn.gaussian"(%arg15) {mean = -9.4184341430664063 : f64, stddev = 0.81852328777313232 : f64} : (f64) -> !hi_spn.probability + %1366 = "hi_spn.gaussian"(%arg16) {mean = -9.3840084075927734 : f64, stddev = 0.7593197226524353 : f64} : (f64) -> !hi_spn.probability + %1367 = "hi_spn.gaussian"(%arg17) {mean = -8.6995563507080078 : f64, stddev = 0.84033447504043579 : f64} : (f64) -> !hi_spn.probability + %1368 = "hi_spn.gaussian"(%arg18) {mean = -8.7265081405639648 : f64, stddev = 0.74287855625152588 : f64} : (f64) -> !hi_spn.probability + %1369 = "hi_spn.gaussian"(%arg19) {mean = -8.5667104721069336 : f64, stddev = 0.68698239326477051 : f64} : (f64) -> !hi_spn.probability + %1370 = "hi_spn.gaussian"(%arg20) {mean = -7.6596660614013671 : f64, stddev = 0.81181913614273071 : f64} : (f64) -> !hi_spn.probability + %1371 = "hi_spn.gaussian"(%arg21) {mean = -6.7493586540222168 : f64, stddev = 0.72671127319335938 : f64} : (f64) -> !hi_spn.probability + %1372 = "hi_spn.gaussian"(%arg22) {mean = -6.1790566444396973 : f64, stddev = 0.82474768161773681 : f64} : (f64) -> !hi_spn.probability + %1373 = "hi_spn.gaussian"(%arg23) {mean = -5.676084041595459 : f64, stddev = 1.0325264930725098 : f64} : (f64) -> !hi_spn.probability + %1374 = "hi_spn.gaussian"(%arg24) {mean = -5.5357837677001953 : f64, stddev = 1.1748358011245728 : f64} : (f64) -> !hi_spn.probability + %1375 = "hi_spn.gaussian"(%arg25) {mean = -5.2995257377624512 : f64, stddev = 1.2293106317520142 : f64} : (f64) -> !hi_spn.probability + %1376 = "hi_spn.product"(%1350, %1351, %1352, %1353, %1354, %1355, %1356, %1357, %1358, %1359, %1360, %1361, %1362, %1363, %1364, %1365, %1366, %1367, %1368, %1369, %1370, %1371, %1372, %1373, %1374, %1375) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + %1377 = "hi_spn.sum"(%26, %53, %80, %107, %134, %161, %188, %215, %242, %269, %296, %323, %350, %377, %404, %431, %458, %485, %512, %539, %566, %593, %620, %647, %674, %701, %728, %755, %782, %809, %836, %863, %890, %917, %944, %971, %998, %1025, %1052, %1079, %1106, %1133, %1160, %1187, %1214, %1241, %1268, %1295, %1322, %1349, %1376) {weights = [0.0019672131147541183, 0.023606557377049187, 0.0013114754098360656, 0.032131147540983604, 0.0039344262295081967, 0.03081967213114754, 0.0072131147540983606, 0.023606557377049187, 0.014426229508196721, 0.026885245901639349, 0.021639344262295079, 0.014426229508196721, 0.026885245901639244, 0.019016393442622952, 0.015737704918032783, 0.026885245901639345, 0.018360655737704918, 0.017049180327868847, 0.019672131147540978, 0.017049180327868851, 0.02885245901639345, 0.0078688524590163951, 0.017049180327868851, 0.019672131147540985, 0.02229508196721312, 0.032786885245901641, 0.023606557377049184, 0.018360655737704911, 0.012459016393442624, 0.03016393442622951, 0.013770491803278693, 0.032131147540983604, 0.026885245901639356, 0.020327868852459016, 0.018360655737704918, 0.019016393442622952, 0.013114754098360654, 0.032131147540983618, 0.020983606557377053, 0.026229508196721318, 0.0085245901639344236, 0.026229508196721315, 0.020327868852459019, 0.016393442622950821, 0.001967213114754101, 0.025573770491803281, 0.012459016393442624, 0.026885245901639338, 0.01573770491803279, 0.02950819672131148, 0.017704918032786888]} : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability + "hi_spn.root"(%1377) : (!hi_spn.probability) -> () + }) {numFeatures = 26 : ui32} : () -> () + }) {batchSize = 1 : ui32, errorModel = 1 : i32, inputType = f64, kernelName = "FADG0.p", maxError = 2.000000e-02 : f64, numFeatures = 26 : ui32} : () -> () +} From 158485d1989409f6ce4905a4e7d30a1089eec7a6 Mon Sep 17 00:00:00 2001 From: CSV Date: Mon, 5 Apr 2021 20:33:00 +0200 Subject: [PATCH 061/385] Fix: flawed consecutiveness detection. --- mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp index f8b36387..94dba880 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp @@ -29,7 +29,7 @@ bool mlir::spn::low::slp::areConsecutiveLoads(Operation* op1, Operation* op2) { return false; } - return loadOp1.sampleIndex() == loadOp2.sampleIndex(); + return loadOp1.sampleIndex() + 1 == loadOp2.sampleIndex(); } bool mlir::spn::low::slp::areConsecutiveLoads(std::vector const& loads) { From 478cdf0bc0cc03a6ed60e30fe199fd6c4ef497a4 Mon Sep 17 00:00:00 2001 From: CSV Date: Mon, 5 Apr 2021 20:33:51 +0200 Subject: [PATCH 062/385] Fix: flawed LoadOp detection. --- mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp index 98db6588..980c0fde 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp @@ -4,6 +4,7 @@ // #include "mlir/Dialect/StandardOps/IR/Ops.h" +#include "LoSPN/LoSPNOps.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPMode.h" using namespace mlir; @@ -12,7 +13,7 @@ using namespace mlir::spn::low::slp; Mode mlir::spn::low::slp::modeFromOperation(const Operation* operation) { if (dyn_cast(operation)) { return CONST; - } else if (dyn_cast(operation)) { + } else if (dyn_cast(operation)) { return LOAD; } return OPCODE; From f3711ed9de8e9e0dfdcbd5cfe1f21b68f59e605a Mon Sep 17 00:00:00 2001 From: CSV Date: Mon, 5 Apr 2021 20:34:36 +0200 Subject: [PATCH 063/385] Updated commutativity detection to LoSPN changes. --- .../LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp index 53cbd31a..f337fb40 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp @@ -35,11 +35,11 @@ namespace { } bool commutative(std::vector const& operations) { - return std::all_of(std::begin(operations), std::end(operations), [&](Operation* op) { - if (op->hasTrait()) { - return true; + return std::any_of(std::begin(operations), std::end(operations), [&](Operation* op) { + if (!op->hasTrait()) { + return false; } - return dyn_cast(op) || dyn_cast(op); + return true; }); } From 25f3a8b745234aa636a6da49207065c0305d86f7 Mon Sep 17 00:00:00 2001 From: CSV Date: Mon, 5 Apr 2021 20:34:44 +0200 Subject: [PATCH 064/385] Better graph printing. --- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp | 31 ++++++++++++++++++- 1 file changed, 30 insertions(+), 1 deletion(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp index 16598d01..f70babbc 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp @@ -4,6 +4,8 @@ // #include "LoSPNtoCPU/Vectorization/SLP/SLPNode.h" +#include "LoSPN/LoSPNOps.h" +#include "mlir/Dialect/StandardOps/IR/Ops.h" #include "llvm/Support/Debug.h" #include @@ -114,6 +116,17 @@ void SLPNode::dump() const { } } +// Helper functions in an anonymous namespace. +namespace { + void dumpBlockArgOrDefiningAddress(Value const& val) { + if (auto* definingOp = val.getDefiningOp()) { + llvm::dbgs() << definingOp; + } else { + llvm::dbgs() << "block arg #" << val.cast().getArgNumber(); + } + } +} + void SLPNode::dumpGraph() const { llvm::dbgs() << "digraph debug_graph {\n"; @@ -134,7 +147,23 @@ void SLPNode::dumpGraph() const { for (size_t lane = 0; lane < node->numLanes(); ++lane) { auto* operation = node->getOperation(lane, i); llvm::dbgs() << "\t\t\t"; - llvm::dbgs() << "" << operation->getName() << " (" << operation << ")"; + llvm::dbgs() << "" << operation->getName() << ""; + // --- Additional operation information --- + llvm::dbgs() << "
"; + llvm::dbgs() << "(" << operation << ")"; + if (auto constOp = dyn_cast(operation)) { + llvm::dbgs() << "
value: " << constOp.getValue(); + } else if (auto lowConstOp = dyn_cast(operation)) { + llvm::dbgs() << "
value: " << lowConstOp.value().convertToDouble(); + } else if (auto readOp = dyn_cast(operation)) { + llvm::dbgs() << "
mem: "; + dumpBlockArgOrDefiningAddress(readOp.batchMem()); + llvm::dbgs() << "
batch: "; + dumpBlockArgOrDefiningAddress(readOp.batchIndex()); + llvm::dbgs() << "
sample: " << readOp.sampleIndex(); + } + llvm::dbgs() << ""; + // --- ================================ --- llvm::dbgs() << ""; if (lane < node->numLanes() - 1) { llvm::dbgs() << ""; From b40a980562b89323f2b86d08d6b56188dff9074c Mon Sep 17 00:00:00 2001 From: CSV Date: Thu, 8 Apr 2021 16:17:49 +0200 Subject: [PATCH 065/385] SLPGraph > CFG progress. --- .../SLP/SLPFunctionTransformer.h | 55 ++++ .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.h | 3 +- .../Vectorization/SLP/SLPVectorizationPass.h | 16 +- mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt | 1 + .../SLP/SLPFunctionTransformer.cpp | 240 ++++++++++++++++++ .../Vectorization/SLP/SLPGraphBuilder.cpp | 30 ++- .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp | 26 ++ .../SLP/SLPVectorizationPass.cpp | 160 +----------- mlir/tools/spnc-opt/spnc-opt.cpp | 4 - 9 files changed, 349 insertions(+), 186 deletions(-) create mode 100644 mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.h create mode 100644 mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.h new file mode 100644 index 00000000..def6a5e4 --- /dev/null +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.h @@ -0,0 +1,55 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPFUNCTIONTRANSFORMER_H +#define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPFUNCTIONTRANSFORMER_H + +#include "mlir/IR/Operation.h" +#include "mlir/IR/Builders.h" +#include "LoSPNtoCPU/LoSPNtoCPUTypeConverter.h" +#include "LoSPNtoCPU/Vectorization/LoSPNVectorizationTypeConverter.h" +#include "SLPNode.h" +#include +#include +#include + +namespace mlir { + namespace spn { + namespace low { + namespace slp { + + class SLPFunctionTransformer { + + public: + + SLPFunctionTransformer(std::unique_ptr&& graph, MLIRContext* context); + + void transform(); + + private: + + Value transform(SLPNode* node, size_t vectorIndex); + Value applyCreation(SLPNode* node, size_t vectorIndex, Operation* vectorOp); + + std::unique_ptr root; + OpBuilder builder; + LoSPNVectorizationTypeConverter typeConverter; + + /// Stores where operations can find their operands in case their defining operations + /// were deleted during vectorization. + llvm::DenseMap>> operandExtractions; + llvm::DenseMap, Value> memRefLoads; + + llvm::DenseMap vectorsDone; + llvm::DenseMap nodeInputsDone; + std::set vectorizedOps; + + }; + } + } + } +} + +#endif //SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPFUNCTIONTRANSFORMER_H diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h index d29f8e81..992aa4b8 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h @@ -15,7 +15,8 @@ namespace mlir { bool areConsecutiveLoads(Operation* load1, Operation* load2); bool areConsecutiveLoads(std::vector const& loads); - + bool canBeGathered(std::vector const& loads); + bool areBroadcastable(std::vector const& ops); } } } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h index 4376b071..681e4487 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h @@ -6,6 +6,7 @@ #ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPVECTORIZATIONPASS_H #define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPVECTORIZATIONPASS_H +#include #include "mlir/IR/Operation.h" #include "mlir/Pass/Pass.h" #include "SLPGraphBuilder.h" @@ -15,10 +16,6 @@ namespace mlir { namespace low { namespace slp { - namespace { - typedef std::pair Extraction; - } - struct SLPVectorizationPass : public PassWrapper> { public: @@ -28,17 +25,6 @@ namespace mlir { private: - void transform(SLPNode* root); - Value transform(SLPNode* node, - size_t vectorIndex, - std::map& vectorsDone, - std::map& nodeInputsDone); - Value applyCreation(SLPNode* node, size_t vectorIndex, Operation* vectorOp); - - /// Stores where operations can find their operands after vectorization in case their defining operations - /// were deleted during vectorization. - std::map> extractions; - }; } } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt b/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt index c2dd9829..48648422 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt +++ b/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt @@ -11,6 +11,7 @@ add_mlir_conversion_library(MLIRLoSPNtoCPUTransforms Vectorization/SLP/SLPGraphBuilder.cpp Vectorization/SLP/SLPUtil.cpp Vectorization/SLP/SLPVectorizationPass.cpp + Vectorization/SLP/SLPFunctionTransformer.cpp LoSPNtoCPUConversionPasses.cpp ADDITIONAL_HEADER_DIRS diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp new file mode 100644 index 00000000..86e865a4 --- /dev/null +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp @@ -0,0 +1,240 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#include "LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.h" +#include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" +#include "LoSPN/LoSPNOps.h" +#include "mlir/Dialect/Vector/VectorOps.h" +#include "mlir/Dialect/StandardOps/IR/Ops.h" + +using namespace mlir; +using namespace mlir::spn; +using namespace mlir::spn::low; +using namespace mlir::spn::low::slp; + +SLPFunctionTransformer::SLPFunctionTransformer(std::unique_ptr&& graph, MLIRContext* context) : root{ + std::move(graph)}, builder{context}, typeConverter{static_cast(root->numLanes())} { + +} + + +// Anonymous namespace for helper functions. +namespace { + + mlir::Value extractMemRefOperand(mlir::Operation* op, mlir::OpBuilder& builder) { + if (auto batchRead = llvm::dyn_cast(op)) { + builder.setInsertionPoint(op); + auto const& memRef = batchRead.batchMem(); + auto const& batchOffset = batchRead.batchIndex(); + auto const& sampleOffset = + builder.create(op->getLoc(), builder.getIndexAttr(batchRead.sampleIndex())); + mlir::ValueRange indices{batchOffset, sampleOffset->getResult(0)}; + return builder.create(op->getLoc(), memRef, indices); + } + assert(false); + } + + void eraseFromFunction(SLPNode const& root) { + for (size_t lane = 0; lane < root.numLanes(); ++lane) { + for (size_t i = 0; i < root.numVectors(); ++i) { + root.getOperation(lane, i)->remove(); + } + } + for (auto const& operand : root.getOperands()) { + eraseFromFunction(*operand); + } + } +} + +void SLPFunctionTransformer::transform() { + transform(root.get(), 0); + eraseFromFunction(*root); + // TODO: create remaining use extractions +} + +Value SLPFunctionTransformer::transform(SLPNode* node, size_t vectorIndex) { + node->dump(); + llvm::dbgs() << "\n"; + if (std::uintptr_t(node->getOperation(0, 0)) == 0x562d7a63bad8 || dyn_cast(node->getOperation(0, 0))) { + llvm::dbgs() << "\n"; + } + + vectorsDone[node]++; + + auto vectorType = typeConverter.convertType(node->getResultType()); + + auto const& vectorizableOps = node->getVector(vectorIndex); + vectorizedOps.insert(vectorizableOps.begin(), vectorizableOps.end()); + auto const& firstOp = vectorizableOps.front(); + auto const& loc = firstOp->getLoc(); + + if (areBroadcastable(vectorizableOps)) { + builder.setInsertionPoint(firstOp); + auto value = firstOp->getResult(0); + auto vectorOp = builder.create(loc, vectorType, value); + return applyCreation(node, vectorIndex, vectorOp); + } + + if (areConsecutiveLoads(vectorizableOps)) { + builder.setInsertionPoint(firstOp); + auto batchRead = static_cast(firstOp); + auto const& base = batchRead.batchMem(); + auto const& batchIndex = batchRead.batchIndex(); + auto const& offset = builder.create(loc, builder.getIndexAttr(batchRead.sampleIndex())).getResult(); + auto indices = ValueRange{batchIndex, offset}; + auto vectorOp = builder.create(loc, vectorType, base, indices); + return applyCreation(node, vectorIndex, vectorOp); + } + + if (canBeGathered(vectorizableOps)) { + builder.setInsertionPoint(firstOp); + std::vector elements; + for (auto const& op : vectorizableOps) { + auto batchRead = static_cast(op); + auto const& base = batchRead.batchMem(); + auto const& batchIndex = batchRead.batchIndex(); + unsigned sampleIndex = batchRead.sampleIndex(); + auto const& memRefTuple = std::make_tuple(base, batchIndex, sampleIndex); + if (memRefLoads.count(memRefTuple)) { + elements.emplace_back(memRefLoads[memRefTuple]); + } else { + auto const& offset = builder.create(loc, builder.getIndexAttr(sampleIndex)).getResult(); + auto indices = ValueRange{batchIndex, offset}; + Value element = builder.create(loc, base, indices); + memRefLoads[memRefTuple] = element; + elements.emplace_back(element); + } + } + Operation* vectorOp = builder.create(loc, vectorType, elements[0]); + for (size_t i = 1; i < vectorizableOps.size(); ++i) { + vectorOp = builder.create(loc, elements[i], vectorOp->getResult(0), i); + } + vectorOp->getBlock()->dump(); + return applyCreation(node, vectorIndex, vectorOp); + } + + if (node->isUniform() && firstOp->getNumOperands() > 0) { + + llvm::SmallVector operands; + + for (size_t i = 0; i < firstOp->getNumOperands(); ++i) { + + if (node->numOperands() == 0) { + size_t nextIndex = vectorsDone[node]; + if (nextIndex < node->numVectors()) { + operands.emplace_back(transform(node, nextIndex)); + } else { + auto nodeInput = node->getNodeInput(nodeInputsDone[node]++); + nodeInput.dump(); + if (nodeInput.getType().isa()) { + nodeInput = extractMemRefOperand(vectorizableOps[0], builder); + nodeInput.dump(); + } + builder.setInsertionPointAfterValue(nodeInput); + Operation* vectorOp = builder.create(loc, vectorType, nodeInput); + for (size_t lane = 1; lane < node->numLanes(); ++lane) { + nodeInput = node->getNodeInput(nodeInputsDone[node]++); + nodeInput.dump(); + if (nodeInput.getType().isa()) { + nodeInput = extractMemRefOperand(vectorizableOps[lane], builder); + nodeInput.dump(); + } + builder.setInsertionPointAfterValue(nodeInput); + vectorOp = builder.create(vectorOp->getLoc(), + nodeInput, + vectorOp->getResult(0), + lane); + } + operands.emplace_back(applyCreation(node, vectorIndex, vectorOp)); + } + } else { + + size_t initialOperandIndex = vectorIndex % node->getOperands().size(); + + size_t operandNodeIndex = initialOperandIndex; + SLPNode* operandNode = (i == 0) ? node : node->getOperand(operandNodeIndex); + bool useNodeInputs = false; + + size_t nextVectorIndex = vectorsDone[operandNode]; + while (nextVectorIndex >= operandNode->numVectors()) { + operandNode = node->getOperand(operandNodeIndex % node->getOperands().size()); + nextVectorIndex = vectorsDone[operandNode]; + // Check if we have looped through all operands already. + if (operandNodeIndex++ == initialOperandIndex + node->numOperands()) { + useNodeInputs = true; + break; + } + } + + if (useNodeInputs) { + auto nodeInput = node->getNodeInput(nodeInputsDone[node]++); + nodeInput.dump(); + if (nodeInput.getType().isa()) { + nodeInput = extractMemRefOperand(vectorizableOps[0], builder); + nodeInput.dump(); + } + builder.setInsertionPointAfterValue(nodeInput); + Operation* vectorOp = builder.create(loc, vectorType, nodeInput); + for (size_t lane = 1; lane < node->numLanes(); ++lane) { + nodeInput = node->getNodeInput(nodeInputsDone[node]++); + if (nodeInput.getType().isa()) { + nodeInput = extractMemRefOperand(vectorizableOps[0], builder); + nodeInput.dump(); + } + builder.setInsertionPointAfterValue(nodeInput); + vectorOp = builder.create(vectorOp->getLoc(), + nodeInput, + vectorOp->getResult(0), + lane); + } + operands.emplace_back(applyCreation(node, vectorIndex, vectorOp)); + } else { + operands.emplace_back(transform(operandNode, nextVectorIndex)); + } + + } + } + + auto vectorOp = Operation::create(loc, + firstOp->getName(), + vectorType, + operands, + firstOp->getAttrs(), + firstOp->getSuccessors(), + firstOp->getNumRegions()); + builder.setInsertionPoint(firstOp); + builder.insert(vectorOp); + return applyCreation(node, vectorIndex, vectorOp); + } + + Operation* vectorOp = builder.create(loc, vectorType, firstOp->getResult(0)); + for (size_t lane = 1; lane < node->numLanes(); ++lane) { + vectorOp = builder.create(vectorOp->getLoc(), + vectorizableOps[lane]->getResult(0), + vectorOp->getResult(0), + lane); + } + + return applyCreation(node, vectorIndex, vectorOp); +} + +Value SLPFunctionTransformer::applyCreation(SLPNode* node, + size_t vectorIndex, + Operation* createdVectorOp) { + for (size_t lane = 0; lane < node->numLanes(); ++lane) { + auto* operation = node->getOperation(lane, vectorIndex); + for (auto* use : operation->getUsers()) { + for (size_t i = 0; i < use->getNumOperands(); ++i) { + if (use->getOperand(i) == operation->getResult(0) && !vectorizedOps.count(use)) { + operandExtractions[use][i] = std::make_pair(createdVectorOp, lane); + break; + } + } + } + operandExtractions.erase(operation); + } + return createdVectorOp->getResult(0); +} + diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp index f337fb40..05c14d19 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp @@ -27,7 +27,17 @@ namespace { bool vectorizable(std::vector const& operations) { for (size_t i = 0; i < operations.size(); ++i) { auto* op = operations[i]; - if (!op->hasTrait() || (op->getName() != operations.front()->getName())) { + if (!op->hasTrait()) { + return false; + } + if (op->getName() != operations.front()->getName()) { + return false; + } + if (op->getNumResults() != 1) { + return false; + } + // MLIR disallows index types in vectors. + if (op->getResult(0).getType().isa()) { return false; } } @@ -125,23 +135,23 @@ void SLPGraphBuilder::buildGraph(std::vector const& operations, SLPN else { for (size_t i = 0; i < operations.front()->getNumOperands(); ++i) { std::vector operands; - bool containsBlockArgument = false; + std::vector operandOperations; + bool usesBlockArguments = false; for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { auto operand = currentNode->getOperation(lane, 0)->getOperand(i); operands.emplace_back(operand); - containsBlockArgument |= operand.isa(); + if (operand.isa()) { + usesBlockArguments = true; + } else { + operandOperations.emplace_back(operand.getDefiningOp()); + } } - // Only consider operands that don't contain block arguments for further graph building. - if (containsBlockArgument) { + + if (usesBlockArguments || !vectorizable(operandOperations)) { for (auto const& operand : operands) { currentNode->addNodeInput(operand); } } else { - std::vector operandOperations; - operandOperations.reserve(operands.size()); - for (auto const& operand : operands) { - operandOperations.emplace_back(operand.getDefiningOp()); - } buildGraph(operandOperations, currentNode->addOperand(operandOperations)); } } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp index 94dba880..c4a17c5c 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp @@ -43,3 +43,29 @@ bool mlir::spn::low::slp::areConsecutiveLoads(std::vector const& loa return true; } +bool mlir::spn::low::slp::canBeGathered(std::vector const& loads) { + auto firstLoad = dyn_cast(loads.front()); + if (!firstLoad) { + return false; + } + for (size_t i = 1; i < loads.size(); ++i) { + auto load = dyn_cast(loads[i]); + if (!load) { + return false; + } + if (firstLoad.batchMem() != load.batchMem()) { + return false; + } + if (firstLoad.batchIndex() != load.batchIndex()) { + return false; + } + } + return true; +} + +bool mlir::spn::low::slp::areBroadcastable(std::vector const& ops) { + return std::all_of(std::begin(ops), std::end(ops), [&](mlir::Operation* op) { + return op == ops.front(); + }); +} + diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp index 3cbf2af0..509b0e92 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp @@ -7,6 +7,7 @@ #include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h" +#include "LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.h" #include "LoSPNtoCPU/Vectorization/TargetInformation.h" #include "LoSPNtoCPU/LoSPNtoCPUTypeConverter.h" #include "LoSPNtoCPU/Vectorization/LoSPNVectorizationTypeConverter.h" @@ -19,12 +20,6 @@ using namespace mlir::spn::low::slp; // Anonymous namespace for helper functions. namespace { - bool isBroadcastable(std::vector const& vector) { - return std::all_of(std::begin(vector), std::end(vector), [&](mlir::Operation* op) { - return op == vector.front(); - }); - } - /// For debugging purposes. void printOperation(mlir::Operation* op) { llvm::dbgs() << "node_" << op << "[label=\"" << op->getName().getStringRef() << "\\n" << op; @@ -104,159 +99,12 @@ void SLPVectorizationPass::runOnOperation() { auto seeds = seedAnalysis.getSeeds(width, seedAnalysis.getOpDepths(), SearchMode::UseBeforeDef); assert(!seeds.empty() && "couldn't find a seed!"); - printSeedTree(seeds.front()); + //printSeedTree(seeds.front()); SLPGraphBuilder builder{3}; auto graph = builder.build(seeds.front()); - transform(graph.get()); - -} - -void SLPVectorizationPass::transform(SLPNode* root) { - root->dumpGraph(); - std::map vectorsDone; - std::map nodeInputsDone; - auto rootOperation = transform(root, 0, vectorsDone, nodeInputsDone); + SLPFunctionTransformer transformer(std::move(graph), &getContext()); + transformer.transform(); getOperation()->dump(); - llvm::dbgs() << extractions.size() << "\n"; - assert(false); - // Update users of vector operations that aren't contained in the graph. -} - -mlir::Value SLPVectorizationPass::transform(SLPNode* node, - size_t vectorIndex, - std::map& vectorsDone, - std::map& nodeInputsDone) { - - node->dump(); - llvm::dbgs() << "\n"; - if (std::uintptr_t(node->getOperation(0, 0)) == 0x716008) { - llvm::dbgs() << "\n"; - } - - vectorsDone[node]++; - - LoSPNVectorizationTypeConverter typeConverter{static_cast(node->numLanes())}; - auto vectorType = typeConverter.convertType(node->getResultType()); - auto builder = OpBuilder{&getContext()}; - - auto const& vectorizableOps = node->getVector(vectorIndex); - auto const& firstOp = vectorizableOps.front(); - auto const& loc = firstOp->getLoc(); - builder.setInsertionPoint(firstOp); - - if (isBroadcastable(vectorizableOps)) { - auto value = firstOp->getResult(0); - auto vectorOp = builder.create(loc, vectorType, value); - return applyCreation(node, vectorIndex, vectorOp); - } - - if (areConsecutiveLoads(vectorizableOps)) { - auto base = firstOp->getResult(0); - auto zero = builder.create(loc, builder.getIndexAttr(0)); - auto indices = ValueRange{zero.getResult()}; - auto vectorOp = builder.create(loc, vectorType, base, indices); - return applyCreation(node, vectorIndex, vectorOp); - } - - if (node->isUniform() && firstOp->getNumOperands() > 0) { - - llvm::SmallVector operands; - - for (size_t i = 0; i < firstOp->getNumOperands(); ++i) { - - if (node->numOperands() == 0) { - size_t nextIndex = vectorsDone[node]; - if (nextIndex < node->numVectors()) { - operands.emplace_back(transform(node, nextIndex, vectorsDone, nodeInputsDone)); - } else { - auto nodeInput = node->getNodeInput(nodeInputsDone[node]++); - nodeInput.dump(); - Operation* vectorOp = builder.create(loc, vectorType, nodeInput); - for (size_t lane = 1; lane < node->numLanes(); ++lane) { - nodeInput = node->getNodeInput(nodeInputsDone[node]++); - vectorOp = builder.create(vectorOp->getLoc(), - nodeInput, - vectorOp->getResult(0), - lane); - } - operands.emplace_back(applyCreation(node, vectorIndex, vectorOp)); - } - } else { - - size_t initialOperandIndex = vectorIndex % node->getOperands().size(); - - size_t operandNodeIndex = initialOperandIndex; - SLPNode* operandNode = (i == 0) ? node : node->getOperand(operandNodeIndex); - bool useNodeInputs = false; - - size_t nextVectorIndex = vectorsDone[operandNode]; - while (nextVectorIndex >= operandNode->numVectors()) { - operandNode = node->getOperand(operandNodeIndex % node->getOperands().size()); - nextVectorIndex = vectorsDone[operandNode]; - // Check if we have looped through all operands already. - if (operandNodeIndex++ == initialOperandIndex + node->numOperands()) { - useNodeInputs = true; - break; - } - } - - if (useNodeInputs) { - auto nodeInput = node->getNodeInput(nodeInputsDone[node]++); - nodeInput.dump(); - Operation* vectorOp = builder.create(loc, vectorType, nodeInput); - for (size_t lane = 1; lane < node->numLanes(); ++lane) { - nodeInput = node->getNodeInput(nodeInputsDone[node]++); - vectorOp = builder.create(vectorOp->getLoc(), - nodeInput, - vectorOp->getResult(0), - lane); - } - operands.emplace_back(applyCreation(node, vectorIndex, vectorOp)); - } else { - operands.emplace_back(transform(operandNode, nextVectorIndex, vectorsDone, nodeInputsDone)); - } - - } - } - - auto vectorOp = Operation::create(loc, - firstOp->getName(), - vectorType, - operands, - firstOp->getAttrs(), - firstOp->getSuccessors(), - firstOp->getNumRegions()); - builder.insert(vectorOp); - return applyCreation(node, vectorIndex, vectorOp); - } - - Operation* vectorOp = builder.create(loc, vectorType, firstOp->getResult(0)); - for (size_t lane = 1; lane < node->numLanes(); ++lane) { - vectorOp = builder.create(vectorOp->getLoc(), - vectorizableOps[lane]->getResult(0), - vectorOp->getResult(0), - lane); - } - - return applyCreation(node, vectorIndex, vectorOp); -} - -mlir::Value SLPVectorizationPass::applyCreation(SLPNode* node, - size_t vectorIndex, - Operation* createdVectorOp) { - for (size_t lane = 0; lane < node->numLanes(); ++lane) { - auto* operation = node->getOperation(lane, vectorIndex); - for (auto* use : operation->getUsers()) { - for (size_t i = 0; i < use->getNumOperands(); ++i) { - if (use->getOperand(i) == operation->getResult(0)) { - extractions[use][i] = std::make_pair(createdVectorOp, i); - break; - } - } - } - extractions.erase(operation); - } - return createdVectorOp->getResult(0); } std::unique_ptr mlir::spn::createSLPVectorizationPass() { diff --git a/mlir/tools/spnc-opt/spnc-opt.cpp b/mlir/tools/spnc-opt/spnc-opt.cpp index 54777e09..fb2edec7 100644 --- a/mlir/tools/spnc-opt/spnc-opt.cpp +++ b/mlir/tools/spnc-opt/spnc-opt.cpp @@ -104,10 +104,6 @@ int main(int argc, char** argv) { return mlir::spn::createSLPVectorizationPass(); }); - mlir::registerPass("spn-to-dot", "Prints the SPN to stderr", []() -> std::unique_ptr { - return mlir::createPrintOpGraphPass(); - }); - llvm::InitLLVM y(argc, argv); // Register any pass manager command line options. From f13c0d8ee2092fe84218e089c6b5f463c95b8434 Mon Sep 17 00:00:00 2001 From: CSV Date: Thu, 8 Apr 2021 22:48:59 +0200 Subject: [PATCH 066/385] Fix: insertion point madness. --- .../SLP/SLPFunctionTransformer.h | 20 +- .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.h | 1 + .../SLP/SLPFunctionTransformer.cpp | 211 +++++++++--------- .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp | 2 +- .../SLP/SLPVectorizationPass.cpp | 43 ++-- 5 files changed, 134 insertions(+), 143 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.h index def6a5e4..78481892 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.h @@ -11,9 +11,7 @@ #include "LoSPNtoCPU/LoSPNtoCPUTypeConverter.h" #include "LoSPNtoCPU/Vectorization/LoSPNVectorizationTypeConverter.h" #include "SLPNode.h" -#include -#include -#include +#include namespace mlir { namespace spn { @@ -24,16 +22,24 @@ namespace mlir { public: - SLPFunctionTransformer(std::unique_ptr&& graph, MLIRContext* context); + SLPFunctionTransformer(std::unique_ptr&& graph, FuncOp function); void transform(); private: Value transform(SLPNode* node, size_t vectorIndex); - Value applyCreation(SLPNode* node, size_t vectorIndex, Operation* vectorOp); + Value applyCreation(SLPNode* node, size_t vectorIndex, Operation* vectorOp, bool keepFirst = false); + + Value extractMemRefOperand(Operation* op, Operation* position = nullptr); + Operation* broadcastFirstInsertRest(Operation* beforeOp, + Type const& vectorType, + SmallVector& elements); + Value getOrCreateConstant(unsigned index, bool asIndex = true); std::unique_ptr root; + FuncOp function; + OpBuilder builder; LoSPNVectorizationTypeConverter typeConverter; @@ -41,10 +47,12 @@ namespace mlir { /// were deleted during vectorization. llvm::DenseMap>> operandExtractions; llvm::DenseMap, Value> memRefLoads; + llvm::DenseMap createdIndexConstants; + llvm::DenseMap createdUnsignedConstants; llvm::DenseMap vectorsDone; llvm::DenseMap nodeInputsDone; - std::set vectorizedOps; + std::unordered_set vectorizedOps; }; } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h index 992aa4b8..3088f744 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h @@ -17,6 +17,7 @@ namespace mlir { bool areConsecutiveLoads(std::vector const& loads); bool canBeGathered(std::vector const& loads); bool areBroadcastable(std::vector const& ops); + } } } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp index 86e865a4..b4d3473f 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp @@ -14,52 +14,35 @@ using namespace mlir::spn; using namespace mlir::spn::low; using namespace mlir::spn::low::slp; -SLPFunctionTransformer::SLPFunctionTransformer(std::unique_ptr&& graph, MLIRContext* context) : root{ - std::move(graph)}, builder{context}, typeConverter{static_cast(root->numLanes())} { +SLPFunctionTransformer::SLPFunctionTransformer(std::unique_ptr&& graph, FuncOp function) : root{ + std::move(graph)}, function{function}, builder{function->getContext()}, typeConverter{ + static_cast(root->numLanes())} { } +void SLPFunctionTransformer::transform() { + + builder.setInsertionPoint(root->getOperation(0, 0)); + + transform(root.get(), 0); + + // Create extractions for operations that need values from the SLPGraph, but are located outside of it. + for (auto const& entry : operandExtractions) { + auto* op = entry.getFirst(); + for (auto const& extraction : entry.getSecond()) { -// Anonymous namespace for helper functions. -namespace { + auto const& vector = extraction.second.first->getResult(0); + auto const& position = getOrCreateConstant(extraction.second.second); - mlir::Value extractMemRefOperand(mlir::Operation* op, mlir::OpBuilder& builder) { - if (auto batchRead = llvm::dyn_cast(op)) { builder.setInsertionPoint(op); - auto const& memRef = batchRead.batchMem(); - auto const& batchOffset = batchRead.batchIndex(); - auto const& sampleOffset = - builder.create(op->getLoc(), builder.getIndexAttr(batchRead.sampleIndex())); - mlir::ValueRange indices{batchOffset, sampleOffset->getResult(0)}; - return builder.create(op->getLoc(), memRef, indices); - } - assert(false); - } + auto element = builder.create(op->getLoc(), vector, position); + op->setOperand(extraction.first, element); - void eraseFromFunction(SLPNode const& root) { - for (size_t lane = 0; lane < root.numLanes(); ++lane) { - for (size_t i = 0; i < root.numVectors(); ++i) { - root.getOperation(lane, i)->remove(); - } - } - for (auto const& operand : root.getOperands()) { - eraseFromFunction(*operand); } } } -void SLPFunctionTransformer::transform() { - transform(root.get(), 0); - eraseFromFunction(*root); - // TODO: create remaining use extractions -} - Value SLPFunctionTransformer::transform(SLPNode* node, size_t vectorIndex) { - node->dump(); - llvm::dbgs() << "\n"; - if (std::uintptr_t(node->getOperation(0, 0)) == 0x562d7a63bad8 || dyn_cast(node->getOperation(0, 0))) { - llvm::dbgs() << "\n"; - } vectorsDone[node]++; @@ -71,48 +54,27 @@ Value SLPFunctionTransformer::transform(SLPNode* node, size_t vectorIndex) { auto const& loc = firstOp->getLoc(); if (areBroadcastable(vectorizableOps)) { - builder.setInsertionPoint(firstOp); auto value = firstOp->getResult(0); - auto vectorOp = builder.create(loc, vectorType, value); - return applyCreation(node, vectorIndex, vectorOp); + return applyCreation(node, vectorIndex, builder.create(loc, vectorType, value), true); } if (areConsecutiveLoads(vectorizableOps)) { - builder.setInsertionPoint(firstOp); auto batchRead = static_cast(firstOp); auto const& base = batchRead.batchMem(); auto const& batchIndex = batchRead.batchIndex(); - auto const& offset = builder.create(loc, builder.getIndexAttr(batchRead.sampleIndex())).getResult(); + auto const& offset = getOrCreateConstant(batchRead.sampleIndex()); + auto indices = ValueRange{batchIndex, offset}; - auto vectorOp = builder.create(loc, vectorType, base, indices); - return applyCreation(node, vectorIndex, vectorOp); + + return applyCreation(node, vectorIndex, builder.create(loc, vectorType, base, indices)); } if (canBeGathered(vectorizableOps)) { - builder.setInsertionPoint(firstOp); - std::vector elements; + SmallVector elements; for (auto const& op : vectorizableOps) { - auto batchRead = static_cast(op); - auto const& base = batchRead.batchMem(); - auto const& batchIndex = batchRead.batchIndex(); - unsigned sampleIndex = batchRead.sampleIndex(); - auto const& memRefTuple = std::make_tuple(base, batchIndex, sampleIndex); - if (memRefLoads.count(memRefTuple)) { - elements.emplace_back(memRefLoads[memRefTuple]); - } else { - auto const& offset = builder.create(loc, builder.getIndexAttr(sampleIndex)).getResult(); - auto indices = ValueRange{batchIndex, offset}; - Value element = builder.create(loc, base, indices); - memRefLoads[memRefTuple] = element; - elements.emplace_back(element); - } - } - Operation* vectorOp = builder.create(loc, vectorType, elements[0]); - for (size_t i = 1; i < vectorizableOps.size(); ++i) { - vectorOp = builder.create(loc, elements[i], vectorOp->getResult(0), i); + elements.emplace_back(extractMemRefOperand(op)); } - vectorOp->getBlock()->dump(); - return applyCreation(node, vectorIndex, vectorOp); + return applyCreation(node, vectorIndex, broadcastFirstInsertRest(firstOp, vectorType, elements)); } if (node->isUniform() && firstOp->getNumOperands() > 0) { @@ -126,27 +88,15 @@ Value SLPFunctionTransformer::transform(SLPNode* node, size_t vectorIndex) { if (nextIndex < node->numVectors()) { operands.emplace_back(transform(node, nextIndex)); } else { - auto nodeInput = node->getNodeInput(nodeInputsDone[node]++); - nodeInput.dump(); - if (nodeInput.getType().isa()) { - nodeInput = extractMemRefOperand(vectorizableOps[0], builder); - nodeInput.dump(); - } - builder.setInsertionPointAfterValue(nodeInput); - Operation* vectorOp = builder.create(loc, vectorType, nodeInput); - for (size_t lane = 1; lane < node->numLanes(); ++lane) { - nodeInput = node->getNodeInput(nodeInputsDone[node]++); - nodeInput.dump(); + SmallVector vectorElements; + for (size_t lane = 0; lane < node->numLanes(); ++lane) { + auto nodeInput = node->getNodeInput(nodeInputsDone[node]++); if (nodeInput.getType().isa()) { - nodeInput = extractMemRefOperand(vectorizableOps[lane], builder); - nodeInput.dump(); + nodeInput = extractMemRefOperand(vectorizableOps[lane]); } - builder.setInsertionPointAfterValue(nodeInput); - vectorOp = builder.create(vectorOp->getLoc(), - nodeInput, - vectorOp->getResult(0), - lane); + vectorElements.emplace_back(nodeInput); } + auto* vectorOp = broadcastFirstInsertRest(firstOp, vectorType, vectorElements); operands.emplace_back(applyCreation(node, vectorIndex, vectorOp)); } } else { @@ -169,26 +119,15 @@ Value SLPFunctionTransformer::transform(SLPNode* node, size_t vectorIndex) { } if (useNodeInputs) { - auto nodeInput = node->getNodeInput(nodeInputsDone[node]++); - nodeInput.dump(); - if (nodeInput.getType().isa()) { - nodeInput = extractMemRefOperand(vectorizableOps[0], builder); - nodeInput.dump(); - } - builder.setInsertionPointAfterValue(nodeInput); - Operation* vectorOp = builder.create(loc, vectorType, nodeInput); - for (size_t lane = 1; lane < node->numLanes(); ++lane) { - nodeInput = node->getNodeInput(nodeInputsDone[node]++); + SmallVector vectorElements; + for (size_t lane = 0; lane < node->numLanes(); ++lane) { + auto nodeInput = node->getNodeInput(nodeInputsDone[node]++); if (nodeInput.getType().isa()) { - nodeInput = extractMemRefOperand(vectorizableOps[0], builder); - nodeInput.dump(); + nodeInput = extractMemRefOperand(vectorizableOps[lane]); } - builder.setInsertionPointAfterValue(nodeInput); - vectorOp = builder.create(vectorOp->getLoc(), - nodeInput, - vectorOp->getResult(0), - lane); + vectorElements.emplace_back(nodeInput); } + auto* vectorOp = broadcastFirstInsertRest(firstOp, vectorType, vectorElements); operands.emplace_back(applyCreation(node, vectorIndex, vectorOp)); } else { operands.emplace_back(transform(operandNode, nextVectorIndex)); @@ -204,28 +143,27 @@ Value SLPFunctionTransformer::transform(SLPNode* node, size_t vectorIndex) { firstOp->getAttrs(), firstOp->getSuccessors(), firstOp->getNumRegions()); - builder.setInsertionPoint(firstOp); - builder.insert(vectorOp); - return applyCreation(node, vectorIndex, vectorOp); + return applyCreation(node, vectorIndex, builder.insert(vectorOp)); } - Operation* vectorOp = builder.create(loc, vectorType, firstOp->getResult(0)); - for (size_t lane = 1; lane < node->numLanes(); ++lane) { - vectorOp = builder.create(vectorOp->getLoc(), - vectorizableOps[lane]->getResult(0), - vectorOp->getResult(0), - lane); + llvm::SmallVector vectorElements; + for (auto op : vectorizableOps) { + vectorElements.emplace_back(op->getResult(0)); } - - return applyCreation(node, vectorIndex, vectorOp); + return applyCreation(node, vectorIndex, broadcastFirstInsertRest(firstOp, vectorType, vectorElements)); } Value SLPFunctionTransformer::applyCreation(SLPNode* node, size_t vectorIndex, - Operation* createdVectorOp) { + Operation* createdVectorOp, + bool keepFirst) { for (size_t lane = 0; lane < node->numLanes(); ++lane) { auto* operation = node->getOperation(lane, vectorIndex); for (auto* use : operation->getUsers()) { + // Happens when creating broadcast operations. + if (use == createdVectorOp) { + continue; + } for (size_t i = 0; i < use->getNumOperands(); ++i) { if (use->getOperand(i) == operation->getResult(0) && !vectorizedOps.count(use)) { operandExtractions[use][i] = std::make_pair(createdVectorOp, lane); @@ -234,7 +172,60 @@ Value SLPFunctionTransformer::applyCreation(SLPNode* node, } } operandExtractions.erase(operation); + if (lane == 0 && keepFirst) { + continue; + } + operation->remove(); } return createdVectorOp->getResult(0); } +Operation* SLPFunctionTransformer::broadcastFirstInsertRest(Operation* beforeOp, + Type const& vectorType, + SmallVector& elements) { + Operation* vectorOp = builder.create(beforeOp->getLoc(), vectorType, elements[0]); + for (size_t i = 1; i < elements.size(); ++i) { + auto const& position = getOrCreateConstant(i, false); + vectorOp = + builder.create(beforeOp->getLoc(), elements[i], vectorOp->getResult(0), position); + } + return vectorOp; +} + +Value SLPFunctionTransformer::getOrCreateConstant(unsigned index, bool asIndex) { + auto& createdConstants = asIndex ? createdIndexConstants : createdUnsignedConstants; + if (!createdConstants.count(index)) { + auto const& insertionPoint = builder.saveInsertionPoint(); + auto attribute = asIndex ? builder.getIndexAttr(index) : builder.getUI32IntegerAttr(index); + builder.setInsertionPointToStart(&function.body().front()); + createdConstants[index] = builder.create(builder.getUnknownLoc(), attribute); + builder.restoreInsertionPoint(insertionPoint); + } + return createdConstants[index]; +} + +Value SLPFunctionTransformer::extractMemRefOperand(Operation* op, Operation* position) { + Value base; + ValueRange indices; + if (auto batchRead = dyn_cast(op)) { + base = batchRead.batchMem(); + auto const& batchIndex = batchRead.batchIndex(); + auto const& sampleIndex = batchRead.sampleIndex(); + auto const& memRefTuple = std::make_tuple(base, batchIndex, sampleIndex); + + if (memRefLoads.count(memRefTuple)) { + return memRefLoads[memRefTuple]; + } + + indices = {batchIndex, getOrCreateConstant(sampleIndex)}; + + } else { + assert(false); + } + + auto const& insertionPoint = builder.saveInsertionPoint(); + builder.setInsertionPoint(op); + auto load = builder.create(op->getLoc(), base, indices); + builder.restoreInsertionPoint(insertionPoint); + return load; +} \ No newline at end of file diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp index c4a17c5c..651c1e5d 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp @@ -65,7 +65,7 @@ bool mlir::spn::low::slp::canBeGathered(std::vector const& loads) { bool mlir::spn::low::slp::areBroadcastable(std::vector const& ops) { return std::all_of(std::begin(ops), std::end(ops), [&](mlir::Operation* op) { - return op == ops.front(); + return OperationEquivalence::isEquivalentTo(op, ops.front()); }); } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp index 509b0e92..1ee442fa 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp @@ -20,29 +20,6 @@ using namespace mlir::spn::low::slp; // Anonymous namespace for helper functions. namespace { - /// For debugging purposes. - void printOperation(mlir::Operation* op) { - llvm::dbgs() << "node_" << op << "[label=\"" << op->getName().getStringRef() << "\\n" << op; - if (auto constantOp = llvm::dyn_cast(op)) { - if (constantOp.value().getType().isIntOrIndex()) { - llvm::dbgs() << "\\nvalue: " << std::to_string(constantOp.value().dyn_cast().getInt()); - } else if (constantOp.value().getType().isIntOrFloat()) { - llvm::dbgs() << "\\nvalue: " - << std::to_string(constantOp.value().dyn_cast().getValueAsDouble()); - } - } else if (auto batchReadOp = llvm::dyn_cast(op)) { - llvm::dbgs() << "\\nbatch mem: " << batchReadOp.batchMem().dyn_cast().getArgNumber(); - llvm::dbgs() << "\\nbatch index: " << batchReadOp.batchMem().dyn_cast().getArgNumber(); - llvm::dbgs() << "\\nsample index: " << batchReadOp.sampleIndex(); - } - llvm::dbgs() << "\", fillcolor=\"#a0522d\"];\n"; - } - - /// For debugging purposes. - void printEdge(mlir::Operation* src, mlir::Operation* dst, size_t index) { - llvm::dbgs() << "node_" << src << " -> node_" << dst << "[label=\"" << std::to_string(index) << "\"];\n"; - } - /// For debugging purposes. void printSeedTree(std::vector const& operations) { std::vector nodes; @@ -74,10 +51,24 @@ namespace { llvm::dbgs() << "rankdir = BT;\n"; llvm::dbgs() << "node[shape=box];\n"; for (auto& op : nodes) { - printOperation(op); + llvm::dbgs() << "node_" << op << "[label=\"" << op->getName().getStringRef() << "\\n" << op; + if (auto constantOp = llvm::dyn_cast(op)) { + if (constantOp.value().getType().isIntOrIndex()) { + llvm::dbgs() << "\\nvalue: " << std::to_string(constantOp.value().dyn_cast().getInt()); + } else if (constantOp.value().getType().isIntOrFloat()) { + llvm::dbgs() << "\\nvalue: " + << std::to_string(constantOp.value().dyn_cast().getValueAsDouble()); + } + } else if (auto batchReadOp = llvm::dyn_cast(op)) { + llvm::dbgs() << "\\nbatch mem: " << batchReadOp.batchMem().dyn_cast().getArgNumber(); + llvm::dbgs() << "\\nbatch index: " << batchReadOp.batchMem().dyn_cast().getArgNumber(); + llvm::dbgs() << "\\nsample index: " << batchReadOp.sampleIndex(); + } + llvm::dbgs() << "\", fillcolor=\"#a0522d\"];\n"; } for (auto& edge : edges) { - printEdge(std::get<0>(edge), std::get<1>(edge), std::get<2>(edge)); + llvm::dbgs() << "node_" << std::get<0>(edge) << " -> node_" << std::get<1>(edge) << "[label=\"" + << std::to_string(std::get<2>(edge)) << "\"];\n"; } llvm::dbgs() << "}\n"; } @@ -102,7 +93,7 @@ void SLPVectorizationPass::runOnOperation() { //printSeedTree(seeds.front()); SLPGraphBuilder builder{3}; auto graph = builder.build(seeds.front()); - SLPFunctionTransformer transformer(std::move(graph), &getContext()); + SLPFunctionTransformer transformer(std::move(graph), function); transformer.transform(); getOperation()->dump(); } From e19564f2f0f9dc0743821839031b6a587b6b1994 Mon Sep 17 00:00:00 2001 From: CSV Date: Mon, 12 Apr 2021 15:08:14 +0200 Subject: [PATCH 067/385] Fix: broken InsertElementOp due to wrong position value type. --- .../LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.h | 2 +- .../LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.h index 78481892..8f5ea0d3 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.h @@ -31,7 +31,7 @@ namespace mlir { Value transform(SLPNode* node, size_t vectorIndex); Value applyCreation(SLPNode* node, size_t vectorIndex, Operation* vectorOp, bool keepFirst = false); - Value extractMemRefOperand(Operation* op, Operation* position = nullptr); + Value extractMemRefOperand(Operation* op); Operation* broadcastFirstInsertRest(Operation* beforeOp, Type const& vectorType, SmallVector& elements); diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp index b4d3473f..ff2942e5 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp @@ -196,7 +196,7 @@ Value SLPFunctionTransformer::getOrCreateConstant(unsigned index, bool asIndex) auto& createdConstants = asIndex ? createdIndexConstants : createdUnsignedConstants; if (!createdConstants.count(index)) { auto const& insertionPoint = builder.saveInsertionPoint(); - auto attribute = asIndex ? builder.getIndexAttr(index) : builder.getUI32IntegerAttr(index); + auto const& attribute = asIndex ? builder.getIndexAttr(index) : builder.getIntegerAttr(builder.getI32Type(), index); builder.setInsertionPointToStart(&function.body().front()); createdConstants[index] = builder.create(builder.getUnknownLoc(), attribute); builder.restoreInsertionPoint(insertionPoint); @@ -204,7 +204,7 @@ Value SLPFunctionTransformer::getOrCreateConstant(unsigned index, bool asIndex) return createdConstants[index]; } -Value SLPFunctionTransformer::extractMemRefOperand(Operation* op, Operation* position) { +Value SLPFunctionTransformer::extractMemRefOperand(Operation* op) { Value base; ValueRange indices; if (auto batchRead = dyn_cast(op)) { From 1a54aeb0e5583e0ecdd8a7507531cac9e1363239 Mon Sep 17 00:00:00 2001 From: CSV Date: Tue, 13 Apr 2021 14:21:15 +0200 Subject: [PATCH 068/385] SLPGraph > CFG improvements. --- .../SLP/SLPFunctionTransformer.h | 5 +- .../SLP/SLPFunctionTransformer.cpp | 106 +++++++++--------- .../Vectorization/SLP/SLPGraphBuilder.cpp | 10 +- mlir/tools/spnc-opt/spnc-opt.cpp | 2 +- 4 files changed, 58 insertions(+), 65 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.h index 8f5ea0d3..b71f6793 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.h @@ -29,7 +29,7 @@ namespace mlir { private: Value transform(SLPNode* node, size_t vectorIndex); - Value applyCreation(SLPNode* node, size_t vectorIndex, Operation* vectorOp, bool keepFirst = false); + Value applyCreation(SLPNode* node, size_t vectorIndex, Operation* vectorOp); Value extractMemRefOperand(Operation* op); Operation* broadcastFirstInsertRest(Operation* beforeOp, @@ -43,9 +43,6 @@ namespace mlir { OpBuilder builder; LoSPNVectorizationTypeConverter typeConverter; - /// Stores where operations can find their operands in case their defining operations - /// were deleted during vectorization. - llvm::DenseMap>> operandExtractions; llvm::DenseMap, Value> memRefLoads; llvm::DenseMap createdIndexConstants; llvm::DenseMap createdUnsignedConstants; diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp index ff2942e5..06996f84 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp @@ -21,25 +21,8 @@ SLPFunctionTransformer::SLPFunctionTransformer(std::unique_ptr&& graph, } void SLPFunctionTransformer::transform() { - builder.setInsertionPoint(root->getOperation(0, 0)); - transform(root.get(), 0); - - // Create extractions for operations that need values from the SLPGraph, but are located outside of it. - for (auto const& entry : operandExtractions) { - auto* op = entry.getFirst(); - for (auto const& extraction : entry.getSecond()) { - - auto const& vector = extraction.second.first->getResult(0); - auto const& position = getOrCreateConstant(extraction.second.second); - - builder.setInsertionPoint(op); - auto element = builder.create(op->getLoc(), vector, position); - op->setOperand(extraction.first, element); - - } - } } Value SLPFunctionTransformer::transform(SLPNode* node, size_t vectorIndex) { @@ -48,17 +31,21 @@ Value SLPFunctionTransformer::transform(SLPNode* node, size_t vectorIndex) { auto vectorType = typeConverter.convertType(node->getResultType()); - auto const& vectorizableOps = node->getVector(vectorIndex); - vectorizedOps.insert(vectorizableOps.begin(), vectorizableOps.end()); - auto const& firstOp = vectorizableOps.front(); + auto const& vectorOps = node->getVector(vectorIndex); + vectorizedOps.insert(vectorOps.begin(), vectorOps.end()); + auto const& firstOp = vectorOps.front(); auto const& loc = firstOp->getLoc(); - if (areBroadcastable(vectorizableOps)) { + if (areBroadcastable(vectorOps)) { auto value = firstOp->getResult(0); - return applyCreation(node, vectorIndex, builder.create(loc, vectorType, value), true); + auto const& insertionPoint = builder.saveInsertionPoint(); + builder.setInsertionPointAfter(firstOp); + auto broadcast = builder.create(loc, vectorType, value); + builder.restoreInsertionPoint(insertionPoint); + return applyCreation(node, vectorIndex, broadcast); } - if (areConsecutiveLoads(vectorizableOps)) { + if (areConsecutiveLoads(vectorOps)) { auto batchRead = static_cast(firstOp); auto const& base = batchRead.batchMem(); auto const& batchIndex = batchRead.batchIndex(); @@ -69,9 +56,9 @@ Value SLPFunctionTransformer::transform(SLPNode* node, size_t vectorIndex) { return applyCreation(node, vectorIndex, builder.create(loc, vectorType, base, indices)); } - if (canBeGathered(vectorizableOps)) { + if (canBeGathered(vectorOps)) { SmallVector elements; - for (auto const& op : vectorizableOps) { + for (auto const& op : vectorOps) { elements.emplace_back(extractMemRefOperand(op)); } return applyCreation(node, vectorIndex, broadcastFirstInsertRest(firstOp, vectorType, elements)); @@ -92,7 +79,7 @@ Value SLPFunctionTransformer::transform(SLPNode* node, size_t vectorIndex) { for (size_t lane = 0; lane < node->numLanes(); ++lane) { auto nodeInput = node->getNodeInput(nodeInputsDone[node]++); if (nodeInput.getType().isa()) { - nodeInput = extractMemRefOperand(vectorizableOps[lane]); + nodeInput = extractMemRefOperand(vectorOps[lane]); } vectorElements.emplace_back(nodeInput); } @@ -123,7 +110,7 @@ Value SLPFunctionTransformer::transform(SLPNode* node, size_t vectorIndex) { for (size_t lane = 0; lane < node->numLanes(); ++lane) { auto nodeInput = node->getNodeInput(nodeInputsDone[node]++); if (nodeInput.getType().isa()) { - nodeInput = extractMemRefOperand(vectorizableOps[lane]); + nodeInput = extractMemRefOperand(vectorOps[lane]); } vectorElements.emplace_back(nodeInput); } @@ -136,44 +123,59 @@ Value SLPFunctionTransformer::transform(SLPNode* node, size_t vectorIndex) { } } - auto vectorOp = Operation::create(loc, - firstOp->getName(), - vectorType, - operands, - firstOp->getAttrs(), - firstOp->getSuccessors(), - firstOp->getNumRegions()); - return applyCreation(node, vectorIndex, builder.insert(vectorOp)); + Operation* vectorOp; + + if (dyn_cast(firstOp)) { + vectorOp = builder.create(loc, operands); + } else if (dyn_cast(firstOp)) { + vectorOp = builder.create(loc, operands); + } else { + vectorOp = builder.insert(Operation::create(loc, + firstOp->getName(), + vectorType, + operands, + firstOp->getAttrs(), + firstOp->getSuccessors(), + firstOp->getNumRegions())); + } + return applyCreation(node, vectorIndex, vectorOp); } llvm::SmallVector vectorElements; - for (auto op : vectorizableOps) { + for (auto op : vectorOps) { vectorElements.emplace_back(op->getResult(0)); } return applyCreation(node, vectorIndex, broadcastFirstInsertRest(firstOp, vectorType, vectorElements)); } -Value SLPFunctionTransformer::applyCreation(SLPNode* node, - size_t vectorIndex, - Operation* createdVectorOp, - bool keepFirst) { +Value SLPFunctionTransformer::applyCreation(SLPNode* node, size_t vectorIndex, Operation* createdVectorOp) { + + bool isBroadcast = dyn_cast(createdVectorOp); + for (size_t lane = 0; lane < node->numLanes(); ++lane) { auto* operation = node->getOperation(lane, vectorIndex); + if (isBroadcast && operation == node->getOperation(0, vectorIndex)) { + continue; + } + bool createExtraction = false; for (auto* use : operation->getUsers()) { - // Happens when creating broadcast operations. - if (use == createdVectorOp) { - continue; - } - for (size_t i = 0; i < use->getNumOperands(); ++i) { - if (use->getOperand(i) == operation->getResult(0) && !vectorizedOps.count(use)) { - operandExtractions[use][i] = std::make_pair(createdVectorOp, lane); - break; - } + if (vectorizedOps.find(use) == std::end(vectorizedOps)) { + createExtraction = true; + break; } } - operandExtractions.erase(operation); - if (lane == 0 && keepFirst) { - continue; + + if (createExtraction) { + + auto const& source = createdVectorOp->getResult(0); + auto const& position = getOrCreateConstant(lane, false); + + auto const& insertionPoint = builder.saveInsertionPoint(); + builder.setInsertionPointAfter(createdVectorOp); + auto element = builder.create(builder.getUnknownLoc(), source, position); + builder.restoreInsertionPoint(insertionPoint); + operation->replaceAllUsesWith(element); + } operation->remove(); } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp index 05c14d19..7a2d98ef 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp @@ -53,12 +53,6 @@ namespace { }); } - bool escapesMultinode(Operation* operation) { - // TODO: check if some intermediate, temporary value of a multinode is used outside of it - assert(operation); - return false; - } - std::vector getOperands(Operation* operation) { std::vector operands; operands.reserve(operation->getNumOperands()); @@ -107,8 +101,8 @@ void SLPGraphBuilder::buildGraph(std::vector const& operations, SLPN } for (size_t i = 0; i < operations.front()->getNumOperands(); ++i) { if (std::all_of(std::begin(allOperands), std::end(allOperands), [&](auto const& operandOperations) { - return operandOperations[i]->getName() == currentOpCode - && !escapesMultinode(operandOperations[i]); + // We don't need multinode-escaping checks since an escaping value can be 'fetched' with vector extractions. + return operandOperations[i]->getName() == currentOpCode; })) { for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { currentNode->addOperationToLane(allOperands[lane][i], lane); diff --git a/mlir/tools/spnc-opt/spnc-opt.cpp b/mlir/tools/spnc-opt/spnc-opt.cpp index fb2edec7..4315f046 100644 --- a/mlir/tools/spnc-opt/spnc-opt.cpp +++ b/mlir/tools/spnc-opt/spnc-opt.cpp @@ -99,7 +99,7 @@ int main(int argc, char** argv) { return mlir::spn::createLoSPNNodeVectorizationPass(); }); - mlir::registerPass("slp-vectorize-structure", "Run SLP vectorization of the structure for CPU target", + mlir::registerPass("slp-vectorize", "Run SLP vectorization of the structure for CPU target", []() -> std::unique_ptr { return mlir::spn::createSLPVectorizationPass(); }); From b0ad43c2f8230c8415c60560aa959b3a82067aaa Mon Sep 17 00:00:00 2001 From: CSV Date: Tue, 13 Apr 2021 14:52:13 +0200 Subject: [PATCH 069/385] Transformer uses more appropriate methods. --- .../LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp index 06996f84..5c7375d9 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp @@ -88,7 +88,7 @@ Value SLPFunctionTransformer::transform(SLPNode* node, size_t vectorIndex) { } } else { - size_t initialOperandIndex = vectorIndex % node->getOperands().size(); + size_t initialOperandIndex = vectorIndex % node->numOperands(); size_t operandNodeIndex = initialOperandIndex; SLPNode* operandNode = (i == 0) ? node : node->getOperand(operandNodeIndex); @@ -96,7 +96,7 @@ Value SLPFunctionTransformer::transform(SLPNode* node, size_t vectorIndex) { size_t nextVectorIndex = vectorsDone[operandNode]; while (nextVectorIndex >= operandNode->numVectors()) { - operandNode = node->getOperand(operandNodeIndex % node->getOperands().size()); + operandNode = node->getOperand(operandNodeIndex % node->numOperands()); nextVectorIndex = vectorsDone[operandNode]; // Check if we have looped through all operands already. if (operandNodeIndex++ == initialOperandIndex + node->numOperands()) { @@ -177,6 +177,7 @@ Value SLPFunctionTransformer::applyCreation(SLPNode* node, size_t vectorIndex, O operation->replaceAllUsesWith(element); } + operation->dropAllUses(); operation->remove(); } return createdVectorOp->getResult(0); From 96f6ea3c62d321d6cfbbdd61d3ed028d143661ec Mon Sep 17 00:00:00 2001 From: CSV Date: Wed, 14 Apr 2021 19:20:41 +0200 Subject: [PATCH 070/385] Added SLP pattern attempt. --- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.h | 16 +- .../SLP/SLPVectorizationPatterns.h | 87 ++++++++++ mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt | 1 + .../SLP/SLPFunctionTransformer.cpp | 2 +- .../Vectorization/SLP/SLPGraphBuilder.cpp | 6 +- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp | 58 +++---- .../SLP/SLPVectorizationPass.cpp | 86 +++++++++- .../SLP/SLPVectorizationPatterns.cpp | 159 ++++++++++++++++++ 8 files changed, 366 insertions(+), 49 deletions(-) create mode 100644 mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h create mode 100644 mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h index fa62e340..f492e157 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h @@ -19,7 +19,6 @@ namespace mlir { explicit SLPNode(std::vector const& operations); - void addOperationToLane(Operation* operation, size_t const& lane); Operation* getOperation(size_t lane, size_t index) const; void setOperation(size_t lane, size_t index, Operation* operation); @@ -30,7 +29,11 @@ namespace mlir { size_t numLanes() const; size_t numVectors() const; - std::vector getVector(size_t index) const; + void addVector(std::vector const& vectorOps); + std::vector& getVector(size_t index); + std::vector& getVectorOf(Operation* op); + std::vector>& getVectors(); + SLPNode* addOperand(std::vector const& operations); SLPNode* getOperand(size_t index) const; @@ -40,13 +43,11 @@ namespace mlir { void addNodeInput(Value const& value); Value const& getNodeInput(size_t index) const; - Type getResultType() const; - void dump() const; void dumpGraph() const; friend bool operator==(SLPNode const& lhs, SLPNode const& rhs) { - return std::tie(lhs.lanes) == std::tie(rhs.lanes); + return std::tie(lhs.vectors) == std::tie(rhs.vectors); } friend bool operator!=(SLPNode const& lhs, SLPNode const& rhs) { @@ -55,10 +56,7 @@ namespace mlir { private: - /// Stores lanes as lists of operations. An inner vector (i.e. a lane) only contains more than one operation - /// if this node is a multinode. Operations with smaller indices inside a lane are executed "after" the higher - /// ones in the source code. - std::vector> lanes; + std::vector> vectors; std::vector> operandNodes; diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h new file mode 100644 index 00000000..1e3f75ce --- /dev/null +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h @@ -0,0 +1,87 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPVECTORIZATIONPATTERNS_H +#define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPVECTORIZATIONPATTERNS_H + +#include "mlir/IR/PatternMatch.h" +#include "SLPNode.h" +#include "LoSPN/LoSPNDialect.h" +#include "LoSPN/LoSPNOps.h" +#include "llvm/Support/Debug.h" + +namespace mlir { + namespace spn { + namespace low { + namespace slp { + + template + class SLPVectorizationPattern : public OpRewritePattern { + + public: + SLPVectorizationPattern(MLIRContext* context, + PatternBenefit benefit, + llvm::DenseMap const& parentNodes) : OpRewritePattern{ + context, benefit}, parentNodes{parentNodes} {} + + protected: + llvm::DenseMap const& parentNodes; + }; + + struct VectorizeBatchRead : public SLPVectorizationPattern { + + using SLPVectorizationPattern::SLPVectorizationPattern; + + LogicalResult matchAndRewrite(SPNBatchRead op, PatternRewriter& rewriter) const override; + }; + + struct VectorizeMul : public SLPVectorizationPattern { + + using SLPVectorizationPattern::SLPVectorizationPattern; + + LogicalResult matchAndRewrite(SPNMul op, PatternRewriter& rewriter) const override; + }; + + struct VectorizeAdd : public SLPVectorizationPattern { + + using SLPVectorizationPattern::SLPVectorizationPattern; + + LogicalResult matchAndRewrite(SPNAdd op, PatternRewriter& rewriter) const override; + }; + + struct VectorizeLog : public SLPVectorizationPattern { + + using SLPVectorizationPattern::SLPVectorizationPattern; + + LogicalResult matchAndRewrite(SPNLog op, PatternRewriter& rewriter) const override; + }; + + struct VectorizeConstant : public SLPVectorizationPattern { + + using SLPVectorizationPattern::SLPVectorizationPattern; + + VectorizeConstant(MLIRContext* context, + PatternBenefit benefit, + const DenseMap& parentNodes) : SLPVectorizationPattern{context, benefit, parentNodes} { + llvm::dbgs() << "Created vectorize constants pattern for SLP vectorization" << "\n"; + } + + LogicalResult matchAndRewrite(SPNConstant op, PatternRewriter& rewriter) const override; + }; + + static void populateSLPVectorizationPatterns(OwningRewritePatternList& patterns, + MLIRContext* context, + llvm::DenseMap const& parentNodes) { + patterns.insert(context, 2, parentNodes); + //patterns.insert(context, 2, parentNodes); + //patterns.insert(context, 2, parentNodes); + } + + } + } + } +} + +#endif //SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPVECTORIZATIONPATTERNS_H diff --git a/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt b/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt index 48648422..a500b060 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt +++ b/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt @@ -11,6 +11,7 @@ add_mlir_conversion_library(MLIRLoSPNtoCPUTransforms Vectorization/SLP/SLPGraphBuilder.cpp Vectorization/SLP/SLPUtil.cpp Vectorization/SLP/SLPVectorizationPass.cpp + Vectorization/SLP/SLPVectorizationPatterns.cpp Vectorization/SLP/SLPFunctionTransformer.cpp LoSPNtoCPUConversionPasses.cpp diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp index 5c7375d9..bd17ba46 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp @@ -29,7 +29,7 @@ Value SLPFunctionTransformer::transform(SLPNode* node, size_t vectorIndex) { vectorsDone[node]++; - auto vectorType = typeConverter.convertType(node->getResultType()); + auto vectorType = typeConverter.convertType(node->getOperation(0, 0)->getResultTypes().front()); auto const& vectorOps = node->getVector(vectorIndex); vectorizedOps.insert(vectorOps.begin(), vectorOps.end()); diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp index 7a2d98ef..3f51240d 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp @@ -104,10 +104,12 @@ void SLPGraphBuilder::buildGraph(std::vector const& operations, SLPN // We don't need multinode-escaping checks since an escaping value can be 'fetched' with vector extractions. return operandOperations[i]->getName() == currentOpCode; })) { + std::vector vectorOps; for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { - currentNode->addOperationToLane(allOperands[lane][i], lane); + vectorOps.emplace_back(allOperands[lane][i]); } - buildGraph(currentNode->getVector(currentNode->numVectors() - 1), currentNode); + currentNode->addVector(vectorOps); + buildGraph(vectorOps, currentNode); } else { // TODO: here might be a good place to implement variable vector width std::vector operandOperations; diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp index f70babbc..a81cae74 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp @@ -13,24 +13,19 @@ using namespace mlir; using namespace mlir::spn; using namespace mlir::spn::low::slp; -SLPNode::SLPNode(std::vector const& operations) : lanes{operations.size()}, operandNodes{}, nodeInputs{} { - for (size_t i = 0; i < operations.size(); ++i) { - lanes[i].emplace_back(operations[i]); - } -} - -void SLPNode::addOperationToLane(Operation* operation, size_t const& lane) { - lanes[lane].emplace_back(operation); +SLPNode::SLPNode(std::vector const& operations) { + assert(!operations.empty()); + vectors.emplace_back(operations); } Operation* SLPNode::getOperation(size_t lane, size_t index) const { assert(lane <= numLanes() && index <= numVectors()); - return lanes[lane][index]; + return vectors[index][lane]; } void SLPNode::setOperation(size_t lane, size_t index, Operation* operation) { assert(lane <= numLanes() && index <= numVectors()); - lanes[lane][index] = operation; + vectors[index][lane] = operation; } bool SLPNode::isMultiNode() const { @@ -38,35 +33,44 @@ bool SLPNode::isMultiNode() const { } bool SLPNode::isUniform() const { - return std::all_of(std::begin(lanes), std::end(lanes), [&](auto const& operations) { - return operations[0]->getName() == lanes[0][0]->getName(); + return std::all_of(std::begin(vectors), std::end(vectors), [&](auto const& operations) { + return operations[0]->getName() == vectors[0][0]->getName(); }); } bool SLPNode::areRootOfNode(std::vector const& operations) const { - for (size_t lane = 0; lane < numLanes(); ++lane) { - if (operations[lane] != lanes[lane].front()) { - return false; - } - } - return true; + return vectors[0] == operations; } size_t SLPNode::numLanes() const { - return lanes.size(); + return vectors[0].size(); } size_t SLPNode::numVectors() const { - return lanes.front().size(); + return vectors.size(); +} + +void SLPNode::addVector(std::vector const& vectorOps) { + assert(vectorOps.size() == numLanes()); + vectors.emplace_back(vectorOps); } -std::vector SLPNode::getVector(size_t index) const { +std::vector& SLPNode::getVector(size_t index) { assert(index <= numVectors()); - std::vector vector; - for (size_t lane = 0; lane < numLanes(); ++lane) { - vector.emplace_back(lanes[lane][index]); + return vectors[index]; +} + +std::vector& SLPNode::getVectorOf(Operation* op) { + for(auto& vector : vectors) { + if(std::find(std::begin(vector), std::end(vector), op) != std::end(vector)) { + return vector; + } } - return vector; + assert(false && "node does not contain the given operation"); +} + +std::vector>& SLPNode::getVectors() { + return vectors; } SLPNode* SLPNode::addOperand(std::vector const& operations) { @@ -100,10 +104,6 @@ Value const& SLPNode::getNodeInput(size_t index) const { return nodeInputs[index]; } -Type SLPNode::getResultType() const { - return lanes[0][0]->getResult(0).getType(); -} - void SLPNode::dump() const { for (size_t i = numVectors(); i-- > 0;) { for (size_t lane = 0; lane < numLanes(); ++lane) { diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp index 1ee442fa..f125de8b 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp @@ -7,13 +7,13 @@ #include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h" -#include "LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.h" +#include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h" #include "LoSPNtoCPU/Vectorization/TargetInformation.h" -#include "LoSPNtoCPU/LoSPNtoCPUTypeConverter.h" -#include "LoSPNtoCPU/Vectorization/LoSPNVectorizationTypeConverter.h" -#include "mlir/Dialect/SCF/SCF.h" -#include "mlir/Dialect/Vector/VectorOps.h" +#include "mlir/Dialect/Linalg/IR/LinalgOps.h" #include "mlir/Dialect/StandardOps/IR/Ops.h" +#include "mlir/Rewrite/PatternApplicator.h" +#include "mlir/Transforms/GreedyPatternRewriteDriver.h" +#include using namespace mlir::spn::low::slp; @@ -73,7 +73,54 @@ namespace { llvm::dbgs() << "}\n"; } -} + llvm::DenseMap computeParentNodesMapping(SLPNode* root) { + llvm::DenseMap parentMapping; + std::queue worklist; + worklist.emplace(root); + while (!worklist.empty()) { + auto* node = worklist.front(); + worklist.pop(); + for(auto& vector : node->getVectors()) { + for(auto* op : vector) { + parentMapping[op] = node; + } + } + for (auto const& operand : node->getOperands()) { + worklist.emplace(operand); + } + } + return parentMapping; + } + + /// Define a custom PatternRewriter for use by the driver. + class MyPatternRewriter : public mlir::PatternRewriter { + public: + MyPatternRewriter(mlir::MLIRContext *ctx) : PatternRewriter(ctx) {} + + /// Override the necessary PatternRewriter hooks here. + }; + +/// Apply the custom driver to `op`. + void applyMyPatternDriver(mlir::Operation *op, + mlir::FrozenRewritePatternList const& patterns) { + // Initialize the custom PatternRewriter. + MyPatternRewriter rewriter(op->getContext()); + + // Create the applicator and apply our cost model. + mlir::PatternApplicator applicator(patterns); + applicator.applyDefaultCostModel(); + + //auto resulttest = patterns.getNativePatterns().begin()->matchAndRewrite(op, rewriter); + + // Try to match and apply a pattern. + mlir::LogicalResult result = applicator.matchAndRewrite(op, rewriter); + if (failed(result)) { + // ... No patterns were applied. + } + // ... A pattern was successfully applied. + } + +} // End anonymous namespace. void SLPVectorizationPass::runOnOperation() { llvm::StringRef funcName = getOperation().getName(); @@ -93,8 +140,31 @@ void SLPVectorizationPass::runOnOperation() { //printSeedTree(seeds.front()); SLPGraphBuilder builder{3}; auto graph = builder.build(seeds.front()); - SLPFunctionTransformer transformer(std::move(graph), function); - transformer.transform(); + //SLPFunctionTransformer transformer(std::move(graph), function); + //transformer.transform(); + // ==== // + + OwningRewritePatternList patterns; + auto const& parentMapping = computeParentNodesMapping(graph.get()); + populateSLPVectorizationPatterns(patterns, &getContext(), parentMapping); + FrozenRewritePatternList frozenPatterns(std::move(patterns)); + for(auto const& entry : parentMapping) { + if(dyn_cast(entry.first)) { + llvm::dbgs() << "constant op: " << entry.first << "\n"; + entry.second->dump(); + //applyMyPatternDriver(entry.first, frozenPatterns); + } + entry.first->dump(); + bool erased; + auto success = applyOpPatternsAndFold(entry.first, frozenPatterns, &erased); + + assert(success.succeeded()); + + if (success.failed()) { + signalPassFailure(); + } + } + getOperation()->dump(); } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp new file mode 100644 index 00000000..5dba8498 --- /dev/null +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp @@ -0,0 +1,159 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#include "mlir/IR/BlockAndValueMapping.h" +#include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h" +#include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" +#include "LoSPNtoCPU/Vectorization/TargetInformation.h" +#include "llvm/Support/FormatVariadic.h" +#include "mlir/Dialect/StandardOps/IR/Ops.h" +#include "mlir/Dialect/SCF/SCF.h" +#include "mlir/Dialect/Math/IR/Math.h" +#include "mlir/IR/BuiltinOps.h" +#include "mlir/Dialect/Vector/VectorOps.h" +#include "LoSPN/LoSPNAttributes.h" + +using namespace mlir; +using namespace mlir::spn; +using namespace mlir::spn::low; +using namespace mlir::spn::low::slp; + +// Helper functions in anonymous namespace. +namespace { + + void broadcastIdentical(Value const& source, Type const& vectorType, PatternRewriter& rewriter) { + auto const& insertionPoint = rewriter.saveInsertionPoint(); + rewriter.setInsertionPointAfterValue(source); + rewriter.create(source.getLoc(), vectorType, source); + rewriter.restoreInsertionPoint(insertionPoint); + } + + template + Value broadcastFirstInsertRest(V const& vector, VectorType const& vectorType, PatternRewriter& rewriter) { + llvm::SmallVector elements; + for(size_t i = 0; i < vector.size(); ++i) { + elements.template emplace_back(vector[i]->getResult(0)); + } + auto const& insertionPoint = rewriter.saveInsertionPoint(); + rewriter.setInsertionPointAfterValue(elements[0]); + Value vectorOp = rewriter.create(vector[0]->getLoc(), vectorType, elements[0]); + for (size_t i = 1; i < vector.size(); ++i) { + auto const& position = i/*getOrCreateConstant(i, false)*/; + vectorOp = rewriter.create(vector[0]->getLoc(), elements[i], vectorOp, position); + } + rewriter.restoreInsertionPoint(insertionPoint); + return vectorOp; + } +} + +LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead op, PatternRewriter& rewriter) const { + + llvm::dbgs() << "Matched SPNBatchRead" << "\n"; + + auto* node = parentNodes.lookup(op.getOperation()); + auto const& vector = node->getVectorOf(op.getOperation()); + auto const& vectorType = VectorType::get(vector.size(), op.getType()); + + if(!node->isUniform()) { + //broadcastFirstInsertRest(vector, vectorType, rewriter); + } + //assert(false); + return failure(); +} + +LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd op, PatternRewriter& rewriter) const { + + llvm::dbgs() << "Matched SPNAdd" << "\n"; + + auto* node = parentNodes.lookup(op.getOperation()); + auto const& vector = node->getVectorOf(op.getOperation()); + auto const& vectorType = VectorType::get(vector.size(), op.getType()); + + if(!node->isUniform()) { + //broadcastFirstInsertRest(vector, vectorType, rewriter); + } + assert(false); + return success(); +} + +LogicalResult VectorizeMul::matchAndRewrite(SPNMul op, PatternRewriter& rewriter) const { + + llvm::dbgs() << "Matched SPNMul" << "\n"; + + auto* node = parentNodes.lookup(op.getOperation()); + auto const& vector = node->getVectorOf(op.getOperation()); + auto const& vectorType = VectorType::get(vector.size(), op.getType()); + + if(!node->isUniform()) { + //broadcastFirstInsertRest(vector, vectorType, rewriter); + } + assert(false); + return success(); +} + +LogicalResult VectorizeLog::matchAndRewrite(SPNLog op, PatternRewriter& rewriter) const { + + llvm::dbgs() << "Matched SPNLog" << "\n"; + + auto* node = parentNodes.lookup(op.getOperation()); + auto const& vector = node->getVectorOf(op.getOperation()); + auto const& vectorType = VectorType::get(vector.size(), op.getType()); + + if(!node->isUniform()) { + //broadcastFirstInsertRest(vector, vectorType, rewriter); + } + assert(false); + return success(); +} + + +LogicalResult VectorizeConstant::matchAndRewrite(SPNConstant op, PatternRewriter& rewriter) const { + + llvm::dbgs() << "Matched SPNConstant" << "\n"; + + assert(false); + /* + auto* node = parentNodes.lookup(op.getOperation()); + auto const& vector = node->getVectorOf(op.getOperation()); + auto const& vectorType = VectorType::get(vector.size(), op.type()); + + if(!node->isUniform()) { + broadcastFirstInsertRest(vector, vectorType, rewriter); + } + + if (node->isUniform()) { + + DenseElementsAttr constAttr; + + if (vectorType.getElementType().cast().getWidth() == 32) { + llvm::SmallVector array; + for (int i = 0; i < vectorType.getNumElements(); ++i) { + array.push_back(static_cast(vector[i]).value().convertToFloat()); + } + constAttr = mlir::DenseElementsAttr::get(vectorType, (llvm::ArrayRef) array); + } else { + llvm::SmallVector array; + for (int i = 0; i < vectorType.getNumElements(); ++i) { + array.push_back(static_cast(vector[i]).value().convertToDouble()); + } + constAttr = mlir::DenseElementsAttr::get(vectorType, (llvm::ArrayRef) array); + } + + auto const& insertionPoint = rewriter.saveInsertionPoint(); + rewriter.setInsertionPointAfter(vector.front()); + auto constValue = rewriter.create(op->getLoc(), constAttr); + rewriter.restoreInsertionPoint(insertionPoint); + + for(auto lane : vector) { + for(auto& use : lane->getUses()) { + if(parentNodes.lookup(use.getOwner())) { + use.set(constValue); + } + } + } + } + */ + return success(); +} From 014281c19c871b0b1f889df30f5db2dff27b0d36 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Wed, 14 Apr 2021 21:49:33 +0200 Subject: [PATCH 071/385] Better operation mode recognition. --- .../Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.h | 2 +- mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp | 5 ++--- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.h index 421ecd5b..1d5d52a6 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.h @@ -26,7 +26,7 @@ namespace mlir { FAILED }; - Mode modeFromOperation(Operation const* operation); + Mode modeFromOperation(Operation* operation); } } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp index 980c0fde..810b4207 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp @@ -3,15 +3,14 @@ // Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. // -#include "mlir/Dialect/StandardOps/IR/Ops.h" #include "LoSPN/LoSPNOps.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPMode.h" using namespace mlir; using namespace mlir::spn::low::slp; -Mode mlir::spn::low::slp::modeFromOperation(const Operation* operation) { - if (dyn_cast(operation)) { +Mode mlir::spn::low::slp::modeFromOperation(Operation* operation) { + if (operation->hasTrait()) { return CONST; } else if (dyn_cast(operation)) { return LOAD; From 50e3fecf77ef07684f5ca46072af6d5a59fb45ea Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Wed, 14 Apr 2021 21:50:16 +0200 Subject: [PATCH 072/385] Updated consecutiveness check. --- .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.h | 3 +- .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp | 32 ++++++++----------- 2 files changed, 15 insertions(+), 20 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h index 3088f744..a10ff29f 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h @@ -7,13 +7,14 @@ #define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPUTIL_H #include "mlir/IR/Operation.h" +#include "LoSPN/LoSPNOps.h" namespace mlir { namespace spn { namespace low { namespace slp { - bool areConsecutiveLoads(Operation* load1, Operation* load2); + bool areConsecutiveLoads(low::SPNBatchRead load1, low::SPNBatchRead load2); bool areConsecutiveLoads(std::vector const& loads); bool canBeGathered(std::vector const& loads); bool areBroadcastable(std::vector const& ops); diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp index 651c1e5d..c12d1f68 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp @@ -4,38 +4,32 @@ // #include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" -#include "LoSPN/LoSPNOps.h" using namespace mlir; -bool mlir::spn::low::slp::areConsecutiveLoads(Operation* op1, Operation* op2) { - - if (op1 == op2) { - return false; - } - - auto loadOp1 = dyn_cast(op1); - auto loadOp2 = dyn_cast(op2); - - if (!loadOp1 || !loadOp2) { +bool mlir::spn::low::slp::areConsecutiveLoads(low::SPNBatchRead load1, low::SPNBatchRead load2) { + if (load1 == load2) { return false; } - - if (loadOp1.batchMem() != loadOp2.batchMem()) { + if (load1.batchMem() != load2.batchMem()) { return false; } - - if (loadOp1.batchIndex() != loadOp2.batchIndex()) { + if (load1.batchIndex() != load2.batchIndex()) { return false; } - - return loadOp1.sampleIndex() + 1 == loadOp2.sampleIndex(); + return load1.sampleIndex() + 1 == load2.sampleIndex(); } bool mlir::spn::low::slp::areConsecutiveLoads(std::vector const& loads) { for (size_t i = 0; i < loads.size() - 1; ++i) { - auto* loadOp1 = loads[i]; - auto* loadOp2 = loads[i + 1]; + auto loadOp1 = dyn_cast(loads[i]); + if (!loadOp1) { + return false; + } + auto loadOp2 = dyn_cast(loads[i + 1]); + if (!loadOp2) { + return false; + } if (!areConsecutiveLoads(loadOp1, loadOp2)) { return false; } From d0de069d27ea5fad25cfff16b3e1b4f11a038c1f Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Wed, 14 Apr 2021 21:51:03 +0200 Subject: [PATCH 073/385] Fix: SLPGraphBuilder not recognizing LoadOps as such. --- .../LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp index 3f51240d..63a5b00d 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp @@ -7,6 +7,7 @@ #include "llvm/Support/Debug.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" +#include "LoSPN/LoSPNOps.h" #include "LoSPN/LoSPNTraits.h" #include @@ -155,10 +156,6 @@ void SLPGraphBuilder::buildGraph(std::vector const& operations, SLPN } void SLPGraphBuilder::reorderOperands(SLPNode* multinode) const { - if (std::uintptr_t(multinode->getOperation(0, 0)) == 12345) { - multinode->dump(); - llvm::dbgs() << "\n"; - } auto const& numOperands = multinode->numOperands(); std::vector> finalOrder{multinode->numLanes()}; std::vector> mode{multinode->numLanes()}; @@ -233,7 +230,8 @@ std::pair SLPGraphBuilder::getBest(Mode const& mode, best = candidates.front(); for (auto& candidate : candidates) { if (mode == LOAD) { - if (areConsecutiveLoads(last, candidate)) { + auto load = dyn_cast(candidate); + if (load && areConsecutiveLoads(static_cast(last), load)) { bestCandidates.emplace_back(candidate); } } else if (candidate->getName() == last->getName()) { @@ -287,8 +285,8 @@ int SLPGraphBuilder::getLookAheadScore(Operation* last, Operation* candidate, si if (last->getName() != candidate->getName()) { return 0; } - if (dyn_cast(last)) { - return areConsecutiveLoads(last, candidate) ? 1 : 0; + if (dyn_cast(last)) { + return areConsecutiveLoads(static_cast(last), static_cast(candidate)) ? 1 : 0; } return 1; } From e568b41efca0ad7bc84f675a3be4dd87dfff6c56 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Fri, 16 Apr 2021 15:56:42 +0200 Subject: [PATCH 074/385] Added contains method to SLPNodes. --- .../Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h | 2 +- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp | 10 ++++++++-- 2 files changed, 9 insertions(+), 3 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h index f492e157..83a4c892 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h @@ -24,6 +24,7 @@ namespace mlir { bool isMultiNode() const; bool isUniform() const; + bool containsOperation(Operation* op) const; bool areRootOfNode(std::vector const& operations) const; size_t numLanes() const; @@ -34,7 +35,6 @@ namespace mlir { std::vector& getVectorOf(Operation* op); std::vector>& getVectors(); - SLPNode* addOperand(std::vector const& operations); SLPNode* getOperand(size_t index) const; std::vector getOperands() const; diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp index a81cae74..eaac8285 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp @@ -38,6 +38,12 @@ bool SLPNode::isUniform() const { }); } +bool SLPNode::containsOperation(Operation* op) const { + return std::any_of(std::begin(vectors), std::end(vectors), [&](auto const& vectorOps) { + return std::find(std::begin(vectorOps), std::end(vectorOps), op) != std::end(vectorOps); + }); +} + bool SLPNode::areRootOfNode(std::vector const& operations) const { return vectors[0] == operations; } @@ -61,8 +67,8 @@ std::vector& SLPNode::getVector(size_t index) { } std::vector& SLPNode::getVectorOf(Operation* op) { - for(auto& vector : vectors) { - if(std::find(std::begin(vector), std::end(vector), op) != std::end(vector)) { + for (auto& vector : vectors) { + if (std::find(std::begin(vector), std::end(vector), op) != std::end(vector)) { return vector; } } From c84f185417558d2b7bd29e2d5454d08c71f85c93 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Fri, 16 Apr 2021 15:57:17 +0200 Subject: [PATCH 075/385] Added escapesMultinode check, better SPLAT checks. --- .../Vectorization/SLP/SLPGraphBuilder.cpp | 24 +++++++++++-------- 1 file changed, 14 insertions(+), 10 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp index 63a5b00d..1fdc8bd5 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp @@ -37,7 +37,7 @@ namespace { if (op->getNumResults() != 1) { return false; } - // MLIR disallows index types in vectors. + // MLIR does not allow index types in vectors. if (op->getResult(0).getType().isa()) { return false; } @@ -46,11 +46,16 @@ namespace { } bool commutative(std::vector const& operations) { - return std::any_of(std::begin(operations), std::end(operations), [&](Operation* op) { - if (!op->hasTrait()) { - return false; - } - return true; + return std::all_of(std::begin(operations), std::end(operations), [&](Operation* op) { + return op->hasTrait(); + }); + } + + bool escapesMultinode(Operation* op, SLPNode* currentNode) { + // TODO: determine if multinodes should stop building if an operation escapes it + // or if simply disallowing reordering in this lane might be better + return std::any_of(std::begin(op->getUsers()), std::end(op->getUsers()), [&](auto* user) { + return !currentNode->containsOperation(user); }); } @@ -102,8 +107,7 @@ void SLPGraphBuilder::buildGraph(std::vector const& operations, SLPN } for (size_t i = 0; i < operations.front()->getNumOperands(); ++i) { if (std::all_of(std::begin(allOperands), std::end(allOperands), [&](auto const& operandOperations) { - // We don't need multinode-escaping checks since an escaping value can be 'fetched' with vector extractions. - return operandOperations[i]->getName() == currentOpCode; + return operandOperations[i]->getName() == currentOpCode && !escapesMultinode(operandOperations[i], currentNode); })) { std::vector vectorOps; for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { @@ -185,7 +189,7 @@ void SLPGraphBuilder::reorderOperands(SLPNode* multinode) const { // Update output finalOrder[lane].emplace_back(bestResult.first); // Detect SPLAT mode - if (i == 1 && bestResult.first == last) { + if (i == 1 && OperationEquivalence::isEquivalentTo(bestResult.first, last)) { mode[lane][i] = SPLAT; } else { mode[lane].emplace_back(bestResult.second); @@ -220,7 +224,7 @@ std::pair SLPGraphBuilder::getBest(Mode const& mode, } else if (mode == SPLAT) { // Look for other splat candidates for (auto& operand : candidates) { - if (operand == last) { + if (OperationEquivalence::isEquivalentTo(operand, last)) { best = operand; break; } From 80fbe340511956e091daafe66c03982bef1f1aca Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sat, 17 Apr 2021 01:42:42 +0200 Subject: [PATCH 076/385] Added getVectorIndex to SLPNodes. --- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.h | 1 + .../LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp | 12 ++++++++---- 2 files changed, 9 insertions(+), 4 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h index 83a4c892..5b0d451e 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h @@ -32,6 +32,7 @@ namespace mlir { size_t numVectors() const; void addVector(std::vector const& vectorOps); std::vector& getVector(size_t index); + size_t getVectorIndex(Operation* op) const; std::vector& getVectorOf(Operation* op); std::vector>& getVectors(); diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp index eaac8285..e00921fa 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp @@ -66,15 +66,19 @@ std::vector& SLPNode::getVector(size_t index) { return vectors[index]; } -std::vector& SLPNode::getVectorOf(Operation* op) { - for (auto& vector : vectors) { - if (std::find(std::begin(vector), std::end(vector), op) != std::end(vector)) { - return vector; +size_t SLPNode::getVectorIndex(Operation* op) const { + for (size_t i = 0; i < vectors.size(); ++i) { + if (std::find(std::begin(vectors[i]), std::end(vectors[i]), op) != std::end(vectors[i])) { + return i; } } assert(false && "node does not contain the given operation"); } +std::vector& SLPNode::getVectorOf(Operation* op) { + return vectors[getVectorIndex(op)]; +} + std::vector>& SLPNode::getVectors() { return vectors; } From a09fa2f4644bc87a4918cb005e1a7b6e794ec035 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sat, 17 Apr 2021 01:43:19 +0200 Subject: [PATCH 077/385] SLP Vectorization patterns update. --- .../SLP/SLPVectorizationPatterns.h | 26 ++-- .../SLP/SLPVectorizationPass.cpp | 72 +++++----- .../SLP/SLPVectorizationPatterns.cpp | 133 +++++++++--------- 3 files changed, 115 insertions(+), 116 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h index 1e3f75ce..90741469 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h @@ -6,10 +6,11 @@ #ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPVECTORIZATIONPATTERNS_H #define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPVECTORIZATIONPATTERNS_H -#include "mlir/IR/PatternMatch.h" #include "SLPNode.h" #include "LoSPN/LoSPNDialect.h" #include "LoSPN/LoSPNOps.h" +#include "mlir/Dialect/StandardOps/IR/Ops.h" +#include "mlir/IR/PatternMatch.h" #include "llvm/Support/Debug.h" namespace mlir { @@ -23,11 +24,14 @@ namespace mlir { public: SLPVectorizationPattern(MLIRContext* context, PatternBenefit benefit, - llvm::DenseMap const& parentNodes) : OpRewritePattern{ - context, benefit}, parentNodes{parentNodes} {} + llvm::DenseMap const& parentNodes, + llvm::DenseMap>& vectorsByNode) + : OpRewritePattern{ + context, benefit}, parentNodes{parentNodes}, vectorsByNode{vectorsByNode} {} protected: llvm::DenseMap const& parentNodes; + llvm::DenseMap>& vectorsByNode; }; struct VectorizeBatchRead : public SLPVectorizationPattern { @@ -62,21 +66,17 @@ namespace mlir { using SLPVectorizationPattern::SLPVectorizationPattern; - VectorizeConstant(MLIRContext* context, - PatternBenefit benefit, - const DenseMap& parentNodes) : SLPVectorizationPattern{context, benefit, parentNodes} { - llvm::dbgs() << "Created vectorize constants pattern for SLP vectorization" << "\n"; - } - LogicalResult matchAndRewrite(SPNConstant op, PatternRewriter& rewriter) const override; }; static void populateSLPVectorizationPatterns(OwningRewritePatternList& patterns, MLIRContext* context, - llvm::DenseMap const& parentNodes) { - patterns.insert(context, 2, parentNodes); - //patterns.insert(context, 2, parentNodes); - //patterns.insert(context, 2, parentNodes); + llvm::DenseMap const& parentNodes, + llvm::DenseMap>& vectorsByNode) { + patterns.insert(context, 2, parentNodes, vectorsByNode); + patterns.insert(context, 2, parentNodes, vectorsByNode); + patterns.insert(context, 2, parentNodes, vectorsByNode); } } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp index f125de8b..61915853 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp @@ -80,8 +80,8 @@ namespace { while (!worklist.empty()) { auto* node = worklist.front(); worklist.pop(); - for(auto& vector : node->getVectors()) { - for(auto* op : vector) { + for (auto& vector : node->getVectors()) { + for (auto* op : vector) { parentMapping[op] = node; } } @@ -92,32 +92,29 @@ namespace { return parentMapping; } - /// Define a custom PatternRewriter for use by the driver. - class MyPatternRewriter : public mlir::PatternRewriter { - public: - MyPatternRewriter(mlir::MLIRContext *ctx) : PatternRewriter(ctx) {} + llvm::SmallVector postOrder(SLPNode* root) { + llvm::SmallVector order; + for (auto* operand : root->getOperands()) { + order.append(postOrder(operand)); + } + order.emplace_back(root); + return order; + } - /// Override the necessary PatternRewriter hooks here. + /// A custom PatternRewriter that does not fold operations. + class NoFoldPatternRewriter : public mlir::PatternRewriter { + public: + explicit NoFoldPatternRewriter(mlir::MLIRContext* ctx) : PatternRewriter(ctx) {} }; -/// Apply the custom driver to `op`. - void applyMyPatternDriver(mlir::Operation *op, - mlir::FrozenRewritePatternList const& patterns) { - // Initialize the custom PatternRewriter. - MyPatternRewriter rewriter(op->getContext()); - - // Create the applicator and apply our cost model. + void applyNoFoldPatternDriver(mlir::Operation* op, mlir::FrozenRewritePatternList const& patterns) { + NoFoldPatternRewriter rewriter(op->getContext()); mlir::PatternApplicator applicator(patterns); applicator.applyDefaultCostModel(); - - //auto resulttest = patterns.getNativePatterns().begin()->matchAndRewrite(op, rewriter); - - // Try to match and apply a pattern. - mlir::LogicalResult result = applicator.matchAndRewrite(op, rewriter); - if (failed(result)) { - // ... No patterns were applied. + auto result = applicator.matchAndRewrite(op, rewriter); + if (result.failed()) { + op->emitOpError("SLP pattern application failed"); } - // ... A pattern was successfully applied. } } // End anonymous namespace. @@ -137,30 +134,35 @@ void SLPVectorizationPass::runOnOperation() { auto seeds = seedAnalysis.getSeeds(width, seedAnalysis.getOpDepths(), SearchMode::UseBeforeDef); assert(!seeds.empty() && "couldn't find a seed!"); - //printSeedTree(seeds.front()); + // printSeedTree(seeds.front()); SLPGraphBuilder builder{3}; auto graph = builder.build(seeds.front()); + // graph->dumpGraph(); //SLPFunctionTransformer transformer(std::move(graph), function); //transformer.transform(); // ==== // OwningRewritePatternList patterns; auto const& parentMapping = computeParentNodesMapping(graph.get()); - populateSLPVectorizationPatterns(patterns, &getContext(), parentMapping); + llvm::DenseMap> vectorsByNode; + populateSLPVectorizationPatterns(patterns, &getContext(), parentMapping, vectorsByNode); FrozenRewritePatternList frozenPatterns(std::move(patterns)); - for(auto const& entry : parentMapping) { - if(dyn_cast(entry.first)) { - llvm::dbgs() << "constant op: " << entry.first << "\n"; - entry.second->dump(); - //applyMyPatternDriver(entry.first, frozenPatterns); - } - entry.first->dump(); - bool erased; - auto success = applyOpPatternsAndFold(entry.first, frozenPatterns, &erased); - assert(success.succeeded()); + // Traverse the SLP graph in postorder and apply the vectorization patterns. + for (auto* node : postOrder(graph.get())) { + vectorsByNode[node].resize_for_overwrite(node->numVectors()); + for (auto const& vector : node->getVectors()) { + for (auto* op : vector) { + applyNoFoldPatternDriver(op, frozenPatterns); + } + } + } - if (success.failed()) { + // If an SLP node failed to vectorize completely, fail the pass. + for (auto const& entry: vectorsByNode) { + if (entry.first->numVectors() != entry.second.size()) { + llvm::dbgs() << "Failed to vectorize node:\n"; + entry.first->dump(); signalPassFailure(); } } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp index 5dba8498..5a97e7d2 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp @@ -33,7 +33,7 @@ namespace { template Value broadcastFirstInsertRest(V const& vector, VectorType const& vectorType, PatternRewriter& rewriter) { llvm::SmallVector elements; - for(size_t i = 0; i < vector.size(); ++i) { + for (size_t i = 0; i < vector.size(); ++i) { elements.template emplace_back(vector[i]->getResult(0)); } auto const& insertionPoint = rewriter.saveInsertionPoint(); @@ -50,110 +50,107 @@ namespace { LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead op, PatternRewriter& rewriter) const { - llvm::dbgs() << "Matched SPNBatchRead" << "\n"; + auto* node = parentNodes.lookup(op); + node->dump(); - auto* node = parentNodes.lookup(op.getOperation()); - auto const& vector = node->getVectorOf(op.getOperation()); - auto const& vectorType = VectorType::get(vector.size(), op.getType()); + auto const& vectorIndex = node->getVectorIndex(op); + auto const& vector = node->getVector(vectorIndex); + auto const& vectorType = VectorType::get(static_cast(vector.size()), op.getType()); - if(!node->isUniform()) { - //broadcastFirstInsertRest(vector, vectorType, rewriter); + if (!node->isUniform()) { + //vectorsByNode[node][vectorIndex] = broadcastFirstInsertRest(vector, vectorType, rewriter).getDefiningOp(); + return success(); } - //assert(false); - return failure(); + return success(); } LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd op, PatternRewriter& rewriter) const { - llvm::dbgs() << "Matched SPNAdd" << "\n"; + auto* node = parentNodes.lookup(op); + node->dump(); - auto* node = parentNodes.lookup(op.getOperation()); - auto const& vector = node->getVectorOf(op.getOperation()); - auto const& vectorType = VectorType::get(vector.size(), op.getType()); + auto const& vectorIndex = node->getVectorIndex(op); + auto const& vector = node->getVector(vectorIndex); + auto const& vectorType = VectorType::get(static_cast(vector.size()), op.getType()); - if(!node->isUniform()) { - //broadcastFirstInsertRest(vector, vectorType, rewriter); + if (!node->isUniform()) { + //vectorsByNode[node][vectorIndex] = broadcastFirstInsertRest(vector, vectorType, rewriter).getDefiningOp(); + return success(); } - assert(false); return success(); } LogicalResult VectorizeMul::matchAndRewrite(SPNMul op, PatternRewriter& rewriter) const { - llvm::dbgs() << "Matched SPNMul" << "\n"; + auto* node = parentNodes.lookup(op); + node->dump(); - auto* node = parentNodes.lookup(op.getOperation()); - auto const& vector = node->getVectorOf(op.getOperation()); - auto const& vectorType = VectorType::get(vector.size(), op.getType()); + auto const& vectorIndex = node->getVectorIndex(op); + auto const& vector = node->getVector(vectorIndex); + auto const& vectorType = VectorType::get(static_cast(vector.size()), op.getType()); - if(!node->isUniform()) { - //broadcastFirstInsertRest(vector, vectorType, rewriter); + if (!node->isUniform()) { + //vectorsByNode[node][vectorIndex] = broadcastFirstInsertRest(vector, vectorType, rewriter).getDefiningOp(); + return success(); } - assert(false); return success(); } LogicalResult VectorizeLog::matchAndRewrite(SPNLog op, PatternRewriter& rewriter) const { - llvm::dbgs() << "Matched SPNLog" << "\n"; + auto* node = parentNodes.lookup(op); + node->dump(); - auto* node = parentNodes.lookup(op.getOperation()); - auto const& vector = node->getVectorOf(op.getOperation()); - auto const& vectorType = VectorType::get(vector.size(), op.getType()); + auto const& vectorIndex = node->getVectorIndex(op); + auto const& vector = node->getVector(vectorIndex); + auto const& vectorType = VectorType::get(static_cast(vector.size()), op.getType()); - if(!node->isUniform()) { - //broadcastFirstInsertRest(vector, vectorType, rewriter); + if (!node->isUniform()) { + //vectorsByNode[node][vectorIndex] = broadcastFirstInsertRest(vector, vectorType, rewriter).getDefiningOp(); + return success(); } - assert(false); + return success(); } - LogicalResult VectorizeConstant::matchAndRewrite(SPNConstant op, PatternRewriter& rewriter) const { - llvm::dbgs() << "Matched SPNConstant" << "\n"; + auto* node = parentNodes.lookup(op); + node->dump(); - assert(false); - /* - auto* node = parentNodes.lookup(op.getOperation()); - auto const& vector = node->getVectorOf(op.getOperation()); - auto const& vectorType = VectorType::get(vector.size(), op.type()); + auto const& vectorIndex = node->getVectorIndex(op); + auto const& vector = node->getVector(vectorIndex); + auto const& vectorType = VectorType::get(static_cast(vector.size()), op.getType()); - if(!node->isUniform()) { - broadcastFirstInsertRest(vector, vectorType, rewriter); + if (!node->isUniform()) { + vectorsByNode[node][vectorIndex] = broadcastFirstInsertRest(vector, vectorType, rewriter).getDefiningOp(); + return success(); } - if (node->isUniform()) { - - DenseElementsAttr constAttr; - - if (vectorType.getElementType().cast().getWidth() == 32) { - llvm::SmallVector array; - for (int i = 0; i < vectorType.getNumElements(); ++i) { - array.push_back(static_cast(vector[i]).value().convertToFloat()); - } - constAttr = mlir::DenseElementsAttr::get(vectorType, (llvm::ArrayRef) array); - } else { - llvm::SmallVector array; - for (int i = 0; i < vectorType.getNumElements(); ++i) { - array.push_back(static_cast(vector[i]).value().convertToDouble()); - } - constAttr = mlir::DenseElementsAttr::get(vectorType, (llvm::ArrayRef) array); - } - - auto const& insertionPoint = rewriter.saveInsertionPoint(); - rewriter.setInsertionPointAfter(vector.front()); - auto constValue = rewriter.create(op->getLoc(), constAttr); - rewriter.restoreInsertionPoint(insertionPoint); + DenseElementsAttr constAttr; - for(auto lane : vector) { - for(auto& use : lane->getUses()) { - if(parentNodes.lookup(use.getOwner())) { - use.set(constValue); - } - } + if (vectorType.getElementType().cast().getWidth() == 32) { + llvm::SmallVector array; + for (int i = 0; i < vectorType.getNumElements(); ++i) { + array.push_back(static_cast(vector[i]).value().convertToFloat()); + } + constAttr = mlir::DenseElementsAttr::get(vectorType, (llvm::ArrayRef) array); + } else { + llvm::SmallVector array; + for (int i = 0; i < vectorType.getNumElements(); ++i) { + array.push_back(static_cast(vector[i]).value().convertToDouble()); } + constAttr = mlir::DenseElementsAttr::get(vectorType, (llvm::ArrayRef) array); } - */ + + auto const& insertionPoint = rewriter.saveInsertionPoint(); + rewriter.setInsertionPointAfter(vector.front()); + auto constValue = rewriter.create(op->getLoc(), constAttr); + rewriter.restoreInsertionPoint(insertionPoint); + + vectorsByNode[node][vectorIndex] = constValue; + + constValue->dump(); + return success(); } From 1f9c393ed75f5fcf4fdefa2d8495d4f4a37b8438 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sat, 17 Apr 2021 21:05:23 +0200 Subject: [PATCH 078/385] Better insertion point handling. --- .../SLP/SLPVectorizationPatterns.cpp | 119 ++++++++++++------ 1 file changed, 78 insertions(+), 41 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp index 5a97e7d2..70daf001 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp @@ -40,50 +40,115 @@ namespace { rewriter.setInsertionPointAfterValue(elements[0]); Value vectorOp = rewriter.create(vector[0]->getLoc(), vectorType, elements[0]); for (size_t i = 1; i < vector.size(); ++i) { + rewriter.setInsertionPointAfterValue(elements[i]); auto const& position = i/*getOrCreateConstant(i, false)*/; vectorOp = rewriter.create(vector[0]->getLoc(), elements[i], vectorOp, position); } rewriter.restoreInsertionPoint(insertionPoint); return vectorOp; } + + template + Operation* last(V const& vector) { + size_t maxIndex = 0; + for (size_t index = 0; index < vector.size(); ++index) { + if (vector[maxIndex]->isBeforeInBlock(vector[index])) { + maxIndex = index; + } + } + return vector[maxIndex]; + } + + template + Operation* first(V const& vector) { + size_t minIndex = 0; + for (size_t index = 0; index < vector.size(); ++index) { + if (!vector[minIndex]->isBeforeInBlock(vector[index])) { + minIndex = index; + } + } + return vector[minIndex]; + } } -LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead op, PatternRewriter& rewriter) const { +LogicalResult VectorizeConstant::matchAndRewrite(SPNConstant op, PatternRewriter& rewriter) const { auto* node = parentNodes.lookup(op); - node->dump(); auto const& vectorIndex = node->getVectorIndex(op); auto const& vector = node->getVector(vectorIndex); auto const& vectorType = VectorType::get(static_cast(vector.size()), op.getType()); if (!node->isUniform()) { - //vectorsByNode[node][vectorIndex] = broadcastFirstInsertRest(vector, vectorType, rewriter).getDefiningOp(); + vectorsByNode[node][vectorIndex] = broadcastFirstInsertRest(vector, vectorType, rewriter).getDefiningOp(); return success(); } + + DenseElementsAttr constAttr; + + if (vectorType.getElementType().cast().getWidth() == 32) { + llvm::SmallVector array; + for (int i = 0; i < vectorType.getNumElements(); ++i) { + array.push_back(static_cast(vector[i]).value().convertToFloat()); + } + constAttr = mlir::DenseElementsAttr::get(vectorType, static_cast>(array)); + } else { + llvm::SmallVector array; + for (int i = 0; i < vectorType.getNumElements(); ++i) { + array.push_back(static_cast(vector[i]).value().convertToDouble()); + } + constAttr = mlir::DenseElementsAttr::get(vectorType, static_cast>(array)); + } + + rewriter.setInsertionPointAfter(first(vector)); + auto constValue = rewriter.create(op->getLoc(), constAttr); + + vectorsByNode[node][vectorIndex] = constValue; + + constValue->dump(); + return success(); } -LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd op, PatternRewriter& rewriter) const { +LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead op, PatternRewriter& rewriter) const { auto* node = parentNodes.lookup(op); - node->dump(); auto const& vectorIndex = node->getVectorIndex(op); auto const& vector = node->getVector(vectorIndex); auto const& vectorType = VectorType::get(static_cast(vector.size()), op.getType()); - if (!node->isUniform()) { - //vectorsByNode[node][vectorIndex] = broadcastFirstInsertRest(vector, vectorType, rewriter).getDefiningOp(); - return success(); + if (!node->isUniform() || !areConsecutiveLoads(vector)) { + vectorsByNode[node][vectorIndex] = broadcastFirstInsertRest(vector, vectorType, rewriter).getDefiningOp(); + } else { + + rewriter.setInsertionPointAfter(first(vector)); + + auto memIndex = rewriter.create(op->getLoc(), rewriter.getIndexAttr(op.sampleIndex())); + ValueRange indices{op.batchIndex(), memIndex}; + + auto vectorLoad = rewriter.create(op->getLoc(), vectorType, op.batchMem(), indices); + + vectorsByNode[node][vectorIndex] = vectorLoad; + + vectorLoad.dump(); } + + for (auto* batchRead : vector) { + if (std::all_of(std::begin(batchRead->getUsers()), std::end(batchRead->getUsers()), [&](auto* user) { + return parentNodes.count(user); + })) { + batchRead->dropAllUses(); + rewriter.eraseOp(batchRead); + } + } + return success(); } -LogicalResult VectorizeMul::matchAndRewrite(SPNMul op, PatternRewriter& rewriter) const { +LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd op, PatternRewriter& rewriter) const { auto* node = parentNodes.lookup(op); - node->dump(); auto const& vectorIndex = node->getVectorIndex(op); auto const& vector = node->getVector(vectorIndex); @@ -96,10 +161,9 @@ LogicalResult VectorizeMul::matchAndRewrite(SPNMul op, PatternRewriter& rewriter return success(); } -LogicalResult VectorizeLog::matchAndRewrite(SPNLog op, PatternRewriter& rewriter) const { +LogicalResult VectorizeMul::matchAndRewrite(SPNMul op, PatternRewriter& rewriter) const { auto* node = parentNodes.lookup(op); - node->dump(); auto const& vectorIndex = node->getVectorIndex(op); auto const& vector = node->getVector(vectorIndex); @@ -109,48 +173,21 @@ LogicalResult VectorizeLog::matchAndRewrite(SPNLog op, PatternRewriter& rewriter //vectorsByNode[node][vectorIndex] = broadcastFirstInsertRest(vector, vectorType, rewriter).getDefiningOp(); return success(); } - return success(); } -LogicalResult VectorizeConstant::matchAndRewrite(SPNConstant op, PatternRewriter& rewriter) const { +LogicalResult VectorizeLog::matchAndRewrite(SPNLog op, PatternRewriter& rewriter) const { auto* node = parentNodes.lookup(op); - node->dump(); auto const& vectorIndex = node->getVectorIndex(op); auto const& vector = node->getVector(vectorIndex); auto const& vectorType = VectorType::get(static_cast(vector.size()), op.getType()); if (!node->isUniform()) { - vectorsByNode[node][vectorIndex] = broadcastFirstInsertRest(vector, vectorType, rewriter).getDefiningOp(); + //vectorsByNode[node][vectorIndex] = broadcastFirstInsertRest(vector, vectorType, rewriter).getDefiningOp(); return success(); } - DenseElementsAttr constAttr; - - if (vectorType.getElementType().cast().getWidth() == 32) { - llvm::SmallVector array; - for (int i = 0; i < vectorType.getNumElements(); ++i) { - array.push_back(static_cast(vector[i]).value().convertToFloat()); - } - constAttr = mlir::DenseElementsAttr::get(vectorType, (llvm::ArrayRef) array); - } else { - llvm::SmallVector array; - for (int i = 0; i < vectorType.getNumElements(); ++i) { - array.push_back(static_cast(vector[i]).value().convertToDouble()); - } - constAttr = mlir::DenseElementsAttr::get(vectorType, (llvm::ArrayRef) array); - } - - auto const& insertionPoint = rewriter.saveInsertionPoint(); - rewriter.setInsertionPointAfter(vector.front()); - auto constValue = rewriter.create(op->getLoc(), constAttr); - rewriter.restoreInsertionPoint(insertionPoint); - - vectorsByNode[node][vectorIndex] = constValue; - - constValue->dump(); - return success(); } From ee36a90b7edf6f2c16a048d47d1f6fc64aa43dfd Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sat, 17 Apr 2021 21:05:39 +0200 Subject: [PATCH 079/385] Cleaner pattern application. --- .../SLP/SLPVectorizationPass.cpp | 23 ++++++++----------- 1 file changed, 9 insertions(+), 14 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp index 61915853..6949188c 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp @@ -107,16 +107,6 @@ namespace { explicit NoFoldPatternRewriter(mlir::MLIRContext* ctx) : PatternRewriter(ctx) {} }; - void applyNoFoldPatternDriver(mlir::Operation* op, mlir::FrozenRewritePatternList const& patterns) { - NoFoldPatternRewriter rewriter(op->getContext()); - mlir::PatternApplicator applicator(patterns); - applicator.applyDefaultCostModel(); - auto result = applicator.matchAndRewrite(op, rewriter); - if (result.failed()) { - op->emitOpError("SLP pattern application failed"); - } - } - } // End anonymous namespace. void SLPVectorizationPass::runOnOperation() { @@ -149,15 +139,22 @@ void SLPVectorizationPass::runOnOperation() { FrozenRewritePatternList frozenPatterns(std::move(patterns)); // Traverse the SLP graph in postorder and apply the vectorization patterns. + NoFoldPatternRewriter rewriter(&getContext()); + PatternApplicator applicator(frozenPatterns); + applicator.applyDefaultCostModel(); for (auto* node : postOrder(graph.get())) { + node->dump(); vectorsByNode[node].resize_for_overwrite(node->numVectors()); for (auto const& vector : node->getVectors()) { - for (auto* op : vector) { - applyNoFoldPatternDriver(op, frozenPatterns); + auto result = applicator.matchAndRewrite(vector.front(), rewriter); + if (result.failed()) { + vector.front()->emitOpError("SLP pattern application failed"); } } } + getOperation()->dump(); + // If an SLP node failed to vectorize completely, fail the pass. for (auto const& entry: vectorsByNode) { if (entry.first->numVectors() != entry.second.size()) { @@ -166,8 +163,6 @@ void SLPVectorizationPass::runOnOperation() { signalPassFailure(); } } - - getOperation()->dump(); } std::unique_ptr mlir::spn::createSLPVectorizationPass() { From fcc42640804bcf1ee0ff29049e0a9395f855f159 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 18 Apr 2021 02:29:21 +0200 Subject: [PATCH 080/385] Added binary patterns, refactored utility methods. --- .../SLP/SLPVectorizationPatterns.cpp | 184 +++++++++++++----- 1 file changed, 139 insertions(+), 45 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp index 70daf001..2d1f5b68 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp @@ -30,45 +30,83 @@ namespace { rewriter.restoreInsertionPoint(insertionPoint); } - template - Value broadcastFirstInsertRest(V const& vector, VectorType const& vectorType, PatternRewriter& rewriter) { - llvm::SmallVector elements; - for (size_t i = 0; i < vector.size(); ++i) { - elements.template emplace_back(vector[i]->getResult(0)); - } + template + Value broadcastFirstInsertRest(ValueIterator begin, + ValueIterator end, + VectorType const& vectorType, + PatternRewriter& rewriter) { auto const& insertionPoint = rewriter.saveInsertionPoint(); - rewriter.setInsertionPointAfterValue(elements[0]); - Value vectorOp = rewriter.create(vector[0]->getLoc(), vectorType, elements[0]); - for (size_t i = 1; i < vector.size(); ++i) { - rewriter.setInsertionPointAfterValue(elements[i]); - auto const& position = i/*getOrCreateConstant(i, false)*/; - vectorOp = rewriter.create(vector[0]->getLoc(), elements[i], vectorOp, position); + rewriter.setInsertionPointAfterValue(*begin); + Value vectorOp = rewriter.create(begin->getLoc(), vectorType, *begin); + unsigned position = 1; + while (++begin != end) { + rewriter.setInsertionPointAfterValue(*begin); + vectorOp = rewriter.create(begin->getLoc(), *begin, vectorOp, position++); } rewriter.restoreInsertionPoint(insertionPoint); return vectorOp; } - template - Operation* last(V const& vector) { - size_t maxIndex = 0; - for (size_t index = 0; index < vector.size(); ++index) { - if (vector[maxIndex]->isBeforeInBlock(vector[index])) { - maxIndex = index; + template + llvm::SmallVector results(OperationIterator begin, OperationIterator end) { + llvm::SmallVector elements; + while (begin != end) { + elements.emplace_back((*begin)->getResult(0)); + ++begin; + } + return elements; + } + + template + Operation* firstOperation(OperationIterator begin, OperationIterator end) { + Operation* firstOp = *begin; + while (++begin != end) { + if (!firstOp->isBeforeInBlock(*begin)) { + firstOp = *begin; + } + } + return firstOp; + } + + template + Value firstValue(ValueIterator begin, ValueIterator end) { + Value firstVal = *begin; + while (begin != end) { + if (begin->template isa()) { + return *begin; + } + if (!firstVal.getDefiningOp()->isBeforeInBlock(*begin)) { + firstVal = *begin; + } + ++begin; + } + return firstVal; + } + + template + Operation* lastOperation(OperationIterator begin, OperationIterator end) { + Operation* lastOp = *begin; + while (++begin != end) { + if (lastOp->isBeforeInBlock(*begin)) { + lastOp = *begin; } } - return vector[maxIndex]; + return lastOp; } - template - Operation* first(V const& vector) { - size_t minIndex = 0; - for (size_t index = 0; index < vector.size(); ++index) { - if (!vector[minIndex]->isBeforeInBlock(vector[index])) { - minIndex = index; + template + Value lastValue(ValueIterator begin, ValueIterator end) { + Value lastVal = *begin; + while (++begin != end) { + if (begin->template isa()) { + continue; + } else if (lastVal.isa() || lastVal.getDefiningOp()->isBeforeInBlock(begin->getDefiningOp())) { + lastVal = *begin; } } - return vector[minIndex]; + return lastVal; } + } LogicalResult VectorizeConstant::matchAndRewrite(SPNConstant op, PatternRewriter& rewriter) const { @@ -80,7 +118,9 @@ LogicalResult VectorizeConstant::matchAndRewrite(SPNConstant op, PatternRewriter auto const& vectorType = VectorType::get(static_cast(vector.size()), op.getType()); if (!node->isUniform()) { - vectorsByNode[node][vectorIndex] = broadcastFirstInsertRest(vector, vectorType, rewriter).getDefiningOp(); + auto const& elements = results(std::begin(vector), std::end(vector)); + auto vectorVal = broadcastFirstInsertRest(std::begin(elements), std::end(elements), vectorType, rewriter); + vectorsByNode[node][vectorIndex] = vectorVal.getDefiningOp(); return success(); } @@ -100,13 +140,11 @@ LogicalResult VectorizeConstant::matchAndRewrite(SPNConstant op, PatternRewriter constAttr = mlir::DenseElementsAttr::get(vectorType, static_cast>(array)); } - rewriter.setInsertionPointAfter(first(vector)); + rewriter.setInsertionPointAfter(firstOperation(std::begin(vector), std::end(vector))); auto constValue = rewriter.create(op->getLoc(), constAttr); vectorsByNode[node][vectorIndex] = constValue; - constValue->dump(); - return success(); } @@ -119,10 +157,12 @@ LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead op, PatternRewrit auto const& vectorType = VectorType::get(static_cast(vector.size()), op.getType()); if (!node->isUniform() || !areConsecutiveLoads(vector)) { - vectorsByNode[node][vectorIndex] = broadcastFirstInsertRest(vector, vectorType, rewriter).getDefiningOp(); + auto const& elements = results(std::begin(vector), std::end(vector)); + auto vectorVal = broadcastFirstInsertRest(std::begin(elements), std::end(elements), vectorType, rewriter); + vectorsByNode[node][vectorIndex] = vectorVal.getDefiningOp(); } else { - rewriter.setInsertionPointAfter(first(vector)); + rewriter.setInsertionPointAfter(firstOperation(std::begin(vector), std::end(vector))); auto memIndex = rewriter.create(op->getLoc(), rewriter.getIndexAttr(op.sampleIndex())); ValueRange indices{op.batchIndex(), memIndex}; @@ -130,17 +170,6 @@ LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead op, PatternRewrit auto vectorLoad = rewriter.create(op->getLoc(), vectorType, op.batchMem(), indices); vectorsByNode[node][vectorIndex] = vectorLoad; - - vectorLoad.dump(); - } - - for (auto* batchRead : vector) { - if (std::all_of(std::begin(batchRead->getUsers()), std::end(batchRead->getUsers()), [&](auto* user) { - return parentNodes.count(user); - })) { - batchRead->dropAllUses(); - rewriter.eraseOp(batchRead); - } } return success(); @@ -155,9 +184,41 @@ LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd op, PatternRewriter& rewriter auto const& vectorType = VectorType::get(static_cast(vector.size()), op.getType()); if (!node->isUniform()) { - //vectorsByNode[node][vectorIndex] = broadcastFirstInsertRest(vector, vectorType, rewriter).getDefiningOp(); + auto const& elements = results(std::begin(vector), std::end(vector)); + auto vectorVal = broadcastFirstInsertRest(std::begin(elements), std::end(elements), vectorType, rewriter); + vectorsByNode[node][vectorIndex] = vectorVal.getDefiningOp(); return success(); } + + llvm::SmallVector operands; + + for (unsigned i = 0; i < op.getNumOperands(); ++i) { + Value operand; + if (std::any_of(std::begin(vector), std::end(vector), [&](auto* vectorOp) { + Value vectorOperand = op.getOperand(i); + return vectorOperand.isa() || !parentNodes.count(vectorOperand.getDefiningOp()); + })) { + llvm::SmallVector elements; + for (auto* vectorOp : vector) { + elements.emplace_back(vectorOp->getOperand(i)); + } + operand = broadcastFirstInsertRest(std::begin(elements), std::end(elements), vectorType, rewriter); + } else { + auto* operandOp = op.getOperand(i).getDefiningOp(); + auto* operandNode = parentNodes.lookup(operandOp); + if (!vectorsByNode.count(operandNode) || vectorsByNode[operandNode].size() != operandNode->numVectors()) { + return rewriter.notifyMatchFailure(op, "operation's LHS has not yet been (fully) vectorized"); + } + operand = vectorsByNode[operandNode][operandNode->getVectorIndex(operandOp)]->getResult(0); + } + operands.emplace_back(operand); + } + + rewriter.setInsertionPointAfterValue(lastValue(std::begin(operands), std::end(operands))); + auto addOp = rewriter.create(op->getLoc(), vectorType, operands); + + vectorsByNode[node][vectorIndex] = addOp; + return success(); } @@ -170,9 +231,42 @@ LogicalResult VectorizeMul::matchAndRewrite(SPNMul op, PatternRewriter& rewriter auto const& vectorType = VectorType::get(static_cast(vector.size()), op.getType()); if (!node->isUniform()) { - //vectorsByNode[node][vectorIndex] = broadcastFirstInsertRest(vector, vectorType, rewriter).getDefiningOp(); + auto const& elements = results(std::begin(vector), std::end(vector)); + auto vectorVal = broadcastFirstInsertRest(std::begin(elements), std::end(elements), vectorType, rewriter); + vectorsByNode[node][vectorIndex] = vectorVal.getDefiningOp(); return success(); } + + llvm::SmallVector operands; + + for (unsigned i = 0; i < op.getNumOperands(); ++i) { + Value operand; + if (std::any_of(std::begin(vector), std::end(vector), [&](auto* vectorOp) { + vectorOp->dump(); + Value vectorOperand = op.getOperand(i); + return vectorOperand.isa() || !parentNodes.count(vectorOperand.getDefiningOp()); + })) { + llvm::SmallVector elements; + for (auto* vectorOp : vector) { + elements.emplace_back(vectorOp->getOperand(i)); + } + operand = broadcastFirstInsertRest(std::begin(elements), std::end(elements), vectorType, rewriter); + } else { + auto* operandOp = op.getOperand(i).getDefiningOp(); + auto* operandNode = parentNodes.lookup(operandOp); + if (!vectorsByNode.count(operandNode) || vectorsByNode[operandNode].size() != operandNode->numVectors()) { + return rewriter.notifyMatchFailure(op, "operation's LHS has not yet been (fully) vectorized"); + } + operand = vectorsByNode[operandNode][operandNode->getVectorIndex(operandOp)]->getResult(0); + } + operands.emplace_back(operand); + } + + rewriter.setInsertionPointAfterValue(lastValue(std::begin(operands), std::end(operands))); + auto mulOp = rewriter.create(op->getLoc(), vectorType, operands); + + vectorsByNode[node][vectorIndex] = mulOp; + return success(); } From f97c38fef2491ccf77cbe95a18c189fcf1c968d3 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 18 Apr 2021 02:29:35 +0200 Subject: [PATCH 081/385] Better SLP graph conversion pass. --- .../SLP/SLPVectorizationPass.cpp | 31 +++++++++++++++++-- 1 file changed, 28 insertions(+), 3 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp index 6949188c..556e4efa 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp @@ -142,17 +142,41 @@ void SLPVectorizationPass::runOnOperation() { NoFoldPatternRewriter rewriter(&getContext()); PatternApplicator applicator(frozenPatterns); applicator.applyDefaultCostModel(); + + // Marks operations that can be deleted. + // We delete them *after* SLP graph conversion to avoid running into NULL operands. + llvm::SmallPtrSet erasableOps; for (auto* node : postOrder(graph.get())) { node->dump(); vectorsByNode[node].resize_for_overwrite(node->numVectors()); - for (auto const& vector : node->getVectors()) { - auto result = applicator.matchAndRewrite(vector.front(), rewriter); + // Also traverse nodes in postorder in case they are multinodes. + auto it = node->getVectors().rbegin(); + while (it != node->getVectors().rend()) { + auto result = applicator.matchAndRewrite(it->front(), rewriter); if (result.failed()) { - vector.front()->emitOpError("SLP pattern application failed"); + it->front()->emitOpError("SLP pattern application failed"); + } + ++it; + } + + it = node->getVectors().rbegin(); + while (it != node->getVectors().rend()) { + for (auto* vectorOp : *it) { + if (std::all_of(std::begin(vectorOp->getUsers()), std::end(vectorOp->getUsers()), [&](auto* user) { + return parentMapping.count(user); + })) { + erasableOps.insert(vectorOp); + } } + ++it; } } + for (auto* op : erasableOps) { + op->dropAllUses(); + rewriter.eraseOp(op); + } + getOperation()->dump(); // If an SLP node failed to vectorize completely, fail the pass. @@ -163,6 +187,7 @@ void SLPVectorizationPass::runOnOperation() { signalPassFailure(); } } + } std::unique_ptr mlir::spn::createSLPVectorizationPass() { From 48ac0c4b840e84b415b79975eaf24fc33f19a3fb Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 18 Apr 2021 04:35:24 +0200 Subject: [PATCH 082/385] Added vector extractions. --- .../SLP/SLPVectorizationPass.cpp | 57 +++++++++++++++---- 1 file changed, 46 insertions(+), 11 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp index 556e4efa..f1c8ad85 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp @@ -11,6 +11,7 @@ #include "LoSPNtoCPU/Vectorization/TargetInformation.h" #include "mlir/Dialect/Linalg/IR/LinalgOps.h" #include "mlir/Dialect/StandardOps/IR/Ops.h" +#include "mlir/Dialect/Vector/VectorOps.h" #include "mlir/Rewrite/PatternApplicator.h" #include "mlir/Transforms/GreedyPatternRewriteDriver.h" #include @@ -144,14 +145,30 @@ void SLPVectorizationPass::runOnOperation() { applicator.applyDefaultCostModel(); // Marks operations that can be deleted. - // We delete them *after* SLP graph conversion to avoid running into NULL operands. - llvm::SmallPtrSet erasableOps; + // We delete them *after* SLP graph conversion to avoid running into NULL operands during conversion. + llvm::SmallPtrSet erasableOps; for (auto* node : postOrder(graph.get())) { node->dump(); - vectorsByNode[node].resize_for_overwrite(node->numVectors()); - // Also traverse nodes in postorder in case they are multinodes. + + // Stores escaping uses for vector extractions that might be necessary later on. + llvm::DenseMap> escapingUses; + + // Also traverse nodes in postorder to properly handle multinodes. auto it = node->getVectors().rbegin(); while (it != node->getVectors().rend()) { + + // Gather escaping uses *now* because the conversion process adds more that *do not* need extractions. + for (size_t lane = 0; lane < it->size(); ++lane) { + auto* vectorOp = (*it)[lane]; + for (auto* user : vectorOp->getUsers()) { + if (!parentMapping.count(user)) { + escapingUses[vectorOp].insert(user); + } + } + } + + // Rewrite vector by applying any matching pattern. + vectorsByNode[node].resize(node->numVectors()); auto result = applicator.matchAndRewrite(it->front(), rewriter); if (result.failed()) { it->front()->emitOpError("SLP pattern application failed"); @@ -159,16 +176,34 @@ void SLPVectorizationPass::runOnOperation() { ++it; } - it = node->getVectors().rbegin(); - while (it != node->getVectors().rend()) { - for (auto* vectorOp : *it) { - if (std::all_of(std::begin(vectorOp->getUsers()), std::end(vectorOp->getUsers()), [&](auto* user) { - return parentMapping.count(user); - })) { + // Gather operations that can be erased and create vector extractions using those that need to stay. + for (size_t vectorIndex = node->numVectors(); vectorIndex-- > 0;) { + auto const& vector = node->getVector(vectorIndex); + for (size_t lane = 0; lane < vector.size(); ++lane) { + auto* vectorOp = vector[lane]; + bool erasable = true; + for (auto& use : vectorOp->getUses()) { + auto* user = use.getOwner(); + if (!parentMapping.count(user)) { + erasable = false; + // Filters out operations that were created during the conversion process. + if (!escapingUses[vectorOp].contains(user)) { + continue; + } + auto const& source = vectorsByNode[node][vectorIndex]->getResult(0); + rewriter.setInsertionPoint(user); + auto extractOp = rewriter.create(vectorOp->getLoc(), source, lane); + auto const& oldOperand = user->getOperand(use.getOperandNumber()); + user->setOperand(use.getOperandNumber(), extractOp); + if (oldOperand.getUses().empty() && !oldOperand.isa()) { + erasableOps.insert(oldOperand.getDefiningOp()); + } + } + } + if (erasable) { erasableOps.insert(vectorOp); } } - ++it; } } From c554921c520cc69b1c26db9f99bcfb4e5cd3fa0e Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 18 Apr 2021 04:49:33 +0200 Subject: [PATCH 083/385] Deleted obsolete FunctionTransformer. --- .../SLP/SLPFunctionTransformer.h | 60 ----- mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt | 1 - .../SLP/SLPFunctionTransformer.cpp | 234 ------------------ 3 files changed, 295 deletions(-) delete mode 100644 mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.h delete mode 100644 mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.h deleted file mode 100644 index b71f6793..00000000 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.h +++ /dev/null @@ -1,60 +0,0 @@ -// -// This file is part of the SPNC project. -// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. -// - -#ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPFUNCTIONTRANSFORMER_H -#define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPFUNCTIONTRANSFORMER_H - -#include "mlir/IR/Operation.h" -#include "mlir/IR/Builders.h" -#include "LoSPNtoCPU/LoSPNtoCPUTypeConverter.h" -#include "LoSPNtoCPU/Vectorization/LoSPNVectorizationTypeConverter.h" -#include "SLPNode.h" -#include - -namespace mlir { - namespace spn { - namespace low { - namespace slp { - - class SLPFunctionTransformer { - - public: - - SLPFunctionTransformer(std::unique_ptr&& graph, FuncOp function); - - void transform(); - - private: - - Value transform(SLPNode* node, size_t vectorIndex); - Value applyCreation(SLPNode* node, size_t vectorIndex, Operation* vectorOp); - - Value extractMemRefOperand(Operation* op); - Operation* broadcastFirstInsertRest(Operation* beforeOp, - Type const& vectorType, - SmallVector& elements); - Value getOrCreateConstant(unsigned index, bool asIndex = true); - - std::unique_ptr root; - FuncOp function; - - OpBuilder builder; - LoSPNVectorizationTypeConverter typeConverter; - - llvm::DenseMap, Value> memRefLoads; - llvm::DenseMap createdIndexConstants; - llvm::DenseMap createdUnsignedConstants; - - llvm::DenseMap vectorsDone; - llvm::DenseMap nodeInputsDone; - std::unordered_set vectorizedOps; - - }; - } - } - } -} - -#endif //SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPFUNCTIONTRANSFORMER_H diff --git a/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt b/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt index a500b060..0d743cea 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt +++ b/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt @@ -12,7 +12,6 @@ add_mlir_conversion_library(MLIRLoSPNtoCPUTransforms Vectorization/SLP/SLPUtil.cpp Vectorization/SLP/SLPVectorizationPass.cpp Vectorization/SLP/SLPVectorizationPatterns.cpp - Vectorization/SLP/SLPFunctionTransformer.cpp LoSPNtoCPUConversionPasses.cpp ADDITIONAL_HEADER_DIRS diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp deleted file mode 100644 index bd17ba46..00000000 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.cpp +++ /dev/null @@ -1,234 +0,0 @@ -// -// This file is part of the SPNC project. -// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. -// - -#include "LoSPNtoCPU/Vectorization/SLP/SLPFunctionTransformer.h" -#include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" -#include "LoSPN/LoSPNOps.h" -#include "mlir/Dialect/Vector/VectorOps.h" -#include "mlir/Dialect/StandardOps/IR/Ops.h" - -using namespace mlir; -using namespace mlir::spn; -using namespace mlir::spn::low; -using namespace mlir::spn::low::slp; - -SLPFunctionTransformer::SLPFunctionTransformer(std::unique_ptr&& graph, FuncOp function) : root{ - std::move(graph)}, function{function}, builder{function->getContext()}, typeConverter{ - static_cast(root->numLanes())} { - -} - -void SLPFunctionTransformer::transform() { - builder.setInsertionPoint(root->getOperation(0, 0)); - transform(root.get(), 0); -} - -Value SLPFunctionTransformer::transform(SLPNode* node, size_t vectorIndex) { - - vectorsDone[node]++; - - auto vectorType = typeConverter.convertType(node->getOperation(0, 0)->getResultTypes().front()); - - auto const& vectorOps = node->getVector(vectorIndex); - vectorizedOps.insert(vectorOps.begin(), vectorOps.end()); - auto const& firstOp = vectorOps.front(); - auto const& loc = firstOp->getLoc(); - - if (areBroadcastable(vectorOps)) { - auto value = firstOp->getResult(0); - auto const& insertionPoint = builder.saveInsertionPoint(); - builder.setInsertionPointAfter(firstOp); - auto broadcast = builder.create(loc, vectorType, value); - builder.restoreInsertionPoint(insertionPoint); - return applyCreation(node, vectorIndex, broadcast); - } - - if (areConsecutiveLoads(vectorOps)) { - auto batchRead = static_cast(firstOp); - auto const& base = batchRead.batchMem(); - auto const& batchIndex = batchRead.batchIndex(); - auto const& offset = getOrCreateConstant(batchRead.sampleIndex()); - - auto indices = ValueRange{batchIndex, offset}; - - return applyCreation(node, vectorIndex, builder.create(loc, vectorType, base, indices)); - } - - if (canBeGathered(vectorOps)) { - SmallVector elements; - for (auto const& op : vectorOps) { - elements.emplace_back(extractMemRefOperand(op)); - } - return applyCreation(node, vectorIndex, broadcastFirstInsertRest(firstOp, vectorType, elements)); - } - - if (node->isUniform() && firstOp->getNumOperands() > 0) { - - llvm::SmallVector operands; - - for (size_t i = 0; i < firstOp->getNumOperands(); ++i) { - - if (node->numOperands() == 0) { - size_t nextIndex = vectorsDone[node]; - if (nextIndex < node->numVectors()) { - operands.emplace_back(transform(node, nextIndex)); - } else { - SmallVector vectorElements; - for (size_t lane = 0; lane < node->numLanes(); ++lane) { - auto nodeInput = node->getNodeInput(nodeInputsDone[node]++); - if (nodeInput.getType().isa()) { - nodeInput = extractMemRefOperand(vectorOps[lane]); - } - vectorElements.emplace_back(nodeInput); - } - auto* vectorOp = broadcastFirstInsertRest(firstOp, vectorType, vectorElements); - operands.emplace_back(applyCreation(node, vectorIndex, vectorOp)); - } - } else { - - size_t initialOperandIndex = vectorIndex % node->numOperands(); - - size_t operandNodeIndex = initialOperandIndex; - SLPNode* operandNode = (i == 0) ? node : node->getOperand(operandNodeIndex); - bool useNodeInputs = false; - - size_t nextVectorIndex = vectorsDone[operandNode]; - while (nextVectorIndex >= operandNode->numVectors()) { - operandNode = node->getOperand(operandNodeIndex % node->numOperands()); - nextVectorIndex = vectorsDone[operandNode]; - // Check if we have looped through all operands already. - if (operandNodeIndex++ == initialOperandIndex + node->numOperands()) { - useNodeInputs = true; - break; - } - } - - if (useNodeInputs) { - SmallVector vectorElements; - for (size_t lane = 0; lane < node->numLanes(); ++lane) { - auto nodeInput = node->getNodeInput(nodeInputsDone[node]++); - if (nodeInput.getType().isa()) { - nodeInput = extractMemRefOperand(vectorOps[lane]); - } - vectorElements.emplace_back(nodeInput); - } - auto* vectorOp = broadcastFirstInsertRest(firstOp, vectorType, vectorElements); - operands.emplace_back(applyCreation(node, vectorIndex, vectorOp)); - } else { - operands.emplace_back(transform(operandNode, nextVectorIndex)); - } - - } - } - - Operation* vectorOp; - - if (dyn_cast(firstOp)) { - vectorOp = builder.create(loc, operands); - } else if (dyn_cast(firstOp)) { - vectorOp = builder.create(loc, operands); - } else { - vectorOp = builder.insert(Operation::create(loc, - firstOp->getName(), - vectorType, - operands, - firstOp->getAttrs(), - firstOp->getSuccessors(), - firstOp->getNumRegions())); - } - return applyCreation(node, vectorIndex, vectorOp); - } - - llvm::SmallVector vectorElements; - for (auto op : vectorOps) { - vectorElements.emplace_back(op->getResult(0)); - } - return applyCreation(node, vectorIndex, broadcastFirstInsertRest(firstOp, vectorType, vectorElements)); -} - -Value SLPFunctionTransformer::applyCreation(SLPNode* node, size_t vectorIndex, Operation* createdVectorOp) { - - bool isBroadcast = dyn_cast(createdVectorOp); - - for (size_t lane = 0; lane < node->numLanes(); ++lane) { - auto* operation = node->getOperation(lane, vectorIndex); - if (isBroadcast && operation == node->getOperation(0, vectorIndex)) { - continue; - } - bool createExtraction = false; - for (auto* use : operation->getUsers()) { - if (vectorizedOps.find(use) == std::end(vectorizedOps)) { - createExtraction = true; - break; - } - } - - if (createExtraction) { - - auto const& source = createdVectorOp->getResult(0); - auto const& position = getOrCreateConstant(lane, false); - - auto const& insertionPoint = builder.saveInsertionPoint(); - builder.setInsertionPointAfter(createdVectorOp); - auto element = builder.create(builder.getUnknownLoc(), source, position); - builder.restoreInsertionPoint(insertionPoint); - operation->replaceAllUsesWith(element); - - } - operation->dropAllUses(); - operation->remove(); - } - return createdVectorOp->getResult(0); -} - -Operation* SLPFunctionTransformer::broadcastFirstInsertRest(Operation* beforeOp, - Type const& vectorType, - SmallVector& elements) { - Operation* vectorOp = builder.create(beforeOp->getLoc(), vectorType, elements[0]); - for (size_t i = 1; i < elements.size(); ++i) { - auto const& position = getOrCreateConstant(i, false); - vectorOp = - builder.create(beforeOp->getLoc(), elements[i], vectorOp->getResult(0), position); - } - return vectorOp; -} - -Value SLPFunctionTransformer::getOrCreateConstant(unsigned index, bool asIndex) { - auto& createdConstants = asIndex ? createdIndexConstants : createdUnsignedConstants; - if (!createdConstants.count(index)) { - auto const& insertionPoint = builder.saveInsertionPoint(); - auto const& attribute = asIndex ? builder.getIndexAttr(index) : builder.getIntegerAttr(builder.getI32Type(), index); - builder.setInsertionPointToStart(&function.body().front()); - createdConstants[index] = builder.create(builder.getUnknownLoc(), attribute); - builder.restoreInsertionPoint(insertionPoint); - } - return createdConstants[index]; -} - -Value SLPFunctionTransformer::extractMemRefOperand(Operation* op) { - Value base; - ValueRange indices; - if (auto batchRead = dyn_cast(op)) { - base = batchRead.batchMem(); - auto const& batchIndex = batchRead.batchIndex(); - auto const& sampleIndex = batchRead.sampleIndex(); - auto const& memRefTuple = std::make_tuple(base, batchIndex, sampleIndex); - - if (memRefLoads.count(memRefTuple)) { - return memRefLoads[memRefTuple]; - } - - indices = {batchIndex, getOrCreateConstant(sampleIndex)}; - - } else { - assert(false); - } - - auto const& insertionPoint = builder.saveInsertionPoint(); - builder.setInsertionPoint(op); - auto load = builder.create(op->getLoc(), base, indices); - builder.restoreInsertionPoint(insertionPoint); - return load; -} \ No newline at end of file From 4797f07daf0b9cf483432fe7afa7e4e89f4ec404 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 18 Apr 2021 04:49:45 +0200 Subject: [PATCH 084/385] Import cleanup. --- .../LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h | 1 - .../LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp | 4 ---- 2 files changed, 5 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h index 681e4487..25be2fa6 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h @@ -9,7 +9,6 @@ #include #include "mlir/IR/Operation.h" #include "mlir/Pass/Pass.h" -#include "SLPGraphBuilder.h" namespace mlir { namespace spn { diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp index f1c8ad85..b8f9cf56 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp @@ -10,10 +10,8 @@ #include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h" #include "LoSPNtoCPU/Vectorization/TargetInformation.h" #include "mlir/Dialect/Linalg/IR/LinalgOps.h" -#include "mlir/Dialect/StandardOps/IR/Ops.h" #include "mlir/Dialect/Vector/VectorOps.h" #include "mlir/Rewrite/PatternApplicator.h" -#include "mlir/Transforms/GreedyPatternRewriteDriver.h" #include using namespace mlir::spn::low::slp; @@ -129,8 +127,6 @@ void SLPVectorizationPass::runOnOperation() { SLPGraphBuilder builder{3}; auto graph = builder.build(seeds.front()); // graph->dumpGraph(); - //SLPFunctionTransformer transformer(std::move(graph), function); - //transformer.transform(); // ==== // OwningRewritePatternList patterns; From a666fbb3057e5424f8f5ff207892f30f241d59ec Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 18 Apr 2021 16:02:46 +0200 Subject: [PATCH 085/385] Added vectorization strategy option to toolchain. --- compiler/include/driver/GlobalOptions.h | 8 ++++++++ .../codegen/mlir/conversion/LoSPNtoCPUConversion.cpp | 8 +++++++- compiler/src/driver/option/GlobalOptions.cpp | 6 ++++++ compiler/src/driver/toolchain/CPUToolchain.cpp | 10 ++++++++++ 4 files changed, 31 insertions(+), 1 deletion(-) diff --git a/compiler/include/driver/GlobalOptions.h b/compiler/include/driver/GlobalOptions.h index c271be7d..bf5af770 100644 --- a/compiler/include/driver/GlobalOptions.h +++ b/compiler/include/driver/GlobalOptions.h @@ -39,6 +39,14 @@ namespace spnc { /// Flag to indicate whether the code generated for the CPU should be vectorized. extern Option cpuVectorize; + /// Available vectorization strategies + enum VectorizationStrategy { + BATCH, + SLP + }; + + extern EnumOpt vectorizationStrategy; + /// Available vector libraries enum VectorLibrary { SVML, diff --git a/compiler/src/codegen/mlir/conversion/LoSPNtoCPUConversion.cpp b/compiler/src/codegen/mlir/conversion/LoSPNtoCPUConversion.cpp index a8ab8944..9b397505 100644 --- a/compiler/src/codegen/mlir/conversion/LoSPNtoCPUConversion.cpp +++ b/compiler/src/codegen/mlir/conversion/LoSPNtoCPUConversion.cpp @@ -6,6 +6,7 @@ #include "LoSPNtoCPUConversion.h" #include "LoSPNtoCPU/LoSPNtoCPUConversionPasses.h" #include "LoSPNtoCPU/Vectorization/VectorOptimizationPasses.h" +#include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h" #include "mlir/Transforms/Passes.h" #include "mlir/Dialect/Tensor/Transforms/Passes.h" #include @@ -18,7 +19,12 @@ void spnc::LoSPNtoCPUConversion::initializePassPipeline(mlir::PassManager* pm, m if (useShuffle) { pm->addPass(mlir::spn::createReplaceGatherWithShufflePass()); } - pm->addPass(mlir::spn::createLoSPNNodeVectorizationPass()); + auto vectorizationStrategy = spnc::option::vectorizationStrategy.get(*this->config); + if(vectorizationStrategy == spnc::option::VectorizationStrategy::SLP) { + pm->addNestedPass(mlir::spn::createSLPVectorizationPass()); + } else if(vectorizationStrategy == spnc::option::VectorizationStrategy::BATCH) { + pm->addPass(mlir::spn::createLoSPNNodeVectorizationPass()); + } if (useShuffle) { // We need another run of the canonicalizer here to remove lo_spn.to_scalar // operations introduced by the replacement of gathers and that should diff --git a/compiler/src/driver/option/GlobalOptions.cpp b/compiler/src/driver/option/GlobalOptions.cpp index 33a8634d..c2618406 100644 --- a/compiler/src/driver/option/GlobalOptions.cpp +++ b/compiler/src/driver/option/GlobalOptions.cpp @@ -22,6 +22,12 @@ EnumOpt spnc::option::compilationTarget{"target", Option spnc::option::cpuVectorize{"cpu-vectorize", false}; +using spnc::option::VectorizationStrategy; +EnumOpt spnc::option::vectorizationStrategy{"vectorization-strategy", BATCH, + {EnumVal(BATCH, "BATCH"), + EnumVal(SLP, "SLP")}, + {depends(spnc::option::cpuVectorize, true)}}; + using spnc::option::VectorLibrary; EnumOpt spnc::option::vectorLibrary{"vector-library", NONE, {EnumVal(SVML, "SVML"), diff --git a/compiler/src/driver/toolchain/CPUToolchain.cpp b/compiler/src/driver/toolchain/CPUToolchain.cpp index 33e51688..c0a64edd 100644 --- a/compiler/src/driver/toolchain/CPUToolchain.cpp +++ b/compiler/src/driver/toolchain/CPUToolchain.cpp @@ -35,6 +35,16 @@ std::unique_ptr > CPUToolchain::constructJobFromFile(const std::stri auto diagHandler = setupDiagnosticHandler(ctx.get()); auto cpuVectorize = spnc::option::cpuVectorize.get(*config); SPDLOG_INFO("CPU Vectorization enabled: {}", cpuVectorize); + if(cpuVectorize) { + auto vectorizationStrategy = spnc::option::vectorizationStrategy.get(*config); + std::string strategyName; + switch (vectorizationStrategy) { + case spnc::option::VectorizationStrategy::BATCH: strategyName = "BATCH"; break; + case spnc::option::VectorizationStrategy::SLP: strategyName = "SLP"; break; + default:SPNC_FATAL_ERROR("Unknown vectorization strategy"); + } + SPDLOG_INFO("CPU Vectorization strategy: {}", strategyName); + } auto targetMachine = createTargetMachine(cpuVectorize); auto kernelInfo = std::make_shared(); kernelInfo->target = KernelTarget::CPU; From fec113632ab11207cf308be3152ec4ccf0ae6574 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 18 Apr 2021 17:15:42 +0200 Subject: [PATCH 086/385] Better constant vectorization patterns. --- .../SLP/SLPVectorizationPatterns.h | 34 +++++++------------ .../SLP/SLPVectorizationPass.cpp | 2 +- .../SLP/SLPVectorizationPatterns.cpp | 24 ++++++------- 3 files changed, 25 insertions(+), 35 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h index 90741469..1b320a64 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h @@ -34,47 +34,39 @@ namespace mlir { llvm::DenseMap>& vectorsByNode; }; - struct VectorizeBatchRead : public SLPVectorizationPattern { + template + struct VectorizeConstantPattern : public SLPVectorizationPattern { + using SLPVectorizationPattern::SLPVectorizationPattern; + LogicalResult matchAndRewrite(ConstantSourceOp op, PatternRewriter& rewriter) const override; + }; + struct VectorizeBatchRead : public SLPVectorizationPattern { using SLPVectorizationPattern::SLPVectorizationPattern; - LogicalResult matchAndRewrite(SPNBatchRead op, PatternRewriter& rewriter) const override; }; - struct VectorizeMul : public SLPVectorizationPattern { - - using SLPVectorizationPattern::SLPVectorizationPattern; - - LogicalResult matchAndRewrite(SPNMul op, PatternRewriter& rewriter) const override; - }; - struct VectorizeAdd : public SLPVectorizationPattern { - using SLPVectorizationPattern::SLPVectorizationPattern; - LogicalResult matchAndRewrite(SPNAdd op, PatternRewriter& rewriter) const override; }; - struct VectorizeLog : public SLPVectorizationPattern { + struct VectorizeMul : public SLPVectorizationPattern { + using SLPVectorizationPattern::SLPVectorizationPattern; + LogicalResult matchAndRewrite(SPNMul op, PatternRewriter& rewriter) const override; + }; + struct VectorizeLog : public SLPVectorizationPattern { using SLPVectorizationPattern::SLPVectorizationPattern; - LogicalResult matchAndRewrite(SPNLog op, PatternRewriter& rewriter) const override; }; - struct VectorizeConstant : public SLPVectorizationPattern { - - using SLPVectorizationPattern::SLPVectorizationPattern; - - LogicalResult matchAndRewrite(SPNConstant op, PatternRewriter& rewriter) const override; - }; - static void populateSLPVectorizationPatterns(OwningRewritePatternList& patterns, MLIRContext* context, llvm::DenseMap const& parentNodes, llvm::DenseMap>& vectorsByNode) { - patterns.insert(context, 2, parentNodes, vectorsByNode); + patterns.insert>(context, 2, parentNodes, vectorsByNode); + patterns.insert>(context, 2, parentNodes, vectorsByNode); patterns.insert(context, 2, parentNodes, vectorsByNode); patterns.insert(context, 2, parentNodes, vectorsByNode); } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp index b8f9cf56..1d5ed6c5 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp @@ -167,7 +167,7 @@ void SLPVectorizationPass::runOnOperation() { vectorsByNode[node].resize(node->numVectors()); auto result = applicator.matchAndRewrite(it->front(), rewriter); if (result.failed()) { - it->front()->emitOpError("SLP pattern application failed"); + it->front()->emitOpError("SLP pattern application failed (did you forget to specify the pattern?)"); } ++it; } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp index 2d1f5b68..5113caff 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp @@ -9,10 +9,10 @@ #include "LoSPNtoCPU/Vectorization/TargetInformation.h" #include "llvm/Support/FormatVariadic.h" #include "mlir/Dialect/StandardOps/IR/Ops.h" +#include "mlir/Dialect/Vector/VectorOps.h" #include "mlir/Dialect/SCF/SCF.h" #include "mlir/Dialect/Math/IR/Math.h" #include "mlir/IR/BuiltinOps.h" -#include "mlir/Dialect/Vector/VectorOps.h" #include "LoSPN/LoSPNAttributes.h" using namespace mlir; @@ -23,13 +23,6 @@ using namespace mlir::spn::low::slp; // Helper functions in anonymous namespace. namespace { - void broadcastIdentical(Value const& source, Type const& vectorType, PatternRewriter& rewriter) { - auto const& insertionPoint = rewriter.saveInsertionPoint(); - rewriter.setInsertionPointAfterValue(source); - rewriter.create(source.getLoc(), vectorType, source); - rewriter.restoreInsertionPoint(insertionPoint); - } - template Value broadcastFirstInsertRest(ValueIterator begin, ValueIterator end, @@ -109,9 +102,14 @@ namespace { } -LogicalResult VectorizeConstant::matchAndRewrite(SPNConstant op, PatternRewriter& rewriter) const { +template +LogicalResult VectorizeConstantPattern::matchAndRewrite(ConstantSourceOp op, PatternRewriter& rewriter) const { - auto* node = parentNodes.lookup(op); + if(!op->template hasTrait()) { + rewriter.notifyMatchFailure(op, "Constant vectorization pattern cannot be applied to this operation"); + } + + auto* node = this->parentNodes.lookup(op); auto const& vectorIndex = node->getVectorIndex(op); auto const& vector = node->getVector(vectorIndex); @@ -120,13 +118,13 @@ LogicalResult VectorizeConstant::matchAndRewrite(SPNConstant op, PatternRewriter if (!node->isUniform()) { auto const& elements = results(std::begin(vector), std::end(vector)); auto vectorVal = broadcastFirstInsertRest(std::begin(elements), std::end(elements), vectorType, rewriter); - vectorsByNode[node][vectorIndex] = vectorVal.getDefiningOp(); + this->vectorsByNode[node][vectorIndex] = vectorVal.getDefiningOp(); return success(); } DenseElementsAttr constAttr; - if (vectorType.getElementType().cast().getWidth() == 32) { + if (vectorType.getElementType().template cast().getWidth() == 32) { llvm::SmallVector array; for (int i = 0; i < vectorType.getNumElements(); ++i) { array.push_back(static_cast(vector[i]).value().convertToFloat()); @@ -143,7 +141,7 @@ LogicalResult VectorizeConstant::matchAndRewrite(SPNConstant op, PatternRewriter rewriter.setInsertionPointAfter(firstOperation(std::begin(vector), std::end(vector))); auto constValue = rewriter.create(op->getLoc(), constAttr); - vectorsByNode[node][vectorIndex] = constValue; + this->vectorsByNode[node][vectorIndex] = constValue; return success(); } From 4f4e00068288c2242dec2f38eef9ab6d4c44e8a5 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Mon, 19 Apr 2021 00:25:22 +0200 Subject: [PATCH 087/385] Added Gaussian vectorization, refactored utility methods. --- .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.h | 67 ++++- .../SLP/SLPVectorizationPatterns.h | 25 +- .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp | 27 -- .../SLP/SLPVectorizationPass.cpp | 1 - .../SLP/SLPVectorizationPatterns.cpp | 265 ++++++++++-------- 5 files changed, 229 insertions(+), 156 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h index a10ff29f..fbdbcdc1 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h @@ -16,8 +16,71 @@ namespace mlir { bool areConsecutiveLoads(low::SPNBatchRead load1, low::SPNBatchRead load2); bool areConsecutiveLoads(std::vector const& loads); - bool canBeGathered(std::vector const& loads); - bool areBroadcastable(std::vector const& ops); + + template + Operation* firstUser(OperationIterator begin, OperationIterator end) { + Operation* firstUser = nullptr; + while (begin != end) { + for (auto* user : (*begin)->getUsers()) { + if (!firstUser || user->isBeforeInBlock(firstUser)) { + firstUser = user; + } + } + ++begin; + } + return firstUser; + } + + template + Operation* firstOperation(OperationIterator begin, OperationIterator end) { + Operation* firstOp = *begin; + while (++begin != end) { + if (!firstOp->isBeforeInBlock(*begin)) { + firstOp = *begin; + } + } + return firstOp; + } + + template + Operation* lastOperation(OperationIterator begin, OperationIterator end) { + Operation* lastOp = *begin; + while (++begin != end) { + if (lastOp->isBeforeInBlock(*begin)) { + lastOp = *begin; + } + } + return lastOp; + } + + template + Value firstValue(ValueIterator begin, ValueIterator end) { + Value firstVal = *begin; + while (begin != end) { + if (begin->template isa()) { + return *begin; + } + if (!firstVal.getDefiningOp()->isBeforeInBlock(*begin)) { + firstVal = *begin; + } + ++begin; + } + return firstVal; + } + + template + Value lastValue(ValueIterator begin, ValueIterator end) { + Value lastVal = *begin; + while (++begin != end) { + if (begin->template isa()) { + continue; + } else if (lastVal.isa() + || lastVal.getDefiningOp()->isBeforeInBlock(begin->getDefiningOp())) { + lastVal = *begin; + } + } + return lastVal; + } } } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h index 1b320a64..419ff048 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h @@ -34,30 +34,29 @@ namespace mlir { llvm::DenseMap>& vectorsByNode; }; - template - struct VectorizeConstantPattern : public SLPVectorizationPattern { - using SLPVectorizationPattern::SLPVectorizationPattern; - LogicalResult matchAndRewrite(ConstantSourceOp op, PatternRewriter& rewriter) const override; + struct VectorizeConstant : public SLPVectorizationPattern { + using SLPVectorizationPattern::SLPVectorizationPattern; + LogicalResult matchAndRewrite(ConstantOp constantOp, PatternRewriter& rewriter) const override; }; struct VectorizeBatchRead : public SLPVectorizationPattern { using SLPVectorizationPattern::SLPVectorizationPattern; - LogicalResult matchAndRewrite(SPNBatchRead op, PatternRewriter& rewriter) const override; + LogicalResult matchAndRewrite(SPNBatchRead batchReadOp, PatternRewriter& rewriter) const override; }; struct VectorizeAdd : public SLPVectorizationPattern { using SLPVectorizationPattern::SLPVectorizationPattern; - LogicalResult matchAndRewrite(SPNAdd op, PatternRewriter& rewriter) const override; + LogicalResult matchAndRewrite(SPNAdd addOp, PatternRewriter& rewriter) const override; }; struct VectorizeMul : public SLPVectorizationPattern { using SLPVectorizationPattern::SLPVectorizationPattern; - LogicalResult matchAndRewrite(SPNMul op, PatternRewriter& rewriter) const override; + LogicalResult matchAndRewrite(SPNMul mulOp, PatternRewriter& rewriter) const override; }; - struct VectorizeLog : public SLPVectorizationPattern { - using SLPVectorizationPattern::SLPVectorizationPattern; - LogicalResult matchAndRewrite(SPNLog op, PatternRewriter& rewriter) const override; + struct VectorizeGaussian : public SLPVectorizationPattern { + using SLPVectorizationPattern::SLPVectorizationPattern; + LogicalResult matchAndRewrite(SPNGaussianLeaf gaussianOp, PatternRewriter& rewriter) const override; }; static void populateSLPVectorizationPatterns(OwningRewritePatternList& patterns, @@ -65,10 +64,10 @@ namespace mlir { llvm::DenseMap const& parentNodes, llvm::DenseMap>& vectorsByNode) { - patterns.insert>(context, 2, parentNodes, vectorsByNode); - patterns.insert>(context, 2, parentNodes, vectorsByNode); + patterns.insert(context, 2, parentNodes, vectorsByNode); patterns.insert(context, 2, parentNodes, vectorsByNode); - patterns.insert(context, 2, parentNodes, vectorsByNode); + patterns.insert(context, 2, parentNodes, vectorsByNode); + patterns.insert(context, 2, parentNodes, vectorsByNode); } } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp index c12d1f68..e122fc70 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp @@ -36,30 +36,3 @@ bool mlir::spn::low::slp::areConsecutiveLoads(std::vector const& loa } return true; } - -bool mlir::spn::low::slp::canBeGathered(std::vector const& loads) { - auto firstLoad = dyn_cast(loads.front()); - if (!firstLoad) { - return false; - } - for (size_t i = 1; i < loads.size(); ++i) { - auto load = dyn_cast(loads[i]); - if (!load) { - return false; - } - if (firstLoad.batchMem() != load.batchMem()) { - return false; - } - if (firstLoad.batchIndex() != load.batchIndex()) { - return false; - } - } - return true; -} - -bool mlir::spn::low::slp::areBroadcastable(std::vector const& ops) { - return std::all_of(std::begin(ops), std::end(ops), [&](mlir::Operation* op) { - return OperationEquivalence::isEquivalentTo(op, ops.front()); - }); -} - diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp index 1d5ed6c5..02b65bb5 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp @@ -144,7 +144,6 @@ void SLPVectorizationPass::runOnOperation() { // We delete them *after* SLP graph conversion to avoid running into NULL operands during conversion. llvm::SmallPtrSet erasableOps; for (auto* node : postOrder(graph.get())) { - node->dump(); // Stores escaping uses for vector extractions that might be necessary later on. llvm::DenseMap> escapingUses; diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp index 5113caff..bf795bc6 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp @@ -6,14 +6,9 @@ #include "mlir/IR/BlockAndValueMapping.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" -#include "LoSPNtoCPU/Vectorization/TargetInformation.h" #include "llvm/Support/FormatVariadic.h" -#include "mlir/Dialect/StandardOps/IR/Ops.h" #include "mlir/Dialect/Vector/VectorOps.h" -#include "mlir/Dialect/SCF/SCF.h" #include "mlir/Dialect/Math/IR/Math.h" -#include "mlir/IR/BuiltinOps.h" -#include "LoSPN/LoSPNAttributes.h" using namespace mlir; using namespace mlir::spn; @@ -28,21 +23,17 @@ namespace { ValueIterator end, VectorType const& vectorType, PatternRewriter& rewriter) { - auto const& insertionPoint = rewriter.saveInsertionPoint(); - rewriter.setInsertionPointAfterValue(*begin); Value vectorOp = rewriter.create(begin->getLoc(), vectorType, *begin); unsigned position = 1; while (++begin != end) { - rewriter.setInsertionPointAfterValue(*begin); vectorOp = rewriter.create(begin->getLoc(), *begin, vectorOp, position++); } - rewriter.restoreInsertionPoint(insertionPoint); return vectorOp; } template - llvm::SmallVector results(OperationIterator begin, OperationIterator end) { - llvm::SmallVector elements; + SmallVector results(OperationIterator begin, OperationIterator end) { + SmallVector elements; while (begin != end) { elements.emplace_back((*begin)->getResult(0)); ++begin; @@ -50,70 +41,51 @@ namespace { return elements; } - template - Operation* firstOperation(OperationIterator begin, OperationIterator end) { - Operation* firstOp = *begin; - while (++begin != end) { - if (!firstOp->isBeforeInBlock(*begin)) { - firstOp = *begin; + template + DenseElementsAttr denseFloatingPoints(AttributeIterator begin, AttributeIterator end, VectorType const& vectorType) { + if (vectorType.getElementType().cast().getWidth() == 32) { + SmallVector array; + while (begin != end) { + array.push_back(begin->template cast().getValue().convertToFloat()); + ++begin; } + return DenseElementsAttr::get(vectorType, static_cast>(array)); } - return firstOp; - } - - template - Value firstValue(ValueIterator begin, ValueIterator end) { - Value firstVal = *begin; + SmallVector array; while (begin != end) { - if (begin->template isa()) { - return *begin; - } - if (!firstVal.getDefiningOp()->isBeforeInBlock(*begin)) { - firstVal = *begin; - } + array.push_back(begin->template cast().getValue().convertToDouble()); ++begin; } - return firstVal; + return DenseElementsAttr::get(vectorType, static_cast>(array)); } - template - Operation* lastOperation(OperationIterator begin, OperationIterator end) { - Operation* lastOp = *begin; - while (++begin != end) { - if (lastOp->isBeforeInBlock(*begin)) { - lastOp = *begin; + template + DenseElementsAttr denseFloatingPoints(T value, VectorType const& vectorType) { + if (vectorType.getElementType().cast().getWidth() == 32) { + SmallVector array; + for (unsigned i = 0; i < vectorType.getNumElements(); ++i) { + array.push_back(static_cast(value)); } + return DenseElementsAttr::get(vectorType, static_cast>(array)); } - return lastOp; - } - - template - Value lastValue(ValueIterator begin, ValueIterator end) { - Value lastVal = *begin; - while (++begin != end) { - if (begin->template isa()) { - continue; - } else if (lastVal.isa() || lastVal.getDefiningOp()->isBeforeInBlock(begin->getDefiningOp())) { - lastVal = *begin; - } + SmallVector array; + for (unsigned i = 0; i < vectorType.getNumElements(); ++i) { + array.push_back(value); } - return lastVal; + return DenseElementsAttr::get(vectorType, static_cast>(array)); } } -template -LogicalResult VectorizeConstantPattern::matchAndRewrite(ConstantSourceOp op, PatternRewriter& rewriter) const { - - if(!op->template hasTrait()) { - rewriter.notifyMatchFailure(op, "Constant vectorization pattern cannot be applied to this operation"); - } +LogicalResult VectorizeConstant::matchAndRewrite(ConstantOp constantOp, PatternRewriter& rewriter) const { - auto* node = this->parentNodes.lookup(op); + auto* node = this->parentNodes.lookup(constantOp); - auto const& vectorIndex = node->getVectorIndex(op); + auto const& vectorIndex = node->getVectorIndex(constantOp); auto const& vector = node->getVector(vectorIndex); - auto const& vectorType = VectorType::get(static_cast(vector.size()), op.getType()); + auto const& vectorType = VectorType::get(static_cast(vector.size()), constantOp.getType()); + + rewriter.setInsertionPoint(firstUser(std::begin(vector), std::end(vector))); if (!node->isUniform()) { auto const& elements = results(std::begin(vector), std::end(vector)); @@ -122,37 +94,28 @@ LogicalResult VectorizeConstantPattern::matchAndRewrite(Consta return success(); } - DenseElementsAttr constAttr; - - if (vectorType.getElementType().template cast().getWidth() == 32) { - llvm::SmallVector array; - for (int i = 0; i < vectorType.getNumElements(); ++i) { - array.push_back(static_cast(vector[i]).value().convertToFloat()); - } - constAttr = mlir::DenseElementsAttr::get(vectorType, static_cast>(array)); - } else { - llvm::SmallVector array; - for (int i = 0; i < vectorType.getNumElements(); ++i) { - array.push_back(static_cast(vector[i]).value().convertToDouble()); - } - constAttr = mlir::DenseElementsAttr::get(vectorType, static_cast>(array)); + SmallVector constants; + for (auto* vectorOp : vector) { + constants.emplace_back(static_cast(vectorOp).getValue()); } + auto const& elements = denseFloatingPoints(std::begin(constants), std::end(constants), vectorType); - rewriter.setInsertionPointAfter(firstOperation(std::begin(vector), std::end(vector))); - auto constValue = rewriter.create(op->getLoc(), constAttr); + auto constVector = rewriter.create(constantOp->getLoc(), elements); - this->vectorsByNode[node][vectorIndex] = constValue; + this->vectorsByNode[node][vectorIndex] = constVector; return success(); } -LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead op, PatternRewriter& rewriter) const { +LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead batchReadOp, PatternRewriter& rewriter) const { - auto* node = parentNodes.lookup(op); + auto* node = parentNodes.lookup(batchReadOp); - auto const& vectorIndex = node->getVectorIndex(op); + auto const& vectorIndex = node->getVectorIndex(batchReadOp); auto const& vector = node->getVector(vectorIndex); - auto const& vectorType = VectorType::get(static_cast(vector.size()), op.getType()); + auto const& vectorType = VectorType::get(static_cast(vector.size()), batchReadOp.getType()); + + rewriter.setInsertionPoint(firstUser(std::begin(vector), std::end(vector))); if (!node->isUniform() || !areConsecutiveLoads(vector)) { auto const& elements = results(std::begin(vector), std::end(vector)); @@ -160,12 +123,10 @@ LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead op, PatternRewrit vectorsByNode[node][vectorIndex] = vectorVal.getDefiningOp(); } else { - rewriter.setInsertionPointAfter(firstOperation(std::begin(vector), std::end(vector))); - - auto memIndex = rewriter.create(op->getLoc(), rewriter.getIndexAttr(op.sampleIndex())); - ValueRange indices{op.batchIndex(), memIndex}; - - auto vectorLoad = rewriter.create(op->getLoc(), vectorType, op.batchMem(), indices); + auto batchReadLoc = batchReadOp->getLoc(); + auto memIndex = rewriter.create(batchReadLoc, rewriter.getIndexAttr(batchReadOp.sampleIndex())); + ValueRange indices{batchReadOp.batchIndex(), memIndex}; + auto vectorLoad = rewriter.create(batchReadLoc, vectorType, batchReadOp.batchMem(), indices); vectorsByNode[node][vectorIndex] = vectorLoad; } @@ -173,13 +134,15 @@ LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead op, PatternRewrit return success(); } -LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd op, PatternRewriter& rewriter) const { +LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd addOp, PatternRewriter& rewriter) const { - auto* node = parentNodes.lookup(op); + auto* node = parentNodes.lookup(addOp); - auto const& vectorIndex = node->getVectorIndex(op); + auto const& vectorIndex = node->getVectorIndex(addOp); auto const& vector = node->getVector(vectorIndex); - auto const& vectorType = VectorType::get(static_cast(vector.size()), op.getType()); + auto const& vectorType = VectorType::get(static_cast(vector.size()), addOp.getType()); + + rewriter.setInsertionPoint(firstOperation(std::begin(vector), std::end(vector))); if (!node->isUniform()) { auto const& elements = results(std::begin(vector), std::end(vector)); @@ -190,10 +153,10 @@ LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd op, PatternRewriter& rewriter llvm::SmallVector operands; - for (unsigned i = 0; i < op.getNumOperands(); ++i) { + for (unsigned i = 0; i < addOp.getNumOperands(); ++i) { Value operand; if (std::any_of(std::begin(vector), std::end(vector), [&](auto* vectorOp) { - Value vectorOperand = op.getOperand(i); + Value vectorOperand = addOp.getOperand(i); return vectorOperand.isa() || !parentNodes.count(vectorOperand.getDefiningOp()); })) { llvm::SmallVector elements; @@ -202,31 +165,32 @@ LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd op, PatternRewriter& rewriter } operand = broadcastFirstInsertRest(std::begin(elements), std::end(elements), vectorType, rewriter); } else { - auto* operandOp = op.getOperand(i).getDefiningOp(); + auto* operandOp = addOp.getOperand(i).getDefiningOp(); auto* operandNode = parentNodes.lookup(operandOp); if (!vectorsByNode.count(operandNode) || vectorsByNode[operandNode].size() != operandNode->numVectors()) { - return rewriter.notifyMatchFailure(op, "operation's LHS has not yet been (fully) vectorized"); + return rewriter.notifyMatchFailure(addOp, "operation's LHS has not yet been (fully) vectorized"); } operand = vectorsByNode[operandNode][operandNode->getVectorIndex(operandOp)]->getResult(0); } operands.emplace_back(operand); } - rewriter.setInsertionPointAfterValue(lastValue(std::begin(operands), std::end(operands))); - auto addOp = rewriter.create(op->getLoc(), vectorType, operands); + auto vectorAddOp = rewriter.create(addOp->getLoc(), vectorType, operands); - vectorsByNode[node][vectorIndex] = addOp; + vectorsByNode[node][vectorIndex] = vectorAddOp; return success(); } -LogicalResult VectorizeMul::matchAndRewrite(SPNMul op, PatternRewriter& rewriter) const { +LogicalResult VectorizeMul::matchAndRewrite(SPNMul mulOp, PatternRewriter& rewriter) const { - auto* node = parentNodes.lookup(op); + auto* node = parentNodes.lookup(mulOp); - auto const& vectorIndex = node->getVectorIndex(op); + auto const& vectorIndex = node->getVectorIndex(mulOp); auto const& vector = node->getVector(vectorIndex); - auto const& vectorType = VectorType::get(static_cast(vector.size()), op.getType()); + auto const& vectorType = VectorType::get(static_cast(vector.size()), mulOp.getType()); + + rewriter.setInsertionPoint(firstOperation(std::begin(vector), std::end(vector))); if (!node->isUniform()) { auto const& elements = results(std::begin(vector), std::end(vector)); @@ -237,11 +201,10 @@ LogicalResult VectorizeMul::matchAndRewrite(SPNMul op, PatternRewriter& rewriter llvm::SmallVector operands; - for (unsigned i = 0; i < op.getNumOperands(); ++i) { + for (unsigned i = 0; i < mulOp.getNumOperands(); ++i) { Value operand; if (std::any_of(std::begin(vector), std::end(vector), [&](auto* vectorOp) { - vectorOp->dump(); - Value vectorOperand = op.getOperand(i); + Value vectorOperand = mulOp.getOperand(i); return vectorOperand.isa() || !parentNodes.count(vectorOperand.getDefiningOp()); })) { llvm::SmallVector elements; @@ -250,36 +213,112 @@ LogicalResult VectorizeMul::matchAndRewrite(SPNMul op, PatternRewriter& rewriter } operand = broadcastFirstInsertRest(std::begin(elements), std::end(elements), vectorType, rewriter); } else { - auto* operandOp = op.getOperand(i).getDefiningOp(); + auto* operandOp = mulOp.getOperand(i).getDefiningOp(); auto* operandNode = parentNodes.lookup(operandOp); if (!vectorsByNode.count(operandNode) || vectorsByNode[operandNode].size() != operandNode->numVectors()) { - return rewriter.notifyMatchFailure(op, "operation's LHS has not yet been (fully) vectorized"); + return rewriter.notifyMatchFailure(mulOp, "operation's LHS has not yet been (fully) vectorized"); } operand = vectorsByNode[operandNode][operandNode->getVectorIndex(operandOp)]->getResult(0); } operands.emplace_back(operand); } - rewriter.setInsertionPointAfterValue(lastValue(std::begin(operands), std::end(operands))); - auto mulOp = rewriter.create(op->getLoc(), vectorType, operands); + auto vectorMulOp = rewriter.create(mulOp->getLoc(), vectorType, operands); - vectorsByNode[node][vectorIndex] = mulOp; + vectorsByNode[node][vectorIndex] = vectorMulOp; return success(); } -LogicalResult VectorizeLog::matchAndRewrite(SPNLog op, PatternRewriter& rewriter) const { +LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, PatternRewriter& rewriter) const { - auto* node = parentNodes.lookup(op); + auto* node = parentNodes.lookup(gaussianOp); - auto const& vectorIndex = node->getVectorIndex(op); + auto const& vectorIndex = node->getVectorIndex(gaussianOp); auto const& vector = node->getVector(vectorIndex); - auto const& vectorType = VectorType::get(static_cast(vector.size()), op.getType()); + auto const& vectorType = VectorType::get(static_cast(vector.size()), gaussianOp.getType()); + + rewriter.setInsertionPoint(firstOperation(std::begin(vector), std::end(vector))); if (!node->isUniform()) { - //vectorsByNode[node][vectorIndex] = broadcastFirstInsertRest(vector, vectorType, rewriter).getDefiningOp(); + auto const& elements = results(std::begin(vector), std::end(vector)); + auto vectorVal = broadcastFirstInsertRest(std::begin(elements), std::end(elements), vectorType, rewriter); + vectorsByNode[node][vectorIndex] = vectorVal.getDefiningOp(); return success(); } + DenseElementsAttr coefficients; + if (vectorType.getElementType().cast().getWidth() == 32) { + SmallVector array; + for (auto* vectorOp : vector) { + float stddev = static_cast(vectorOp).stddev().convertToFloat(); + array.emplace_back(1.0f / (stddev * std::sqrt(2.0f * M_PIf32))); + } + coefficients = DenseElementsAttr::get(vectorType, static_cast>(array)); + } else { + SmallVector array; + for (auto* vectorOp : vector) { + double stddev = static_cast(vectorOp).stddev().convertToDouble(); + array.emplace_back(1.0 / (stddev * std::sqrt(2.0 * M_PI))); + } + coefficients = DenseElementsAttr::get(vectorType, static_cast>(array)); + } + + // Gather means in a dense floating point attribute vector. + SmallVector meanAttributes; + for (auto* vectorOp : vector) { + meanAttributes.emplace_back(static_cast(vectorOp).meanAttr()); + } + auto const& means = denseFloatingPoints(std::begin(meanAttributes), std::end(meanAttributes), vectorType); + + // Gather standard deviations in a dense floating point attribute vector. + SmallVector stddevAttributes; + for (auto* vectorOp : vector) { + stddevAttributes.emplace_back(static_cast(vectorOp).stddevAttr()); + } + auto const& stddevs = denseFloatingPoints(std::begin(stddevAttributes), std::end(stddevAttributes), vectorType); + + // Grab the input vector. + Value inputVector; + if (std::any_of(std::begin(vector), std::end(vector), [&](auto* vectorOp) { + return static_cast(vectorOp).index().template isa(); + })) { + llvm::SmallVector elements; + for (auto* vectorOp : vector) { + elements.emplace_back(static_cast(vectorOp).index()); + } + inputVector = broadcastFirstInsertRest(std::begin(elements), std::end(elements), vectorType, rewriter); + } else { + auto* inputOp = gaussianOp.index().getDefiningOp(); + auto* inputNode = parentNodes.lookup(inputOp); + if (!vectorsByNode.count(inputNode) || vectorsByNode[inputNode].size() != inputNode->numVectors()) { + return rewriter.notifyMatchFailure(gaussianOp, "operation's input node has not yet been (fully) vectorized"); + } + inputVector = vectorsByNode[inputNode][inputNode->getVectorIndex(inputOp)]->getResult(0); + } + + // Calculate Gaussian distribution using e^(-0.5 * ((x - mean) / stddev)^2)) / (stddev * sqrt(2 * PI)) + auto const& gaussianLoc = gaussianOp->getLoc(); + + // (x - mean) + auto meanVector = rewriter.create(gaussianLoc, means); + Value gaussianVector = rewriter.create(gaussianLoc, vectorType, inputVector, meanVector); + + // ((x - mean) / stddev)^2 + auto stddevVector = rewriter.create(gaussianLoc, stddevs); + gaussianVector = rewriter.create(gaussianLoc, vectorType, gaussianVector, stddevVector); + gaussianVector = rewriter.create(gaussianLoc, vectorType, gaussianVector, gaussianVector); + + // e^(-0.5 * ((x - mean) / stddev)^2)) + auto halfVector = rewriter.create(gaussianLoc, denseFloatingPoints(-0.5, vectorType)); + gaussianVector = rewriter.create(gaussianLoc, vectorType, halfVector, gaussianVector); + gaussianVector = rewriter.create(gaussianLoc, gaussianVector); + + // e^(-0.5 * ((x - mean) / stddev)^2)) / (stddev * sqrt(2 * PI)) + auto coefficientVector = rewriter.create(gaussianLoc, coefficients); + gaussianVector = rewriter.create(gaussianLoc, coefficientVector, gaussianVector); + + vectorsByNode[node][vectorIndex] = gaussianVector.getDefiningOp(); + return success(); } From 26d41806adc55bec9bfed513ee4c2dc56e960c1a Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Mon, 19 Apr 2021 22:01:35 +0200 Subject: [PATCH 088/385] SLP vectorization progress is now based on values instead of operations. --- .../Vectorization/SLP/SLPGraphBuilder.h | 10 +- .../LoSPNtoCPU/Vectorization/SLP/SLPMode.h | 2 +- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.h | 82 +++-- .../LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h | 11 +- .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.h | 115 +++--- .../Vectorization/SLP/SLPVectorizationPass.h | 11 +- .../SLP/SLPVectorizationPatterns.h | 36 +- .../Vectorization/SLP/SLPGraphBuilder.cpp | 189 ++++------ .../LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp | 11 +- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp | 231 ++++++------ .../Vectorization/SLP/SLPSeeding.cpp | 75 ++-- .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp | 208 ++++++++++- .../SLP/SLPVectorizationPass.cpp | 193 +--------- .../SLP/SLPVectorizationPatterns.cpp | 343 +++++++++++------- 14 files changed, 798 insertions(+), 719 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h index 0a26463b..c6251f47 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h @@ -22,19 +22,17 @@ namespace mlir { explicit SLPGraphBuilder(size_t maxLookAhead); - std::unique_ptr build(seed_t const& seed) const; + std::unique_ptr build(vector_t const& seed) const; private: - void buildGraph(std::vector const& operations, SLPNode* currentNode) const; + void buildGraph(NodeVector* vector, SLPNode* currentNode) const; void reorderOperands(SLPNode* multinode) const; - std::pair getBest(Mode const& mode, - Operation* last, - std::vector& candidates) const; + std::pair getBest(Mode const& mode, Value const& last, SmallVector& candidates) const; - int getLookAheadScore(Operation* last, Operation* candidate, size_t const& maxLevel) const; + int getLookAheadScore(Value const& last, Value const& candidate, unsigned maxLevel) const; size_t const maxLookAhead; diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.h index 1d5d52a6..b89edad8 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.h @@ -26,7 +26,7 @@ namespace mlir { FAILED }; - Mode modeFromOperation(Operation* operation); + Mode modeFromValue(Value const& value); } } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h index 5b0d451e..20863768 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h @@ -13,56 +13,76 @@ namespace mlir { namespace low { namespace slp { + typedef SmallVector vector_t; + + class NodeVector { + + friend class SLPNode; + + public: + + explicit NodeVector(vector_t const& values); + explicit NodeVector(SmallVector const& operations); + + bool isUniform() const; + bool containsBlockArg() const; + bool contains(Value const& value) const; + + size_t numLanes() const; + + size_t numOperands() const; + NodeVector* getOperand(size_t index) const; + + vector_t::const_iterator begin() const; + vector_t::const_iterator end() const; + + Value const& getElement(size_t lane) const; + Value const& operator[](size_t lane) const; + + private: + vector_t values; + SmallVector> operands; + }; + class SLPNode { public: - explicit SLPNode(std::vector const& operations); + explicit SLPNode(vector_t const& values); + explicit SLPNode(SmallVector const& operations); - Operation* getOperation(size_t lane, size_t index) const; - void setOperation(size_t lane, size_t index, Operation* operation); + Value getValue(size_t lane, size_t index) const; + void setValue(size_t lane, size_t index, Value const& newValue); - bool isMultiNode() const; bool isUniform() const; - bool containsOperation(Operation* op) const; - bool areRootOfNode(std::vector const& operations) const; + bool contains(Value const& value) const; - size_t numLanes() const; + bool isRootOfNode(NodeVector const& vector) const; + size_t numLanes() const; size_t numVectors() const; - void addVector(std::vector const& vectorOps); - std::vector& getVector(size_t index); - size_t getVectorIndex(Operation* op) const; - std::vector& getVectorOf(Operation* op); - std::vector>& getVectors(); - SLPNode* addOperand(std::vector const& operations); + NodeVector* addVector(vector_t const& values, NodeVector* definingVector); + NodeVector* addVector(SmallVector const& operations); + NodeVector* getVector(size_t index) const; + + SLPNode* addOperand(vector_t const& values, NodeVector* definingVector); SLPNode* getOperand(size_t index) const; std::vector getOperands() const; size_t numOperands() const; - - void addNodeInput(Value const& value); - Value const& getNodeInput(size_t index) const; - - void dump() const; - void dumpGraph() const; - - friend bool operator==(SLPNode const& lhs, SLPNode const& rhs) { - return std::tie(lhs.vectors) == std::tie(rhs.vectors); +/* + bool operator==(SLPNode const& other) { + return std::tie(vectors) == std::tie(other.vectors); } - friend bool operator!=(SLPNode const& lhs, SLPNode const& rhs) { - return !(lhs == rhs); + bool operator!=(SLPNode const& other) { + return !(*this == other); } +*/ private: - - std::vector> vectors; - - std::vector> operandNodes; - - std::vector nodeInputs; - + SmallVector> vectors; + SmallVector> operandNodes; }; } } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h index ed5910b2..d9ce4783 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h @@ -7,6 +7,7 @@ #define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPSEEDING_H #include "mlir/IR/Operation.h" +#include "SLPNode.h" #include namespace mlir { @@ -14,8 +15,6 @@ namespace mlir { namespace low { namespace slp { - typedef std::vector seed_t; - enum SearchMode { /// DefBeforeUse, @@ -31,16 +30,10 @@ namespace mlir { explicit SeedAnalysis(Operation* rootOp); - std::map getOpDepths() const; - - std::vector getSeeds(size_t const& op, - std::map const& depthsOf, - SearchMode const& mode) const; + vector_t getSeed(unsigned width, SearchMode const& mode) const; private: - Operation* rootOp; - }; } } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h index fbdbcdc1..166b3c4f 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h @@ -8,78 +8,105 @@ #include "mlir/IR/Operation.h" #include "LoSPN/LoSPNOps.h" +#include "SLPNode.h" namespace mlir { namespace spn { namespace low { namespace slp { - bool areConsecutiveLoads(low::SPNBatchRead load1, low::SPNBatchRead load2); - bool areConsecutiveLoads(std::vector const& loads); + void dumpOpTree(vector_t const& values); + void dumpSLPGraph(SLPNode const& root); + void dumpSLPNode(SLPNode const& node); + void dumpSLPNodeVector(NodeVector const& nodeVector); - template - Operation* firstUser(OperationIterator begin, OperationIterator end) { - Operation* firstUser = nullptr; + bool vectorizable(Operation* op); + bool vectorizable(Value const& value); + + bool isBeforeInBlock(Operation* lhs, Operation* rhs); + bool isBeforeInBlock(Value const& lhs, Value const& rhs); + + bool consecutiveLoads(Value const& lhs, Value const& rhs); + + template + bool vectorizable(ValueIterator begin, ValueIterator end) { + if (begin->template isa()) { + return false; + } + auto const& name = begin->getDefiningOp()->getName(); + ++begin; while (begin != end) { - for (auto* user : (*begin)->getUsers()) { - if (!firstUser || user->isBeforeInBlock(firstUser)) { - firstUser = user; - } + if (!vectorizable(*begin) || begin->getDefiningOp()->getName() != name) { + return false; } ++begin; } - return firstUser; + return true; } - template - Operation* firstOperation(OperationIterator begin, OperationIterator end) { - Operation* firstOp = *begin; - while (++begin != end) { - if (!firstOp->isBeforeInBlock(*begin)) { - firstOp = *begin; + template + bool commutative(ValueIterator begin, ValueIterator end) { + while (begin != end) { + if (begin->template isa() + || !begin->getDefiningOp()->template hasTrait()) { + return false; } + ++begin; } - return firstOp; + return true; } - template - Operation* lastOperation(OperationIterator begin, OperationIterator end) { - Operation* lastOp = *begin; - while (++begin != end) { - if (lastOp->isBeforeInBlock(*begin)) { - lastOp = *begin; + template + bool consecutiveLoads(ValueIterator begin, ValueIterator end) { + Value previous = *begin; + if (++begin == end || previous.isa() || !dyn_cast(previous.getDefiningOp())) { + return false; + } + while (begin != end) { + Value current = *begin; + if (!consecutiveLoads(previous, current)) { + return false; } + previous = current; + ++begin; } - return lastOp; + return true; } - template - Value firstValue(ValueIterator begin, ValueIterator end) { - Value firstVal = *begin; - while (begin != end) { - if (begin->template isa()) { - return *begin; - } - if (!firstVal.getDefiningOp()->isBeforeInBlock(*begin)) { - firstVal = *begin; + template + typename std::iterator_traits::value_type firstOccurrence(Iterator begin, Iterator end) { + typename std::iterator_traits::value_type first = *begin; + while (++begin != end) { + if (isBeforeInBlock(first, *begin)) { + first = *begin; } - ++begin; } - return firstVal; + return first; } - template - Value lastValue(ValueIterator begin, ValueIterator end) { - Value lastVal = *begin; + template + typename std::iterator_traits::value_type lastOccurrence(Iterator begin, Iterator end) { + typename std::iterator_traits::value_type first = *begin; while (++begin != end) { - if (begin->template isa()) { - continue; - } else if (lastVal.isa() - || lastVal.getDefiningOp()->isBeforeInBlock(begin->getDefiningOp())) { - lastVal = *begin; + if (!isBeforeInBlock(first, *begin)) { + first = *begin; + } + } + return first; + } + + template + Operation* firstUser(UsableIterator begin, UsableIterator end) { + Operation* firstUser = nullptr; + while (begin != end) { + for (auto* user : begin->getUsers()) { + if (!firstUser || user->isBeforeInBlock(firstUser)) { + firstUser = user; + } } + ++begin; } - return lastVal; + return firstUser; } } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h index 25be2fa6..0b17c9b5 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h @@ -15,20 +15,21 @@ namespace mlir { namespace low { namespace slp { - struct SLPVectorizationPass : public PassWrapper> { + struct SLPVectorizationPass : public PassWrapper> { public: protected: - void runOnOperation() override; + void runOnOperation() + override; private: - }; - } + }; } - std::unique_ptr createSLPVectorizationPass(); } + std::unique_ptr createSLPVectorizationPass(); +} } #endif //SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPVECTORIZATIONPASS_H diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h index 419ff048..a8798e24 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h @@ -6,11 +6,15 @@ #ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPVECTORIZATIONPATTERNS_H #define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPVECTORIZATIONPATTERNS_H +#include + #include "SLPNode.h" #include "LoSPN/LoSPNDialect.h" #include "LoSPN/LoSPNOps.h" #include "mlir/Dialect/StandardOps/IR/Ops.h" #include "mlir/IR/PatternMatch.h" +#include "mlir/Rewrite/FrozenRewritePatternList.h" +#include "mlir/Rewrite/PatternApplicator.h" #include "llvm/Support/Debug.h" namespace mlir { @@ -18,20 +22,27 @@ namespace mlir { namespace low { namespace slp { + /// A custom PatternRewriter for rewriting SLP node vectors. + class SLPVectorPatternRewriter : public mlir::PatternRewriter { + public: + explicit SLPVectorPatternRewriter(mlir::MLIRContext* ctx); + LogicalResult rewrite(SLPNode* root); + }; + template class SLPVectorizationPattern : public OpRewritePattern { public: SLPVectorizationPattern(MLIRContext* context, PatternBenefit benefit, - llvm::DenseMap const& parentNodes, - llvm::DenseMap>& vectorsByNode) - : OpRewritePattern{ - context, benefit}, parentNodes{parentNodes}, vectorsByNode{vectorsByNode} {} + std::shared_ptr vector, + DenseMap& vectorizedOps) : OpRewritePattern{ + context, benefit}, vector{std::move(vector)}, vectorizedOps{vectorizedOps} {} protected: - llvm::DenseMap const& parentNodes; - llvm::DenseMap>& vectorsByNode; + /// The current vector being transformed. + std::shared_ptr vector; + llvm::DenseMap& vectorizedOps; }; struct VectorizeConstant : public SLPVectorizationPattern { @@ -61,13 +72,12 @@ namespace mlir { static void populateSLPVectorizationPatterns(OwningRewritePatternList& patterns, MLIRContext* context, - llvm::DenseMap const& parentNodes, - llvm::DenseMap>& vectorsByNode) { - patterns.insert(context, 2, parentNodes, vectorsByNode); - patterns.insert(context, 2, parentNodes, vectorsByNode); - patterns.insert(context, 2, parentNodes, vectorsByNode); - patterns.insert(context, 2, parentNodes, vectorsByNode); + std::shared_ptr const& vector, + llvm::DenseMap& vectorizedOps) { + patterns.insert(context, 2, vector, vectorizedOps); + patterns.insert(context, 2, vector, vectorizedOps); + patterns.insert(context, 2, vector, vectorizedOps); + patterns.insert(context, 2, vector, vectorizedOps); } } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp index 1fdc8bd5..32c63368 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp @@ -4,11 +4,8 @@ // #include "mlir/Dialect/StandardOps/IR/Ops.h" -#include "llvm/Support/Debug.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" -#include "LoSPN/LoSPNOps.h" -#include "LoSPN/LoSPNTraits.h" #include using namespace mlir; @@ -16,116 +13,100 @@ using namespace mlir::spn::low::slp; SLPGraphBuilder::SLPGraphBuilder(size_t maxLookAhead) : maxLookAhead{maxLookAhead} {} -std::unique_ptr SLPGraphBuilder::build(seed_t const& seed) const { +std::unique_ptr SLPGraphBuilder::build(vector_t const& seed) const { auto root = std::make_unique(SLPNode{seed}); - buildGraph(seed, root.get()); + buildGraph(root->getVector(0), root.get()); return root; } // Some helper functions in an anonymous namespace. namespace { - bool vectorizable(std::vector const& operations) { - for (size_t i = 0; i < operations.size(); ++i) { - auto* op = operations[i]; - if (!op->hasTrait()) { - return false; - } - if (op->getName() != operations.front()->getName()) { - return false; - } - if (op->getNumResults() != 1) { - return false; - } - // MLIR does not allow index types in vectors. - if (op->getResult(0).getType().isa()) { - return false; - } - } - return true; - } - - bool commutative(std::vector const& operations) { - return std::all_of(std::begin(operations), std::end(operations), [&](Operation* op) { - return op->hasTrait(); - }); - } - - bool escapesMultinode(Operation* op, SLPNode* currentNode) { + bool escapesMultinode(Value const& value, SLPNode* currentNode) { // TODO: determine if multinodes should stop building if an operation escapes it // or if simply disallowing reordering in this lane might be better - return std::any_of(std::begin(op->getUsers()), std::end(op->getUsers()), [&](auto* user) { - return !currentNode->containsOperation(user); + return std::any_of(std::begin(value.getUsers()), std::end(value.getUsers()), [&](auto* user) { + return !currentNode->contains(user->getResult(0)); }); } - std::vector getOperands(Operation* operation) { - std::vector operands; - operands.reserve(operation->getNumOperands()); - for (auto operand : operation->getOperands()) { - operands.emplace_back(operand.getDefiningOp()); + SmallVector getOperands(Value const& value) { + SmallVector operands; + operands.reserve(value.getDefiningOp()->getNumOperands()); + for (auto operand : value.getDefiningOp()->getOperands()) { + operands.emplace_back(operand); } return operands; } - std::vector> getOperands(std::vector const& operations) { - std::vector> allOperands; - allOperands.reserve(operations.size()); - for (auto* operation : operations) { - allOperands.emplace_back(getOperands(operation)); + SmallVector> getAllOperands(NodeVector* vector) { + SmallVector> allOperands; + allOperands.reserve(vector->numLanes()); + for (auto const& value : *vector) { + allOperands.emplace_back(getOperands(value)); } return allOperands; } - void sortByOpcode(std::vector& operations, Optional const& smallestOpcode) { - std::sort(std::begin(operations), std::end(operations), [&](Operation* lhs, Operation* rhs) { + void sortByOpcode(SmallVector& values, Optional const& smallestOpcode) { + std::sort(std::begin(values), std::end(values), [&](Value const& lhs, Value const& rhs) { + auto* lhsOp = lhs.getDefiningOp(); + auto* rhsOp = rhs.getDefiningOp(); + if (!lhsOp && !rhsOp) { + return lhs.cast().getArgNumber() < rhs.cast().getArgNumber(); + } else if (lhsOp && !rhsOp) { + return true; + } else if (!lhsOp && rhsOp) { + return false; + } if (smallestOpcode.hasValue()) { - if (lhs->getName() == smallestOpcode.getValue()) { - return rhs->getName() != smallestOpcode.getValue(); - } else if (rhs->getName() == smallestOpcode.getValue()) { + if (lhsOp->getName() == smallestOpcode.getValue()) { + return rhsOp->getName() != smallestOpcode.getValue(); + } else if (rhsOp->getName() == smallestOpcode.getValue()) { return false; } } - return lhs->getName().getStringRef() < rhs->getName().getStringRef(); + return lhsOp->getName().getStringRef() < rhsOp->getName().getStringRef(); }); } } // end namespace -void SLPGraphBuilder::buildGraph(std::vector const& operations, SLPNode* currentNode) const { +void SLPGraphBuilder::buildGraph(NodeVector* vector, SLPNode* currentNode) const { // Stop growing graph - if (!vectorizable(operations)) { + if (!vectorizable(vector->begin(), vector->end())) { return; } - auto const& currentOpCode = operations.front()->getName(); + auto const& currentOpCode = vector->begin()->getDefiningOp()->getName(); + auto const& arity = vector->begin()->getDefiningOp()->getNumOperands(); // Recursion call to grow graph further // 1. Commutative - if (commutative(operations)) { + if (commutative(vector->begin(), vector->end())) { // A. Coarsening Mode - auto allOperands = getOperands(operations); + auto allOperands = getAllOperands(vector); for (auto& operands : allOperands) { sortByOpcode(operands, currentOpCode); } - for (size_t i = 0; i < operations.front()->getNumOperands(); ++i) { - if (std::all_of(std::begin(allOperands), std::end(allOperands), [&](auto const& operandOperations) { - return operandOperations[i]->getName() == currentOpCode && !escapesMultinode(operandOperations[i], currentNode); + for (size_t i = 0; i < arity; ++i) { + if (std::all_of(std::begin(allOperands), std::end(allOperands), [&](SmallVector const& operands) { + return operands[i].getDefiningOp()->getName() == currentOpCode && !escapesMultinode(operands[i], currentNode); })) { - std::vector vectorOps; + vector_t vectorValues; for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { - vectorOps.emplace_back(allOperands[lane][i]); + vectorValues.emplace_back(allOperands[lane][i]); } - currentNode->addVector(vectorOps); - buildGraph(vectorOps, currentNode); + auto* newVector = currentNode->addVector(vectorValues, vector); + buildGraph(newVector, currentNode); } else { // TODO: here might be a good place to implement variable vector width - std::vector operandOperations; + vector_t operandValues; for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { - operandOperations.emplace_back(allOperands[lane][i]); + operandValues.emplace_back(allOperands[lane][i]); } - currentNode->addOperand(operandOperations); + currentNode->addOperand(operandValues, vector); } } // B. Normal Mode: Finished building multi-node - if (currentNode->isMultiNode() && currentNode->areRootOfNode(operations)) { + if (currentNode->isRootOfNode(*vector)) { reorderOperands(currentNode); for (auto* operandNode : currentNode->getOperands()) { buildGraph(operandNode->getVector(operandNode->numVectors() - 1), operandNode); @@ -134,46 +115,33 @@ void SLPGraphBuilder::buildGraph(std::vector const& operations, SLPN } // 2. Non-Commutative else { - for (size_t i = 0; i < operations.front()->getNumOperands(); ++i) { - std::vector operands; - std::vector operandOperations; - bool usesBlockArguments = false; + for (size_t i = 0; i < arity; ++i) { + vector_t operandValues; for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { - auto operand = currentNode->getOperation(lane, 0)->getOperand(i); - operands.emplace_back(operand); - if (operand.isa()) { - usesBlockArguments = true; - } else { - operandOperations.emplace_back(operand.getDefiningOp()); - } - } - - if (usesBlockArguments || !vectorizable(operandOperations)) { - for (auto const& operand : operands) { - currentNode->addNodeInput(operand); - } - } else { - buildGraph(operandOperations, currentNode->addOperand(operandOperations)); + auto operand = currentNode->getValue(lane, 0).getDefiningOp()->getOperand(i); + operandValues.emplace_back(operand); } + auto* operandNode = currentNode->addOperand(operandValues, vector); + buildGraph(operandNode->getVector(0), operandNode); } } } void SLPGraphBuilder::reorderOperands(SLPNode* multinode) const { auto const& numOperands = multinode->numOperands(); - std::vector> finalOrder{multinode->numLanes()}; - std::vector> mode{multinode->numLanes()}; + SmallVector> finalOrder{multinode->numLanes()}; + SmallVector> mode{multinode->numLanes()}; // 1. Strip first lane for (size_t i = 0; i < numOperands; ++i) { - auto operation = multinode->getOperand(i)->getOperation(0, 0); - finalOrder[0].emplace_back(operation); - mode[0].emplace_back(modeFromOperation(operation)); + auto value = multinode->getOperand(i)->getValue(0, 0); + finalOrder[0].emplace_back(value); + mode[0].emplace_back(modeFromValue(value)); } // 2. For all other lanes, find best candidate for (size_t lane = 1; lane < multinode->numLanes(); ++lane) { - std::vector candidates; + SmallVector candidates; for (auto const& operand : multinode->getOperands()) { - candidates.emplace_back(operand->getOperation(lane, 0)); + candidates.emplace_back(operand->getValue(lane, 0)); } // Look for a matching candidate for (size_t i = 0; i < numOperands; ++i) { @@ -184,19 +152,19 @@ void SLPGraphBuilder::reorderOperands(SLPNode* multinode) const { mode[lane].emplace_back(FAILED); continue; } - auto* last = finalOrder[lane - 1][i]; + auto const& last = finalOrder[lane - 1][i]; auto const& bestResult = getBest(mode[lane - 1][i], last, candidates); // Update output finalOrder[lane].emplace_back(bestResult.first); // Detect SPLAT mode - if (i == 1 && OperationEquivalence::isEquivalentTo(bestResult.first, last)) { + if (i == 1 && bestResult.first == last) { mode[lane][i] = SPLAT; } else { mode[lane].emplace_back(bestResult.second); } } // Distribute remaining candidates in case we encountered a FAILED. - for (auto* candidate : candidates) { + for (auto const& candidate : candidates) { for (size_t i = 0; i < numOperands; ++i) { if (finalOrder[lane][i] == nullptr) { finalOrder[lane][i] = candidate; @@ -207,24 +175,24 @@ void SLPGraphBuilder::reorderOperands(SLPNode* multinode) const { } for (size_t operandIndex = 0; operandIndex < multinode->numOperands(); ++operandIndex) { for (size_t lane = 0; lane < multinode->numLanes(); ++lane) { - multinode->getOperand(operandIndex)->setOperation(lane, 0, finalOrder[lane][operandIndex]); + multinode->getOperand(operandIndex)->setValue(lane, 0, finalOrder[lane][operandIndex]); } } } -std::pair SLPGraphBuilder::getBest(Mode const& mode, - Operation* last, - std::vector& candidates) const { - Operation* best = nullptr; +std::pair SLPGraphBuilder::getBest(Mode const& mode, + Value const& last, + SmallVector& candidates) const { + Value best; Mode resultMode = mode; - std::vector bestCandidates; + SmallVector bestCandidates; if (mode == FAILED) { // Don't select now, let others choose first best = nullptr; } else if (mode == SPLAT) { // Look for other splat candidates for (auto& operand : candidates) { - if (OperationEquivalence::isEquivalentTo(operand, last)) { + if (operand == last) { best = operand; break; } @@ -234,12 +202,13 @@ std::pair SLPGraphBuilder::getBest(Mode const& mode, best = candidates.front(); for (auto& candidate : candidates) { if (mode == LOAD) { - auto load = dyn_cast(candidate); - if (load && areConsecutiveLoads(static_cast(last), load)) { + if (consecutiveLoads(last, candidate)) { + bestCandidates.emplace_back(candidate); + } + } else if (!last.isa() && !candidate.isa()) { + if (last.getDefiningOp()->getName() == candidate.getDefiningOp()->getName()) { bestCandidates.emplace_back(candidate); } - } else if (candidate->getName() == last->getName()) { - bestCandidates.emplace_back(candidate); } } // 1. If we have a trivial solution, use it @@ -284,13 +253,13 @@ std::pair SLPGraphBuilder::getBest(Mode const& mode, return {best, resultMode}; } -int SLPGraphBuilder::getLookAheadScore(Operation* last, Operation* candidate, size_t const& maxLevel) const { +int SLPGraphBuilder::getLookAheadScore(Value const& last, Value const& candidate, unsigned maxLevel) const { if (maxLevel == 0) { - if (last->getName() != candidate->getName()) { + if (last.getDefiningOp()->getName() != candidate.getDefiningOp()->getName()) { return 0; } - if (dyn_cast(last)) { - return areConsecutiveLoads(static_cast(last), static_cast(candidate)) ? 1 : 0; + if (last.getDefiningOp()) { + return consecutiveLoads(last, candidate) ? 1 : 0; } return 1; } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp index 810b4207..85f8e427 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp @@ -7,12 +7,15 @@ #include "LoSPNtoCPU/Vectorization/SLP/SLPMode.h" using namespace mlir; -using namespace mlir::spn::low::slp; -Mode mlir::spn::low::slp::modeFromOperation(Operation* operation) { - if (operation->hasTrait()) { +mlir::spn::low::slp::Mode mlir::spn::low::slp::modeFromValue(Value const& value) { + if (value.isa()) { + return SPLAT; + } + auto* definingOp = value.getDefiningOp(); + if (definingOp->hasTrait()) { return CONST; - } else if (dyn_cast(operation)) { + } else if (dyn_cast(definingOp)) { return LOAD; } return OPCODE; diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp index e00921fa..d267d319 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp @@ -6,86 +6,146 @@ #include "LoSPNtoCPU/Vectorization/SLP/SLPNode.h" #include "LoSPN/LoSPNOps.h" #include "mlir/Dialect/StandardOps/IR/Ops.h" -#include "llvm/Support/Debug.h" -#include using namespace mlir; using namespace mlir::spn; +using namespace mlir::spn::low; using namespace mlir::spn::low::slp; -SLPNode::SLPNode(std::vector const& operations) { +// === NodeVector === + +NodeVector::NodeVector(vector_t const& values) { + assert(!values.empty()); + for (auto const& value : values) { + assert(value.isa() || value.getDefiningOp()->hasTrait()); + this->values.emplace_back(value); + } +} + +NodeVector::NodeVector(SmallVector const& operations) { assert(!operations.empty()); - vectors.emplace_back(operations); + for (auto* op : operations) { + assert(op->hasTrait()); + values.emplace_back(op->getResult(0)); + } } -Operation* SLPNode::getOperation(size_t lane, size_t index) const { - assert(lane <= numLanes() && index <= numVectors()); - return vectors[index][lane]; +bool NodeVector::isUniform() const { + return std::all_of(std::begin(values), std::end(values), [&](Value const& value) { + if (value.isa()) { + return false; + } + return value.getDefiningOp()->getName() == values.front().getDefiningOp()->getName(); + }); +} + +bool NodeVector::containsBlockArg() const { + return std::any_of(std::begin(values), std::end(values), [&](Value const& value) { + return value.isa(); + }); +} + +bool NodeVector::contains(Value const& value) const { + return std::find(std::begin(values), std::end(values), value) != std::end(values); +} + +size_t NodeVector::numLanes() const { + return values.size(); +} + +size_t NodeVector::numOperands() const { + return operands.size(); +} + +NodeVector* NodeVector::getOperand(size_t index) const { + assert(index < operands.size()); + return operands[index].get(); +} + +vector_t::const_iterator NodeVector::begin() const { + return values.begin(); +} + +vector_t::const_iterator NodeVector::end() const { + return values.end(); +} + +Value const& NodeVector::getElement(size_t lane) const { + return this->operator[](lane); +} + +Value const& NodeVector::operator[](size_t lane) const { + assert(lane < numLanes()); + return values[lane]; +} + +// === SLPNode === + +SLPNode::SLPNode(SmallVector const& operations) { + addVector(operations); } -void SLPNode::setOperation(size_t lane, size_t index, Operation* operation) { +SLPNode::SLPNode(vector_t const& values) { + addVector(values, nullptr); +} + +Value SLPNode::getValue(size_t lane, size_t index) const { assert(lane <= numLanes() && index <= numVectors()); - vectors[index][lane] = operation; + return vectors[index]->values[lane]; } -bool SLPNode::isMultiNode() const { - return numVectors() > 1; +void SLPNode::setValue(size_t lane, size_t index, Value const& newValue) { + assert(lane <= numLanes() && index <= numVectors()); + vectors[index]->values[lane] = newValue; } bool SLPNode::isUniform() const { - return std::all_of(std::begin(vectors), std::end(vectors), [&](auto const& operations) { - return operations[0]->getName() == vectors[0][0]->getName(); + return std::all_of(std::begin(vectors), std::end(vectors), [&](auto const& nodeVector) { + return nodeVector->isUniform(); }); } -bool SLPNode::containsOperation(Operation* op) const { - return std::any_of(std::begin(vectors), std::end(vectors), [&](auto const& vectorOps) { - return std::find(std::begin(vectorOps), std::end(vectorOps), op) != std::end(vectorOps); +bool SLPNode::contains(Value const& value) const { + return std::any_of(std::begin(vectors), std::end(vectors), [&](auto const& nodeVector) { + return nodeVector->contains(value); }); } -bool SLPNode::areRootOfNode(std::vector const& operations) const { - return vectors[0] == operations; +bool SLPNode::isRootOfNode(NodeVector const& vector) const { + return vectors[0]->values == vector.values; } size_t SLPNode::numLanes() const { - return vectors[0].size(); + return vectors[0]->numLanes(); } size_t SLPNode::numVectors() const { return vectors.size(); } -void SLPNode::addVector(std::vector const& vectorOps) { - assert(vectorOps.size() == numLanes()); - vectors.emplace_back(vectorOps); -} - -std::vector& SLPNode::getVector(size_t index) { - assert(index <= numVectors()); - return vectors[index]; -} - -size_t SLPNode::getVectorIndex(Operation* op) const { - for (size_t i = 0; i < vectors.size(); ++i) { - if (std::find(std::begin(vectors[i]), std::end(vectors[i]), op) != std::end(vectors[i])) { - return i; - } +NodeVector* SLPNode::addVector(vector_t const& values, NodeVector* definingVector) { + auto* newVector = vectors.emplace_back(std::make_shared(values)).get(); + if (definingVector) { + definingVector->operands.emplace_back(newVector); } - assert(false && "node does not contain the given operation"); + return newVector; } -std::vector& SLPNode::getVectorOf(Operation* op) { - return vectors[getVectorIndex(op)]; +NodeVector* SLPNode::addVector(SmallVector const& operations) { + return vectors.emplace_back(std::make_shared(operations)).get(); } -std::vector>& SLPNode::getVectors() { - return vectors; +NodeVector* SLPNode::getVector(size_t index) const { + assert(index <= numVectors()); + return vectors[index].get(); } -SLPNode* SLPNode::addOperand(std::vector const& operations) { - operandNodes.emplace_back(std::make_unique(operations)); - return operandNodes.back().get(); +SLPNode* SLPNode::addOperand(vector_t const& values, NodeVector* definingVector) { + auto* operandNode = operandNodes.emplace_back(std::make_unique(values)).get(); + if (definingVector) { + definingVector->operands.emplace_back(operandNode->vectors.front()); + } + return operandNode; } SLPNode* SLPNode::getOperand(size_t index) const { @@ -105,90 +165,3 @@ std::vector SLPNode::getOperands() const { size_t SLPNode::numOperands() const { return operandNodes.size(); } - -void SLPNode::addNodeInput(Value const& value) { - nodeInputs.emplace_back(value); -} - -Value const& SLPNode::getNodeInput(size_t index) const { - return nodeInputs[index]; -} - -void SLPNode::dump() const { - for (size_t i = numVectors(); i-- > 0;) { - for (size_t lane = 0; lane < numLanes(); ++lane) { - llvm::dbgs() << *getOperation(lane, i) << "(" << getOperation(lane, i) << ")"; - if (lane < numLanes() - 1) { - llvm::dbgs() << "\t|\t"; - } - } - llvm::dbgs() << "\n"; - } -} - -// Helper functions in an anonymous namespace. -namespace { - void dumpBlockArgOrDefiningAddress(Value const& val) { - if (auto* definingOp = val.getDefiningOp()) { - llvm::dbgs() << definingOp; - } else { - llvm::dbgs() << "block arg #" << val.cast().getArgNumber(); - } - } -} - -void SLPNode::dumpGraph() const { - - llvm::dbgs() << "digraph debug_graph {\n"; - llvm::dbgs() << "rankdir = BT;\n"; - llvm::dbgs() << "node[shape=box];\n"; - - std::queue worklist; - worklist.emplace(this); - - while (!worklist.empty()) { - auto const* node = worklist.front(); - worklist.pop(); - - llvm::dbgs() << "node_" << node << "[label=<\n"; - llvm::dbgs() << "\t\n"; - for (size_t i = node->numVectors(); i-- > 0;) { - llvm::dbgs() << "\t\t\n"; - for (size_t lane = 0; lane < node->numLanes(); ++lane) { - auto* operation = node->getOperation(lane, i); - llvm::dbgs() << "\t\t\t"; - if (lane < node->numLanes() - 1) { - llvm::dbgs() << ""; - } - llvm::dbgs() << "\n"; - } - llvm::dbgs() << "\t\t\n"; - } - llvm::dbgs() << "\t
"; - llvm::dbgs() << "" << operation->getName() << ""; - // --- Additional operation information --- - llvm::dbgs() << "
"; - llvm::dbgs() << "(" << operation << ")"; - if (auto constOp = dyn_cast(operation)) { - llvm::dbgs() << "
value: " << constOp.getValue(); - } else if (auto lowConstOp = dyn_cast(operation)) { - llvm::dbgs() << "
value: " << lowConstOp.value().convertToDouble(); - } else if (auto readOp = dyn_cast(operation)) { - llvm::dbgs() << "
mem: "; - dumpBlockArgOrDefiningAddress(readOp.batchMem()); - llvm::dbgs() << "
batch: "; - dumpBlockArgOrDefiningAddress(readOp.batchIndex()); - llvm::dbgs() << "
sample: " << readOp.sampleIndex(); - } - llvm::dbgs() << ""; - // --- ================================ --- - llvm::dbgs() << "
\n"; - llvm::dbgs() << ">];\n"; - - for (auto const& operand : node->getOperands()) { - llvm::dbgs() << "node_" << node << "->" << "node_" << operand << ";\n"; - worklist.emplace(operand); - } - } - llvm::dbgs() << "}\n"; -} diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp index 6c685517..e03f5f12 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp @@ -4,58 +4,61 @@ // #include "LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h" -#include "llvm/ADT/StringMap.h" -#include "mlir/Dialect/Traits.h" +#include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" using namespace mlir; using namespace mlir::spn::low::slp; SeedAnalysis::SeedAnalysis(Operation* rootOp) : rootOp{rootOp} {} -std::map SeedAnalysis::getOpDepths() const { - - std::map opDepths; - - rootOp->walk([&](Operation* op) { - if (!opDepths.count(op)) { - opDepths[op] = 0; - } - for (auto* use : op->getUsers()) { - if (!opDepths.count(use) || opDepths[op] + 1 > opDepths[use]) { - opDepths[use] = opDepths[op] + 1; +// Helper functions in anonymous namespace. +namespace { + DenseMap getOpDepths(Operation* rootOp) { + DenseMap opDepths; + rootOp->walk([&](Operation* op) { + if (!vectorizable(op)) { + return; } - } - }); - - return opDepths; + auto const& value = op->getResult(0); + if (!opDepths.count(value)) { + opDepths[value] = 0; + } + for (auto const& use : op->getUses()) { + if (!opDepths.count(use.get()) || opDepths[value] + 1 > opDepths[use.get()]) { + opDepths[use.get()] = opDepths[value] + 1; + } + } + }); + return opDepths; + } } -std::vector SeedAnalysis::getSeeds(size_t const& width, - std::map const& depthsOf, - SearchMode const& mode) const { +vector_t SeedAnalysis::getSeed(unsigned width, SearchMode const& mode) const { - llvm::StringMap> seedsByOpName; + llvm::StringMap> seedsByOpName; + auto const& opDepths = getOpDepths(rootOp); - rootOp->walk([&](Operation* op) { - auto depth = depthsOf.at(op); - if (!op->hasTrait() || depth < log2(width)) { - return; + for (auto const& entry : opDepths) { + auto const& value = entry.first; + auto const& depth = entry.second; + if (depth < log2(width)) { + continue; } bool needsNewSeed = true; - for (auto& seed : seedsByOpName[op->getName().getStringRef()]) { - if (seed.size() < width && depthsOf.at(seed.front()) == depth) { - seed.emplace_back(op); + for (auto& seed : seedsByOpName[value.getDefiningOp()->getName().getStringRef()]) { + if (seed.size() < width && opDepths.lookup(seed.front()) == depth) { + seed.emplace_back(entry.first); needsNewSeed = false; break; } } if (needsNewSeed) { - seed_t seed{op}; - seedsByOpName[op->getName().getStringRef()].emplace_back(seed); + vector_t seed{value}; + seedsByOpName[value.getDefiningOp()->getName().getStringRef()].emplace_back(seed); } - }); + } - std::vector seeds; + SmallVector seeds; // Flatten the map that maps opcode to seeds. for (auto const& entry : seedsByOpName) { for (auto const& seed : entry.second) { @@ -67,16 +70,16 @@ std::vector SeedAnalysis::getSeeds(size_t const& width, } // Sort the seeds such that either the seeds closest to the beginning of the function come first (DefBeforeUse), // or those closest to the return statement (UseBeforeDef). - std::sort(seeds.begin(), seeds.end(), [&](seed_t const& seed1, seed_t const& seed2) { + std::sort(seeds.begin(), seeds.end(), [&](auto const& seed1, auto const& seed2) { if (mode == DefBeforeUse) { - return depthsOf.at(seed1.front()) < depthsOf.at(seed2.front()); + return opDepths.lookup(seed1.front()) < opDepths.lookup(seed2.front()); } else if (mode == UseBeforeDef) { - return depthsOf.at(seed1.front()) > depthsOf.at(seed2.front()); + return opDepths.lookup(seed1.front()) > opDepths.lookup(seed2.front()); } else { // Unused so far. assert(false); } }); - return seeds; + return seeds.front(); } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp index e122fc70..a3202ffd 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp @@ -4,35 +4,211 @@ // #include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" +#include "mlir/Dialect/StandardOps/IR/Ops.h" +#include "llvm/ADT/SmallPtrSet.h" +#include +#include using namespace mlir; -bool mlir::spn::low::slp::areConsecutiveLoads(low::SPNBatchRead load1, low::SPNBatchRead load2) { - if (load1 == load2) { +bool mlir::spn::low::slp::vectorizable(Operation* op) { + // (MLIR does not allow index types in vectors) + return op->hasTrait() && op->hasTrait() + && !op->getResult(0).getType().isa(); +} + +bool mlir::spn::low::slp::vectorizable(Value const& value) { + // Block arguments don't have defining ops (they can still be put in a vector by other means). + if (value.isa()) { + return false; + } + return vectorizable(value.getDefiningOp()); +} + +bool mlir::spn::low::slp::isBeforeInBlock(Operation* lhs, Operation* rhs) { + return lhs->isBeforeInBlock(rhs); +} + +bool mlir::spn::low::slp::isBeforeInBlock(Value const& lhs, Value const& rhs) { + if (lhs.isa()) { + return !rhs.isa(); + } else if (rhs.isa()) { + return false; + } + return lhs.getDefiningOp()->isBeforeInBlock(rhs.getDefiningOp()); +} + +bool mlir::spn::low::slp::consecutiveLoads(Value const& lhs, Value const& rhs) { + if (lhs == rhs || lhs.isa() || rhs.isa()) { return false; } - if (load1.batchMem() != load2.batchMem()) { + auto lhsLoad = dyn_cast(lhs.getDefiningOp()); + auto rhsLoad = dyn_cast(rhs.getDefiningOp()); + if (!lhsLoad || !rhsLoad) { return false; } - if (load1.batchIndex() != load2.batchIndex()) { + if (lhsLoad.batchMem() != rhsLoad.batchMem()) { return false; } - return load1.sampleIndex() + 1 == load2.sampleIndex(); + if (lhsLoad.batchIndex() != rhsLoad.batchIndex()) { + return false; + } + if (lhsLoad.sampleIndex() + 1 != rhsLoad.sampleIndex()) { + return false; + } + return true; +} + +void mlir::spn::low::slp::dumpSLPNode(mlir::spn::low::slp::SLPNode const& node) { + for (size_t i = node.numVectors(); i-- > 0;) { + dumpSLPNodeVector(*node.getVector(i)); + llvm::dbgs() << "\n"; + } } -bool mlir::spn::low::slp::areConsecutiveLoads(std::vector const& loads) { - for (size_t i = 0; i < loads.size() - 1; ++i) { - auto loadOp1 = dyn_cast(loads[i]); - if (!loadOp1) { - return false; +void mlir::spn::low::slp::dumpSLPNodeVector(const mlir::spn::low::slp::NodeVector& nodeVector) { + for (size_t lane = 0; lane < nodeVector.numLanes(); ++lane) { + llvm::dbgs() << nodeVector[lane]; + if (!nodeVector[lane].isa()) { + llvm::dbgs() << "(" << nodeVector[lane].getDefiningOp() << ")"; } - auto loadOp2 = dyn_cast(loads[i + 1]); - if (!loadOp2) { - return false; + if (lane < nodeVector.numLanes() - 1) { + llvm::dbgs() << "\t|\t"; } - if (!areConsecutiveLoads(loadOp1, loadOp2)) { - return false; + } +} + +// Helper functions in an anonymous namespace. +namespace { + void dumpBlockArgOrDefiningAddress(Value const& val) { + if (auto* definingOp = val.getDefiningOp()) { + llvm::dbgs() << definingOp; + } else { + llvm::dbgs() << "block arg #" << val.cast().getArgNumber(); } } - return true; + void dumpBlockArgOrDefiningOpName(Value const& val) { + if (auto* definingOp = val.getDefiningOp()) { + llvm::dbgs() << definingOp->getName(); + } else { + llvm::dbgs() << "block arg #" << val.cast().getArgNumber(); + } + } +} + +void mlir::spn::low::slp::dumpOpTree(const vector_t& values) { + DenseMap nodes; + SmallVector> edges; + + std::stack worklist; + for (auto const& value : values) { + worklist.push(value); + } + + while (!worklist.empty()) { + auto value = worklist.top(); + worklist.pop(); + if (nodes.count(value)) { + continue; + } + nodes[value] = nodes.size(); + if (auto* definingOp = value.getDefiningOp()) { + for (size_t i = 0; i < definingOp->getNumOperands(); ++i) { + auto const& operand = definingOp->getOperand(i); + edges.emplace_back(std::make_tuple(value, operand, i)); + worklist.push(operand); + } + } + } + + llvm::dbgs() << "digraph debug_graph {\n"; + llvm::dbgs() << "rankdir = BT;\n"; + llvm::dbgs() << "node[shape=box];\n"; + for (auto const& entry : nodes) { + auto const& value = entry.first; + auto const& id = entry.second; + llvm::dbgs() << "node_" << id << "[label=\""; + if (auto* definingOp = value.getDefiningOp()) { + llvm::dbgs() << definingOp->getName().getStringRef() << "\\n" << definingOp; + if (auto constantOp = dyn_cast(definingOp)) { + if (constantOp.value().getType().isIntOrIndex()) { + llvm::dbgs() << "\\nvalue: " << std::to_string(constantOp.value().dyn_cast().getInt()); + } else if (constantOp.value().getType().isIntOrFloat()) { + llvm::dbgs() << "\\nvalue: " << std::to_string(constantOp.value().dyn_cast().getValueAsDouble()); + } + } else if (auto batchReadOp = dyn_cast(definingOp)) { + llvm::dbgs() << "\\nbatch mem: " << batchReadOp.batchMem().dyn_cast().getArgNumber(); + llvm::dbgs() << "\\nbatch index: " << batchReadOp.batchMem().dyn_cast().getArgNumber(); + llvm::dbgs() << "\\nsample index: " << batchReadOp.sampleIndex(); + } + } else { + dumpBlockArgOrDefiningAddress(value); + } + llvm::dbgs() << "\", fillcolor=\"#a0522d\"];\n"; + } + for (auto const& edge : edges) { + llvm::dbgs() << "node_" << std::get<0>(edge) << " -> node_" << std::get<1>(edge) << "[label=\"" + << std::to_string(std::get<2>(edge)) << "\"];\n"; + } + llvm::dbgs() << "}\n"; +} + +void mlir::spn::low::slp::dumpSLPGraph(SLPNode const& root) { + + llvm::dbgs() << "digraph debug_graph {\n"; + llvm::dbgs() << "rankdir = BT;\n"; + llvm::dbgs() << "node[shape=box];\n"; + + std::queue worklist; + worklist.emplace(&root); + + while (!worklist.empty()) { + auto const* node = worklist.front(); + worklist.pop(); + + llvm::dbgs() << "node_" << node << "[label=<\n"; + llvm::dbgs() << "\t\n"; + for (size_t i = node->numVectors(); i-- > 0;) { + llvm::dbgs() << "\t\t\n"; + for (size_t lane = 0; lane < node->numLanes(); ++lane) { + auto value = node->getValue(lane, i); + llvm::dbgs() << "\t\t\t"; + if (lane < node->numLanes() - 1) { + llvm::dbgs() << ""; + } + llvm::dbgs() << "\n"; + } + llvm::dbgs() << "\t\t\n"; + } + llvm::dbgs() << "\t
"; + llvm::dbgs() << ""; + dumpBlockArgOrDefiningOpName(value); + llvm::dbgs() << ""; + // --- Additional operation information --- + if (auto* definingOp = value.getDefiningOp()) { + llvm::dbgs() << "
"; + llvm::dbgs() << "(" << definingOp << ")"; + if (auto constOp = dyn_cast(definingOp)) { + llvm::dbgs() << "
value: " << constOp.getValue(); + } else if (auto lowConstOp = dyn_cast(definingOp)) { + llvm::dbgs() << "
value: " << lowConstOp.value().convertToDouble(); + } else if (auto readOp = dyn_cast(definingOp)) { + llvm::dbgs() << "
mem: "; + dumpBlockArgOrDefiningAddress(readOp.batchMem()); + llvm::dbgs() << "
batch: "; + dumpBlockArgOrDefiningAddress(readOp.batchIndex()); + llvm::dbgs() << "
sample: " << readOp.sampleIndex(); + } + llvm::dbgs() << ""; + } + // --- ================================ --- + llvm::dbgs() << "
\n"; + llvm::dbgs() << ">];\n"; + + for (auto const& operand : node->getOperands()) { + llvm::dbgs() << "node_" << node << "->" << "node_" << operand << ";\n"; + worklist.emplace(operand); + } + } + llvm::dbgs() << "}\n"; } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp index 02b65bb5..0909780f 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp @@ -10,104 +10,9 @@ #include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h" #include "LoSPNtoCPU/Vectorization/TargetInformation.h" #include "mlir/Dialect/Linalg/IR/LinalgOps.h" -#include "mlir/Dialect/Vector/VectorOps.h" -#include "mlir/Rewrite/PatternApplicator.h" -#include using namespace mlir::spn::low::slp; -// Anonymous namespace for helper functions. -namespace { - - /// For debugging purposes. - void printSeedTree(std::vector const& operations) { - std::vector nodes; - std::vector> edges; - - std::stack worklist; - for (auto& op : operations) { - worklist.push(op); - } - - while (!worklist.empty()) { - - auto op = worklist.top(); - worklist.pop(); - - if (std::find(std::begin(nodes), std::end(nodes), op) == nodes.end()) { - nodes.emplace_back(op); - for (size_t i = 0; i < op->getNumOperands(); ++i) { - auto const& operand = op->getOperand(i); - if (operand.getDefiningOp() != nullptr) { - edges.emplace_back(std::make_tuple(op, operand.getDefiningOp(), i)); - worklist.push(operand.getDefiningOp()); - } - } - } - } - - llvm::dbgs() << "digraph debug_graph {\n"; - llvm::dbgs() << "rankdir = BT;\n"; - llvm::dbgs() << "node[shape=box];\n"; - for (auto& op : nodes) { - llvm::dbgs() << "node_" << op << "[label=\"" << op->getName().getStringRef() << "\\n" << op; - if (auto constantOp = llvm::dyn_cast(op)) { - if (constantOp.value().getType().isIntOrIndex()) { - llvm::dbgs() << "\\nvalue: " << std::to_string(constantOp.value().dyn_cast().getInt()); - } else if (constantOp.value().getType().isIntOrFloat()) { - llvm::dbgs() << "\\nvalue: " - << std::to_string(constantOp.value().dyn_cast().getValueAsDouble()); - } - } else if (auto batchReadOp = llvm::dyn_cast(op)) { - llvm::dbgs() << "\\nbatch mem: " << batchReadOp.batchMem().dyn_cast().getArgNumber(); - llvm::dbgs() << "\\nbatch index: " << batchReadOp.batchMem().dyn_cast().getArgNumber(); - llvm::dbgs() << "\\nsample index: " << batchReadOp.sampleIndex(); - } - llvm::dbgs() << "\", fillcolor=\"#a0522d\"];\n"; - } - for (auto& edge : edges) { - llvm::dbgs() << "node_" << std::get<0>(edge) << " -> node_" << std::get<1>(edge) << "[label=\"" - << std::to_string(std::get<2>(edge)) << "\"];\n"; - } - llvm::dbgs() << "}\n"; - } - - llvm::DenseMap computeParentNodesMapping(SLPNode* root) { - llvm::DenseMap parentMapping; - std::queue worklist; - worklist.emplace(root); - while (!worklist.empty()) { - auto* node = worklist.front(); - worklist.pop(); - for (auto& vector : node->getVectors()) { - for (auto* op : vector) { - parentMapping[op] = node; - } - } - for (auto const& operand : node->getOperands()) { - worklist.emplace(operand); - } - } - return parentMapping; - } - - llvm::SmallVector postOrder(SLPNode* root) { - llvm::SmallVector order; - for (auto* operand : root->getOperands()) { - order.append(postOrder(operand)); - } - order.emplace_back(root); - return order; - } - - /// A custom PatternRewriter that does not fold operations. - class NoFoldPatternRewriter : public mlir::PatternRewriter { - public: - explicit NoFoldPatternRewriter(mlir::MLIRContext* ctx) : PatternRewriter(ctx) {} - }; - -} // End anonymous namespace. - void SLPVectorizationPass::runOnOperation() { llvm::StringRef funcName = getOperation().getName(); if (!funcName.contains("task_")) { @@ -121,103 +26,21 @@ void SLPVectorizationPass::runOnOperation() { auto computationType = function.getArguments().back().getType().dyn_cast().getElementType(); auto width = TargetInformation::nativeCPUTarget().getHWVectorEntries(computationType); - auto seeds = seedAnalysis.getSeeds(width, seedAnalysis.getOpDepths(), SearchMode::UseBeforeDef); - assert(!seeds.empty() && "couldn't find a seed!"); - // printSeedTree(seeds.front()); + auto const& seed = seedAnalysis.getSeed(width, SearchMode::UseBeforeDef); + assert(!seed.empty() && "couldn't find a seed!"); + dumpOpTree(seed); SLPGraphBuilder builder{3}; - auto graph = builder.build(seeds.front()); - // graph->dumpGraph(); + auto graph = builder.build(seed); + dumpSLPGraph(*graph); // ==== // - OwningRewritePatternList patterns; - auto const& parentMapping = computeParentNodesMapping(graph.get()); - llvm::DenseMap> vectorsByNode; - populateSLPVectorizationPatterns(patterns, &getContext(), parentMapping, vectorsByNode); - FrozenRewritePatternList frozenPatterns(std::move(patterns)); - - // Traverse the SLP graph in postorder and apply the vectorization patterns. - NoFoldPatternRewriter rewriter(&getContext()); - PatternApplicator applicator(frozenPatterns); - applicator.applyDefaultCostModel(); - - // Marks operations that can be deleted. - // We delete them *after* SLP graph conversion to avoid running into NULL operands during conversion. - llvm::SmallPtrSet erasableOps; - for (auto* node : postOrder(graph.get())) { - - // Stores escaping uses for vector extractions that might be necessary later on. - llvm::DenseMap> escapingUses; - - // Also traverse nodes in postorder to properly handle multinodes. - auto it = node->getVectors().rbegin(); - while (it != node->getVectors().rend()) { - - // Gather escaping uses *now* because the conversion process adds more that *do not* need extractions. - for (size_t lane = 0; lane < it->size(); ++lane) { - auto* vectorOp = (*it)[lane]; - for (auto* user : vectorOp->getUsers()) { - if (!parentMapping.count(user)) { - escapingUses[vectorOp].insert(user); - } - } - } - - // Rewrite vector by applying any matching pattern. - vectorsByNode[node].resize(node->numVectors()); - auto result = applicator.matchAndRewrite(it->front(), rewriter); - if (result.failed()) { - it->front()->emitOpError("SLP pattern application failed (did you forget to specify the pattern?)"); - } - ++it; - } - - // Gather operations that can be erased and create vector extractions using those that need to stay. - for (size_t vectorIndex = node->numVectors(); vectorIndex-- > 0;) { - auto const& vector = node->getVector(vectorIndex); - for (size_t lane = 0; lane < vector.size(); ++lane) { - auto* vectorOp = vector[lane]; - bool erasable = true; - for (auto& use : vectorOp->getUses()) { - auto* user = use.getOwner(); - if (!parentMapping.count(user)) { - erasable = false; - // Filters out operations that were created during the conversion process. - if (!escapingUses[vectorOp].contains(user)) { - continue; - } - auto const& source = vectorsByNode[node][vectorIndex]->getResult(0); - rewriter.setInsertionPoint(user); - auto extractOp = rewriter.create(vectorOp->getLoc(), source, lane); - auto const& oldOperand = user->getOperand(use.getOperandNumber()); - user->setOperand(use.getOperandNumber(), extractOp); - if (oldOperand.getUses().empty() && !oldOperand.isa()) { - erasableOps.insert(oldOperand.getDefiningOp()); - } - } - } - if (erasable) { - erasableOps.insert(vectorOp); - } - } - } - } - - for (auto* op : erasableOps) { - op->dropAllUses(); - rewriter.eraseOp(op); + SLPVectorPatternRewriter rewriter(&getContext()); + if (failed(rewriter.rewrite(graph.get()))) { + signalPassFailure(); } getOperation()->dump(); - // If an SLP node failed to vectorize completely, fail the pass. - for (auto const& entry: vectorsByNode) { - if (entry.first->numVectors() != entry.second.size()) { - llvm::dbgs() << "Failed to vectorize node:\n"; - entry.first->dump(); - signalPassFailure(); - } - } - } std::unique_ptr mlir::spn::createSLPVectorizationPass() { diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp index bf795bc6..950682de 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp @@ -3,12 +3,14 @@ // Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. // -#include "mlir/IR/BlockAndValueMapping.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" -#include "llvm/Support/FormatVariadic.h" #include "mlir/Dialect/Vector/VectorOps.h" #include "mlir/Dialect/Math/IR/Math.h" +#include "mlir/IR/BlockAndValueMapping.h" +#include "llvm/ADT/SmallPtrSet.h" +#include "llvm/Support/FormatVariadic.h" +#include using namespace mlir; using namespace mlir::spn; @@ -31,16 +33,6 @@ namespace { return vectorOp; } - template - SmallVector results(OperationIterator begin, OperationIterator end) { - SmallVector elements; - while (begin != end) { - elements.emplace_back((*begin)->getResult(0)); - ++begin; - } - return elements; - } - template DenseElementsAttr denseFloatingPoints(AttributeIterator begin, AttributeIterator end, VectorType const& vectorType) { if (vectorType.getElementType().cast().getWidth() == 32) { @@ -75,52 +67,183 @@ namespace { return DenseElementsAttr::get(vectorType, static_cast>(array)); } + llvm::SmallVector postOrder(SLPNode* root) { + llvm::SmallVector order; + for (auto* operand : root->getOperands()) { + assert(std::find(std::begin(order), std::end(order), operand) == std::end(order)); + order.append(postOrder(operand)); + } + order.emplace_back(root); + return order; + } + + llvm::DenseMap> getEscapingLanesMap(SLPNode* root) { + llvm::DenseMap> outsideUses; + for (auto* node : postOrder(root)) { + for (size_t i = 0; i < node->numVectors(); ++i) { + auto* vector = node->getVector(i); + for (size_t lane = 0; lane < vector->numLanes(); ++lane) { + auto const& element = vector->getElement(lane); + // Skip duplicate (splat) values. + if (outsideUses[vector].count(element)) { + continue; + } + outsideUses[vector][element] = std::distance(std::begin(element.getUses()), std::end(element.getUses())); + for (size_t j = 0; j < vector->numOperands(); ++j) { + auto* operand = vector->getOperand(j); + outsideUses[operand][operand->getElement(lane)]--; + } + } + } + } + + llvm::DenseMap> escapingLanes; + for (auto const& entry : outsideUses) { + auto* vector = entry.first; + for (auto const& useAndCount : entry.second) { + auto const& value = useAndCount.first; + auto const& count = useAndCount.second; + if (count > 0) { + for (size_t lane = 0; lane < vector->numLanes(); ++lane) { + if (vector->getElement(lane) == value) { + escapingLanes[vector].insert(lane); + break; + } + } + } + } + } + return escapingLanes; + } + + DenseMap emptyVectorizedOpsMap(SLPNode* root) { + DenseMap vectorizedOps; + std::queue worklist; + worklist.emplace(root); + while (!worklist.empty()) { + auto* node = worklist.front(); + worklist.pop(); + for (size_t i = 0; i < node->numVectors(); ++i) { + vectorizedOps[node->getVector(i)] = nullptr; + } + for (auto const& operand : node->getOperands()) { + worklist.emplace(operand); + } + } + return vectorizedOps; + } + } -LogicalResult VectorizeConstant::matchAndRewrite(ConstantOp constantOp, PatternRewriter& rewriter) const { +SLPVectorPatternRewriter::SLPVectorPatternRewriter(mlir::MLIRContext* ctx) : PatternRewriter{ctx} {} + +LogicalResult SLPVectorPatternRewriter::rewrite(SLPNode* root) { + + OwningRewritePatternList patterns; + auto vectorizedOps = emptyVectorizedOpsMap(root); + std::shared_ptr currentVector; + populateSLPVectorizationPatterns(patterns, context, currentVector, vectorizedOps); + FrozenRewritePatternList frozenPatterns(std::move(patterns)); + + PatternApplicator applicator{frozenPatterns}; + applicator.applyDefaultCostModel(); + + // Marks operations that can be deleted. + // We delete them *after* SLP graph conversion to avoid running into NULL operands during conversion. + SmallPtrSet erasableOps; + + // Stores escaping uses for vector extractions that might be necessary later on. + auto const& escapingLanes = getEscapingLanesMap(root); + + // Traverse the SLP graph in postorder and apply the vectorization patterns. + for (auto* node : postOrder(root)) { + + // Also traverse nodes in postorder to properly handle multinodes. + for (size_t vectorIndex = node->numVectors(); vectorIndex-- > 0;) { + auto* vector = node->getVector(vectorIndex); + currentVector.reset(vector); + // Rewrite vector by applying any matching pattern. + if (vector->containsBlockArg()) { + // TODO: BroadcastInsert + } else { + auto* vectorOp = vector->begin()->getDefiningOp(); + if (failed(applicator.matchAndRewrite(vectorOp, *this))) { + vectorOp->emitOpError("SLP pattern application failed (did you forget to specify the pattern?)"); + } + } + } - auto* node = this->parentNodes.lookup(constantOp); + // Gather operations that can be erased and create vector extractions using those that need to stay. + for (size_t vectorIndex = node->numVectors(); vectorIndex-- > 0;) { + auto* vector = node->getVector(vectorIndex); + for (size_t lane = 0; lane < vector->numLanes(); ++lane) { + auto const& vectorValue = vector->getElement(lane); + if (vectorValue.isa()) { + continue; + } + erasableOps.insert(vectorValue.getDefiningOp()); + if (escapingLanes.lookup(vector).contains(lane)) { + auto const& source = vectorizedOps[vector]->getResult(0); + // TODO: set insertion point right before the first outside user + setInsertionPointAfterValue(source); + auto extractOp = create(vectorValue.getLoc(), source, lane); + vectorValue.replaceAllUsesWith(extractOp.result()); + } + } + } + } + + for (auto* op : erasableOps) { + op->dropAllUses(); + eraseOp(op); + } + + // If an SLP node failed to vectorize completely, fail the pass. + for (auto const& entry: vectorizedOps) { + if (!entry.second) { + llvm::dbgs() << "Failed to vectorize vector:\n"; + dumpSLPNodeVector(*entry.first); + return failure(); + } + } + + return success(); +} + +LogicalResult VectorizeConstant::matchAndRewrite(ConstantOp constantOp, PatternRewriter& rewriter) const { - auto const& vectorIndex = node->getVectorIndex(constantOp); - auto const& vector = node->getVector(vectorIndex); - auto const& vectorType = VectorType::get(static_cast(vector.size()), constantOp.getType()); + auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), constantOp.getType()); - rewriter.setInsertionPoint(firstUser(std::begin(vector), std::end(vector))); + rewriter.setInsertionPoint(firstUser(vector->begin(), vector->end())); - if (!node->isUniform()) { - auto const& elements = results(std::begin(vector), std::end(vector)); - auto vectorVal = broadcastFirstInsertRest(std::begin(elements), std::end(elements), vectorType, rewriter); - this->vectorsByNode[node][vectorIndex] = vectorVal.getDefiningOp(); + if (!vector->isUniform()) { + auto vectorVal = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); + vectorizedOps[vector.get()] = vectorVal.getDefiningOp(); return success(); } SmallVector constants; - for (auto* vectorOp : vector) { - constants.emplace_back(static_cast(vectorOp).getValue()); + for (auto const& value : *vector) { + constants.emplace_back(value.getDefiningOp().value()); } auto const& elements = denseFloatingPoints(std::begin(constants), std::end(constants), vectorType); auto constVector = rewriter.create(constantOp->getLoc(), elements); - this->vectorsByNode[node][vectorIndex] = constVector; + vectorizedOps[vector.get()] = constVector; return success(); } LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead batchReadOp, PatternRewriter& rewriter) const { - auto* node = parentNodes.lookup(batchReadOp); + auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), batchReadOp.getType()); - auto const& vectorIndex = node->getVectorIndex(batchReadOp); - auto const& vector = node->getVector(vectorIndex); - auto const& vectorType = VectorType::get(static_cast(vector.size()), batchReadOp.getType()); + rewriter.setInsertionPoint(firstUser(vector->begin(), vector->end())); - rewriter.setInsertionPoint(firstUser(std::begin(vector), std::end(vector))); - - if (!node->isUniform() || !areConsecutiveLoads(vector)) { - auto const& elements = results(std::begin(vector), std::end(vector)); - auto vectorVal = broadcastFirstInsertRest(std::begin(elements), std::end(elements), vectorType, rewriter); - vectorsByNode[node][vectorIndex] = vectorVal.getDefiningOp(); + if (!vector->isUniform() || !consecutiveLoads(vector->begin(), vector->end())) { + auto vectorVal = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); + vectorizedOps[vector.get()] = vectorVal.getDefiningOp(); } else { auto batchReadLoc = batchReadOp->getLoc(); @@ -128,7 +251,7 @@ LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead batchReadOp, Patt ValueRange indices{batchReadOp.batchIndex(), memIndex}; auto vectorLoad = rewriter.create(batchReadLoc, vectorType, batchReadOp.batchMem(), indices); - vectorsByNode[node][vectorIndex] = vectorLoad; + vectorizedOps[vector.get()] = vectorLoad; } return success(); @@ -136,129 +259,101 @@ LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead batchReadOp, Patt LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd addOp, PatternRewriter& rewriter) const { - auto* node = parentNodes.lookup(addOp); - - auto const& vectorIndex = node->getVectorIndex(addOp); - auto const& vector = node->getVector(vectorIndex); - auto const& vectorType = VectorType::get(static_cast(vector.size()), addOp.getType()); + auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), addOp.getType()); + auto const& firstValue = firstOccurrence(vector->begin(), vector->end()); - rewriter.setInsertionPoint(firstOperation(std::begin(vector), std::end(vector))); + if (firstValue.isa()) { + rewriter.setInsertionPointAfterValue(firstValue); + } else { + rewriter.setInsertionPoint(firstValue.getDefiningOp()); + } - if (!node->isUniform()) { - auto const& elements = results(std::begin(vector), std::end(vector)); - auto vectorVal = broadcastFirstInsertRest(std::begin(elements), std::end(elements), vectorType, rewriter); - vectorsByNode[node][vectorIndex] = vectorVal.getDefiningOp(); + if (!vector->isUniform()) { + auto vectorVal = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); + vectorizedOps[vector.get()] = vectorVal.getDefiningOp(); return success(); } llvm::SmallVector operands; for (unsigned i = 0; i < addOp.getNumOperands(); ++i) { - Value operand; - if (std::any_of(std::begin(vector), std::end(vector), [&](auto* vectorOp) { - Value vectorOperand = addOp.getOperand(i); - return vectorOperand.isa() || !parentNodes.count(vectorOperand.getDefiningOp()); - })) { - llvm::SmallVector elements; - for (auto* vectorOp : vector) { - elements.emplace_back(vectorOp->getOperand(i)); - } - operand = broadcastFirstInsertRest(std::begin(elements), std::end(elements), vectorType, rewriter); - } else { - auto* operandOp = addOp.getOperand(i).getDefiningOp(); - auto* operandNode = parentNodes.lookup(operandOp); - if (!vectorsByNode.count(operandNode) || vectorsByNode[operandNode].size() != operandNode->numVectors()) { - return rewriter.notifyMatchFailure(addOp, "operation's LHS has not yet been (fully) vectorized"); - } - operand = vectorsByNode[operandNode][operandNode->getVectorIndex(operandOp)]->getResult(0); + auto* operandOp = vectorizedOps[vector->getOperand(i)]; + if (!operandOp) { + return rewriter.notifyMatchFailure(addOp, "operation's operands have not yet been (fully) vectorized"); } - operands.emplace_back(operand); + operands.emplace_back(operandOp->getResult(0)); } auto vectorAddOp = rewriter.create(addOp->getLoc(), vectorType, operands); - vectorsByNode[node][vectorIndex] = vectorAddOp; + vectorizedOps[vector.get()] = vectorAddOp; return success(); } LogicalResult VectorizeMul::matchAndRewrite(SPNMul mulOp, PatternRewriter& rewriter) const { - auto* node = parentNodes.lookup(mulOp); - - auto const& vectorIndex = node->getVectorIndex(mulOp); - auto const& vector = node->getVector(vectorIndex); - auto const& vectorType = VectorType::get(static_cast(vector.size()), mulOp.getType()); + auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), mulOp.getType()); + auto const& firstValue = firstOccurrence(vector->begin(), vector->end()); - rewriter.setInsertionPoint(firstOperation(std::begin(vector), std::end(vector))); + if (firstValue.isa()) { + rewriter.setInsertionPointAfterValue(firstValue); + } else { + rewriter.setInsertionPoint(firstValue.getDefiningOp()); + } - if (!node->isUniform()) { - auto const& elements = results(std::begin(vector), std::end(vector)); - auto vectorVal = broadcastFirstInsertRest(std::begin(elements), std::end(elements), vectorType, rewriter); - vectorsByNode[node][vectorIndex] = vectorVal.getDefiningOp(); + if (!vector->isUniform()) { + auto vectorVal = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); + vectorizedOps[vector.get()] = vectorVal.getDefiningOp(); return success(); } llvm::SmallVector operands; for (unsigned i = 0; i < mulOp.getNumOperands(); ++i) { - Value operand; - if (std::any_of(std::begin(vector), std::end(vector), [&](auto* vectorOp) { - Value vectorOperand = mulOp.getOperand(i); - return vectorOperand.isa() || !parentNodes.count(vectorOperand.getDefiningOp()); - })) { - llvm::SmallVector elements; - for (auto* vectorOp : vector) { - elements.emplace_back(vectorOp->getOperand(i)); - } - operand = broadcastFirstInsertRest(std::begin(elements), std::end(elements), vectorType, rewriter); - } else { - auto* operandOp = mulOp.getOperand(i).getDefiningOp(); - auto* operandNode = parentNodes.lookup(operandOp); - if (!vectorsByNode.count(operandNode) || vectorsByNode[operandNode].size() != operandNode->numVectors()) { - return rewriter.notifyMatchFailure(mulOp, "operation's LHS has not yet been (fully) vectorized"); - } - operand = vectorsByNode[operandNode][operandNode->getVectorIndex(operandOp)]->getResult(0); + auto* operandOp = vectorizedOps[vector->getOperand(i)]; + if (!operandOp) { + return rewriter.notifyMatchFailure(mulOp, "operation's operands have not yet been (fully) vectorized"); } - operands.emplace_back(operand); + operands.emplace_back(operandOp->getResult(0)); } - auto vectorMulOp = rewriter.create(mulOp->getLoc(), vectorType, operands); + auto vectorAddOp = rewriter.create(mulOp->getLoc(), vectorType, operands); - vectorsByNode[node][vectorIndex] = vectorMulOp; + vectorizedOps[vector.get()] = vectorAddOp; return success(); } LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, PatternRewriter& rewriter) const { - auto* node = parentNodes.lookup(gaussianOp); - - auto const& vectorIndex = node->getVectorIndex(gaussianOp); - auto const& vector = node->getVector(vectorIndex); - auto const& vectorType = VectorType::get(static_cast(vector.size()), gaussianOp.getType()); + auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), gaussianOp.getType()); + auto const& firstValue = firstOccurrence(vector->begin(), vector->end()); - rewriter.setInsertionPoint(firstOperation(std::begin(vector), std::end(vector))); + if (firstValue.isa()) { + rewriter.setInsertionPointAfterValue(firstValue); + } else { + rewriter.setInsertionPoint(firstValue.getDefiningOp()); + } - if (!node->isUniform()) { - auto const& elements = results(std::begin(vector), std::end(vector)); - auto vectorVal = broadcastFirstInsertRest(std::begin(elements), std::end(elements), vectorType, rewriter); - vectorsByNode[node][vectorIndex] = vectorVal.getDefiningOp(); + if (!vector->isUniform()) { + auto vectorVal = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); + vectorizedOps[vector.get()] = vectorVal.getDefiningOp(); return success(); } DenseElementsAttr coefficients; if (vectorType.getElementType().cast().getWidth() == 32) { SmallVector array; - for (auto* vectorOp : vector) { - float stddev = static_cast(vectorOp).stddev().convertToFloat(); + for (auto const& value : *vector) { + float stddev = static_cast(value.getDefiningOp()).stddev().convertToFloat(); array.emplace_back(1.0f / (stddev * std::sqrt(2.0f * M_PIf32))); } coefficients = DenseElementsAttr::get(vectorType, static_cast>(array)); } else { SmallVector array; - for (auto* vectorOp : vector) { - double stddev = static_cast(vectorOp).stddev().convertToDouble(); + for (auto const& value : *vector) { + double stddev = static_cast(value.getDefiningOp()).stddev().convertToDouble(); array.emplace_back(1.0 / (stddev * std::sqrt(2.0 * M_PI))); } coefficients = DenseElementsAttr::get(vectorType, static_cast>(array)); @@ -266,36 +361,24 @@ LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, Pat // Gather means in a dense floating point attribute vector. SmallVector meanAttributes; - for (auto* vectorOp : vector) { - meanAttributes.emplace_back(static_cast(vectorOp).meanAttr()); + for (auto const& value : *vector) { + meanAttributes.emplace_back(static_cast(value.getDefiningOp()).meanAttr()); } auto const& means = denseFloatingPoints(std::begin(meanAttributes), std::end(meanAttributes), vectorType); // Gather standard deviations in a dense floating point attribute vector. SmallVector stddevAttributes; - for (auto* vectorOp : vector) { - stddevAttributes.emplace_back(static_cast(vectorOp).stddevAttr()); + for (auto const& value : *vector) { + stddevAttributes.emplace_back(static_cast(value.getDefiningOp()).stddevAttr()); } auto const& stddevs = denseFloatingPoints(std::begin(stddevAttributes), std::end(stddevAttributes), vectorType); // Grab the input vector. - Value inputVector; - if (std::any_of(std::begin(vector), std::end(vector), [&](auto* vectorOp) { - return static_cast(vectorOp).index().template isa(); - })) { - llvm::SmallVector elements; - for (auto* vectorOp : vector) { - elements.emplace_back(static_cast(vectorOp).index()); - } - inputVector = broadcastFirstInsertRest(std::begin(elements), std::end(elements), vectorType, rewriter); - } else { - auto* inputOp = gaussianOp.index().getDefiningOp(); - auto* inputNode = parentNodes.lookup(inputOp); - if (!vectorsByNode.count(inputNode) || vectorsByNode[inputNode].size() != inputNode->numVectors()) { - return rewriter.notifyMatchFailure(gaussianOp, "operation's input node has not yet been (fully) vectorized"); - } - inputVector = vectorsByNode[inputNode][inputNode->getVectorIndex(inputOp)]->getResult(0); + auto* inputOp = vectorizedOps[vector->getOperand(0)]; + if (!inputOp) { + return rewriter.notifyMatchFailure(gaussianOp, "operation's operands have not yet been (fully) vectorized"); } + Value inputVector = inputOp->getResult(0); // Calculate Gaussian distribution using e^(-0.5 * ((x - mean) / stddev)^2)) / (stddev * sqrt(2 * PI)) auto const& gaussianLoc = gaussianOp->getLoc(); @@ -318,7 +401,7 @@ LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, Pat auto coefficientVector = rewriter.create(gaussianLoc, coefficients); gaussianVector = rewriter.create(gaussianLoc, coefficientVector, gaussianVector); - vectorsByNode[node][vectorIndex] = gaussianVector.getDefiningOp(); + vectorizedOps[vector.get()] = gaussianVector.getDefiningOp(); return success(); } From 6ae6c82dc7ea6f475de108df64083a916d9d18c5 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Mon, 19 Apr 2021 22:44:32 +0200 Subject: [PATCH 089/385] Fix: flawed seed depth computation. --- .../Vectorization/SLP/SLPSeeding.cpp | 21 +++++++++---------- 1 file changed, 10 insertions(+), 11 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp index e03f5f12..011e5e62 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp @@ -16,18 +16,17 @@ namespace { DenseMap getOpDepths(Operation* rootOp) { DenseMap opDepths; rootOp->walk([&](Operation* op) { - if (!vectorizable(op)) { - return; - } - auto const& value = op->getResult(0); - if (!opDepths.count(value)) { - opDepths[value] = 0; - } - for (auto const& use : op->getUses()) { - if (!opDepths.count(use.get()) || opDepths[value] + 1 > opDepths[use.get()]) { - opDepths[use.get()] = opDepths[value] + 1; + unsigned depth = 0; + for (auto const& value : op->getOperands()) { + if (!opDepths.count(value)) { + opDepths[value] = 0; + } else { + depth = std::max(depth, opDepths[value] + 1); } } + for (auto const& result : op->getResults()) { + opDepths[result] = depth; + } }); return opDepths; } @@ -41,7 +40,7 @@ vector_t SeedAnalysis::getSeed(unsigned width, SearchMode const& mode) const { for (auto const& entry : opDepths) { auto const& value = entry.first; auto const& depth = entry.second; - if (depth < log2(width)) { + if (!vectorizable(value) || depth < log2(width)) { continue; } bool needsNewSeed = true; From e7b67b1229bac356abcd73f0b4420fd82c71083f Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Mon, 19 Apr 2021 22:45:04 +0200 Subject: [PATCH 090/385] Updated dumpSLPGraph() to value changes. --- .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.h | 10 +++++----- .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp | 12 ++++++------ 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h index 166b3c4f..64dffd18 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h @@ -15,11 +15,6 @@ namespace mlir { namespace low { namespace slp { - void dumpOpTree(vector_t const& values); - void dumpSLPGraph(SLPNode const& root); - void dumpSLPNode(SLPNode const& node); - void dumpSLPNodeVector(NodeVector const& nodeVector); - bool vectorizable(Operation* op); bool vectorizable(Value const& value); @@ -109,6 +104,11 @@ namespace mlir { return firstUser; } + void dumpOpTree(vector_t const& values); + void dumpSLPGraph(SLPNode const& root); + void dumpSLPNode(SLPNode const& node); + void dumpSLPNodeVector(NodeVector const& nodeVector); + } } } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp index a3202ffd..bd4e0be2 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp @@ -12,7 +12,7 @@ using namespace mlir; bool mlir::spn::low::slp::vectorizable(Operation* op) { - // (MLIR does not allow index types in vectors) + // MLIR does not allow index types in vectors. return op->hasTrait() && op->hasTrait() && !op->getResult(0).getType().isa(); } @@ -98,7 +98,7 @@ namespace { void mlir::spn::low::slp::dumpOpTree(const vector_t& values) { DenseMap nodes; - SmallVector> edges; + SmallVector> edges; std::stack worklist; for (auto const& value : values) { @@ -113,7 +113,7 @@ void mlir::spn::low::slp::dumpOpTree(const vector_t& values) { } nodes[value] = nodes.size(); if (auto* definingOp = value.getDefiningOp()) { - for (size_t i = 0; i < definingOp->getNumOperands(); ++i) { + for (unsigned i = 0; i < definingOp->getNumOperands(); ++i) { auto const& operand = definingOp->getOperand(i); edges.emplace_back(std::make_tuple(value, operand, i)); worklist.push(operand); @@ -127,7 +127,7 @@ void mlir::spn::low::slp::dumpOpTree(const vector_t& values) { for (auto const& entry : nodes) { auto const& value = entry.first; auto const& id = entry.second; - llvm::dbgs() << "node_" << id << "[label=\""; + llvm::dbgs() << "\tnode_" << id << "[label=\""; if (auto* definingOp = value.getDefiningOp()) { llvm::dbgs() << definingOp->getName().getStringRef() << "\\n" << definingOp; if (auto constantOp = dyn_cast(definingOp)) { @@ -147,8 +147,8 @@ void mlir::spn::low::slp::dumpOpTree(const vector_t& values) { llvm::dbgs() << "\", fillcolor=\"#a0522d\"];\n"; } for (auto const& edge : edges) { - llvm::dbgs() << "node_" << std::get<0>(edge) << " -> node_" << std::get<1>(edge) << "[label=\"" - << std::to_string(std::get<2>(edge)) << "\"];\n"; + llvm::dbgs() << "\tnode_" << nodes[std::get<0>(edge)] << " -> node_" << nodes[std::get<1>(edge)] << "[label=\"" + << std::get<2>(edge) << "\"];\n"; } llvm::dbgs() << "}\n"; } From 673b19c369d881ea0016e534362b7a7576f8b7ed Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Mon, 19 Apr 2021 23:16:11 +0200 Subject: [PATCH 091/385] Fix: weird dumpSLPNodeVector() output behaviour. --- .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp | 26 ++++++++++--------- 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp index bd4e0be2..1faa62e3 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp @@ -66,18 +66,6 @@ void mlir::spn::low::slp::dumpSLPNode(mlir::spn::low::slp::SLPNode const& node) } } -void mlir::spn::low::slp::dumpSLPNodeVector(const mlir::spn::low::slp::NodeVector& nodeVector) { - for (size_t lane = 0; lane < nodeVector.numLanes(); ++lane) { - llvm::dbgs() << nodeVector[lane]; - if (!nodeVector[lane].isa()) { - llvm::dbgs() << "(" << nodeVector[lane].getDefiningOp() << ")"; - } - if (lane < nodeVector.numLanes() - 1) { - llvm::dbgs() << "\t|\t"; - } - } -} - // Helper functions in an anonymous namespace. namespace { void dumpBlockArgOrDefiningAddress(Value const& val) { @@ -96,6 +84,20 @@ namespace { } } +void mlir::spn::low::slp::dumpSLPNodeVector(const mlir::spn::low::slp::NodeVector& nodeVector) { + for (size_t lane = 0; lane < nodeVector.numLanes(); ++lane) { + if (!nodeVector[lane].isa()) { + llvm::dbgs() << nodeVector[lane] << " (" << nodeVector[lane].getDefiningOp() << ")"; + } else { + dumpBlockArgOrDefiningOpName(nodeVector[lane]); + } + if (lane < nodeVector.numLanes() - 1) { + llvm::dbgs() << "\t|\t"; + } + } + llvm::dbgs() << "\n"; +} + void mlir::spn::low::slp::dumpOpTree(const vector_t& values) { DenseMap nodes; SmallVector> edges; From 34763298a0dfc6b364ad11f1b273fcd0a5e2cc40 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Mon, 19 Apr 2021 23:16:32 +0200 Subject: [PATCH 092/385] Fix: segfault during operand reordering. --- .../Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp index 32c63368..3669c0df 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp @@ -158,7 +158,7 @@ void SLPGraphBuilder::reorderOperands(SLPNode* multinode) const { finalOrder[lane].emplace_back(bestResult.first); // Detect SPLAT mode if (i == 1 && bestResult.first == last) { - mode[lane][i] = SPLAT; + mode[lane].emplace_back(SPLAT); } else { mode[lane].emplace_back(bestResult.second); } From ba992059ea2db62263b29af8149178969393fab0 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Tue, 20 Apr 2021 04:14:01 +0200 Subject: [PATCH 093/385] Fix: switched semantics of firstOccurrence and lastOccurrence. --- .../Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h index 64dffd18..bb4786fa 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h @@ -72,7 +72,7 @@ namespace mlir { typename std::iterator_traits::value_type firstOccurrence(Iterator begin, Iterator end) { typename std::iterator_traits::value_type first = *begin; while (++begin != end) { - if (isBeforeInBlock(first, *begin)) { + if (!isBeforeInBlock(first, *begin)) { first = *begin; } } @@ -81,13 +81,13 @@ namespace mlir { template typename std::iterator_traits::value_type lastOccurrence(Iterator begin, Iterator end) { - typename std::iterator_traits::value_type first = *begin; + typename std::iterator_traits::value_type last = *begin; while (++begin != end) { - if (!isBeforeInBlock(first, *begin)) { - first = *begin; + if (isBeforeInBlock(last, *begin)) { + last = *begin; } } - return first; + return last; } template From cbee64affd0ce6a5a3f13452301f1c48e6849083 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Tue, 20 Apr 2021 04:40:16 +0200 Subject: [PATCH 094/385] Fix: vectorizable() checks not working properly. --- .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp index 1faa62e3..95f6c477 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp @@ -12,17 +12,18 @@ using namespace mlir; bool mlir::spn::low::slp::vectorizable(Operation* op) { - // MLIR does not allow index types in vectors. - return op->hasTrait() && op->hasTrait() - && !op->getResult(0).getType().isa(); + return (op->hasTrait() || op->hasTrait()) + && op->hasTrait() && op->getResult(0).getType().isIntOrFloat(); } bool mlir::spn::low::slp::vectorizable(Value const& value) { // Block arguments don't have defining ops (they can still be put in a vector by other means). - if (value.isa()) { - return false; + if (auto* definingOp = value.getDefiningOp()) { + if (!vectorizable(definingOp)) { + return false; + } } - return vectorizable(value.getDefiningOp()); + return value.getType().isIntOrFloat(); } bool mlir::spn::low::slp::isBeforeInBlock(Operation* lhs, Operation* rhs) { From 7ffb063161e567a911cb601c69b35f0d216e8044 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Tue, 20 Apr 2021 04:40:55 +0200 Subject: [PATCH 095/385] Fix: seed analysis not working deterministically. --- .../Vectorization/SLP/SLPSeeding.cpp | 21 +++++++++++-------- 1 file changed, 12 insertions(+), 9 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp index 011e5e62..e797befa 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp @@ -37,25 +37,28 @@ vector_t SeedAnalysis::getSeed(unsigned width, SearchMode const& mode) const { llvm::StringMap> seedsByOpName; auto const& opDepths = getOpDepths(rootOp); - for (auto const& entry : opDepths) { - auto const& value = entry.first; - auto const& depth = entry.second; - if (!vectorizable(value) || depth < log2(width)) { - continue; + rootOp->walk([&](Operation* op) { + if (!vectorizable(op)) { + return; + } + auto const& value = op->getResult(0); + auto const& depth = opDepths.lookup(value); + if (depth < log2(width)) { + return; } bool needsNewSeed = true; - for (auto& seed : seedsByOpName[value.getDefiningOp()->getName().getStringRef()]) { + for (auto& seed : seedsByOpName[op->getName().getStringRef()]) { if (seed.size() < width && opDepths.lookup(seed.front()) == depth) { - seed.emplace_back(entry.first); + seed.emplace_back(value); needsNewSeed = false; break; } } if (needsNewSeed) { vector_t seed{value}; - seedsByOpName[value.getDefiningOp()->getName().getStringRef()].emplace_back(seed); + seedsByOpName[op->getName().getStringRef()].emplace_back(seed); } - } + }); SmallVector seeds; // Flatten the map that maps opcode to seeds. From e8040e63936056b881b4236c74d554e66ed1b52e Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Tue, 20 Apr 2021 04:42:05 +0200 Subject: [PATCH 096/385] Fix: unreliable operation erasure & extraction behaviour. --- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.h | 1 - .../SLP/SLPVectorizationPatterns.h | 26 +-- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp | 6 - .../SLP/SLPVectorizationPatterns.cpp | 164 +++++++++--------- 4 files changed, 93 insertions(+), 104 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h index 20863768..0e65e21f 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h @@ -25,7 +25,6 @@ namespace mlir { explicit NodeVector(SmallVector const& operations); bool isUniform() const; - bool containsBlockArg() const; bool contains(Value const& value) const; size_t numLanes() const; diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h index a8798e24..51456593 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h @@ -15,6 +15,7 @@ #include "mlir/IR/PatternMatch.h" #include "mlir/Rewrite/FrozenRewritePatternList.h" #include "mlir/Rewrite/PatternApplicator.h" +#include "llvm/ADT/SmallPtrSet.h" #include "llvm/Support/Debug.h" namespace mlir { @@ -35,14 +36,16 @@ namespace mlir { public: SLPVectorizationPattern(MLIRContext* context, PatternBenefit benefit, - std::shared_ptr vector, - DenseMap& vectorizedOps) : OpRewritePattern{ - context, benefit}, vector{std::move(vector)}, vectorizedOps{vectorizedOps} {} + NodeVector* const& vector, + DenseMap& vectorizedOps, + SmallPtrSet& erasableOps) : OpRewritePattern{ + context, benefit}, vector{vector}, vectorizedOps{vectorizedOps}, erasableOps{erasableOps} {} protected: /// The current vector being transformed. - std::shared_ptr vector; - llvm::DenseMap& vectorizedOps; + NodeVector* const& vector; + DenseMap& vectorizedOps; + SmallPtrSet& erasableOps; }; struct VectorizeConstant : public SLPVectorizationPattern { @@ -72,12 +75,13 @@ namespace mlir { static void populateSLPVectorizationPatterns(OwningRewritePatternList& patterns, MLIRContext* context, - std::shared_ptr const& vector, - llvm::DenseMap& vectorizedOps) { - patterns.insert(context, 2, vector, vectorizedOps); - patterns.insert(context, 2, vector, vectorizedOps); - patterns.insert(context, 2, vector, vectorizedOps); - patterns.insert(context, 2, vector, vectorizedOps); + NodeVector* const& vector, + DenseMap& vectorizedOps, + SmallPtrSet& erasableOps) { + patterns.insert(context, 2, vector, vectorizedOps, erasableOps); + patterns.insert(context, 2, vector, vectorizedOps, erasableOps); + patterns.insert(context, 2, vector, vectorizedOps, erasableOps); + patterns.insert(context, 2, vector, vectorizedOps, erasableOps); } } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp index d267d319..18de42a8 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp @@ -39,12 +39,6 @@ bool NodeVector::isUniform() const { }); } -bool NodeVector::containsBlockArg() const { - return std::any_of(std::begin(values), std::end(values), [&](Value const& value) { - return value.isa(); - }); -} - bool NodeVector::contains(Value const& value) const { return std::find(std::begin(values), std::end(values), value) != std::end(values); } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp index 950682de..cd71a204 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp @@ -8,9 +8,7 @@ #include "mlir/Dialect/Vector/VectorOps.h" #include "mlir/Dialect/Math/IR/Math.h" #include "mlir/IR/BlockAndValueMapping.h" -#include "llvm/ADT/SmallPtrSet.h" #include "llvm/Support/FormatVariadic.h" -#include using namespace mlir; using namespace mlir::spn; @@ -77,8 +75,8 @@ namespace { return order; } - llvm::DenseMap> getEscapingLanesMap(SLPNode* root) { - llvm::DenseMap> outsideUses; + DenseMap> getEscapingLanesMap(SLPNode* root) { + DenseMap> outsideUses; for (auto* node : postOrder(root)) { for (size_t i = 0; i < node->numVectors(); ++i) { auto* vector = node->getVector(i); @@ -91,13 +89,14 @@ namespace { outsideUses[vector][element] = std::distance(std::begin(element.getUses()), std::end(element.getUses())); for (size_t j = 0; j < vector->numOperands(); ++j) { auto* operand = vector->getOperand(j); + assert(outsideUses[operand][operand->getElement(lane)] > 0); outsideUses[operand][operand->getElement(lane)]--; } } } } - llvm::DenseMap> escapingLanes; + DenseMap> escapingLanes; for (auto const& entry : outsideUses) { auto* vector = entry.first; for (auto const& useAndCount : entry.second) { @@ -106,7 +105,7 @@ namespace { if (count > 0) { for (size_t lane = 0; lane < vector->numLanes(); ++lane) { if (vector->getElement(lane) == value) { - escapingLanes[vector].insert(lane); + escapingLanes[vector].emplace_back(lane); break; } } @@ -116,21 +115,17 @@ namespace { return escapingLanes; } - DenseMap emptyVectorizedOpsMap(SLPNode* root) { - DenseMap vectorizedOps; - std::queue worklist; - worklist.emplace(root); - while (!worklist.empty()) { - auto* node = worklist.front(); - worklist.pop(); - for (size_t i = 0; i < node->numVectors(); ++i) { - vectorizedOps[node->getVector(i)] = nullptr; - } - for (auto const& operand : node->getOperands()) { - worklist.emplace(operand); - } - } - return vectorizedOps; + bool containsBlockArgs(NodeVector* vector) { + return std::any_of(vector->begin(), vector->end(), [&](Value const& value) { + return value.isa(); + }); + } + + template + bool valuesVectorizable(ValueIterator begin, ValueIterator end) { + return std::all_of(begin, end, [&](Value const& value) { + return vectorizable(value); + }); } } @@ -139,32 +134,45 @@ SLPVectorPatternRewriter::SLPVectorPatternRewriter(mlir::MLIRContext* ctx) : Pat LogicalResult SLPVectorPatternRewriter::rewrite(SLPNode* root) { + // The current vector being transformed. + NodeVector* vector = nullptr; + + // Maps individual SLP node vectors to finished vector operations. + DenseMap vectorizedOps; + + // Marks operations as potentially erasable (potentially because they might have escaping uses). + // We delete them *after* SLP graph conversion to avoid running into NULL operands during conversion. + SmallPtrSet erasableOps; + OwningRewritePatternList patterns; - auto vectorizedOps = emptyVectorizedOpsMap(root); - std::shared_ptr currentVector; - populateSLPVectorizationPatterns(patterns, context, currentVector, vectorizedOps); + populateSLPVectorizationPatterns(patterns, context, vector, vectorizedOps, erasableOps); FrozenRewritePatternList frozenPatterns(std::move(patterns)); PatternApplicator applicator{frozenPatterns}; applicator.applyDefaultCostModel(); - // Marks operations that can be deleted. - // We delete them *after* SLP graph conversion to avoid running into NULL operands during conversion. - SmallPtrSet erasableOps; - // Stores escaping uses for vector extractions that might be necessary later on. auto const& escapingLanes = getEscapingLanesMap(root); + // No need to extract/remove values more than once in case they appear more than once (e.g. in splat mode). + SmallPtrSet finishedValues; + // Traverse the SLP graph in postorder and apply the vectorization patterns. for (auto* node : postOrder(root)) { // Also traverse nodes in postorder to properly handle multinodes. for (size_t vectorIndex = node->numVectors(); vectorIndex-- > 0;) { - auto* vector = node->getVector(vectorIndex); - currentVector.reset(vector); + vector = node->getVector(vectorIndex); // Rewrite vector by applying any matching pattern. - if (vector->containsBlockArg()) { - // TODO: BroadcastInsert + if (!valuesVectorizable(vector->begin(), vector->end())) { + continue; + } + if (containsBlockArgs(vector)) { + auto const& elementType = vector->getElement(0).getType(); + auto const& type = VectorType::get(static_cast(vector->numLanes()), elementType); + setInsertionPoint(firstUser(vector->begin(), vector->end())); + auto vectorOp = broadcastFirstInsertRest(vector->begin(), vector->end(), type, *this); + vectorizedOps[vector] = vectorOp.getDefiningOp(); } else { auto* vectorOp = vector->begin()->getDefiningOp(); if (failed(applicator.matchAndRewrite(vectorOp, *this))) { @@ -173,40 +181,31 @@ LogicalResult SLPVectorPatternRewriter::rewrite(SLPNode* root) { } } - // Gather operations that can be erased and create vector extractions using those that need to stay. + // Create vector extractions for escaping uses & erase superfluous operations. for (size_t vectorIndex = node->numVectors(); vectorIndex-- > 0;) { - auto* vector = node->getVector(vectorIndex); - for (size_t lane = 0; lane < vector->numLanes(); ++lane) { - auto const& vectorValue = vector->getElement(lane); - if (vectorValue.isa()) { - continue; - } - erasableOps.insert(vectorValue.getDefiningOp()); - if (escapingLanes.lookup(vector).contains(lane)) { - auto const& source = vectorizedOps[vector]->getResult(0); - // TODO: set insertion point right before the first outside user - setInsertionPointAfterValue(source); - auto extractOp = create(vectorValue.getLoc(), source, lane); - vectorValue.replaceAllUsesWith(extractOp.result()); + vector = node->getVector(vectorIndex); + if (vectorizedOps.count(vector) && erasableOps.count(vector)) { + for (size_t lane = 0; lane < vector->numLanes(); ++lane) { + auto const& vectorValue = vector->getElement(lane); + if (finishedValues.contains(vectorValue)) { + continue; + } + auto const& lanesToExtract = escapingLanes.lookup(vector); + if (std::find(std::begin(lanesToExtract), std::end(lanesToExtract), lane) != std::end(lanesToExtract)) { + auto const& source = vectorizedOps[vector]->getResult(0); + setInsertionPoint(firstOccurrence(std::begin(vectorValue.getUsers()), std::end(vectorValue.getUsers()))); + auto extractOp = create(vectorValue.getLoc(), source, lane); + vectorValue.replaceAllUsesWith(extractOp.result()); + } else { + vectorValue.dropAllUses(); + } + finishedValues.insert(vectorValue); + eraseOp(vectorValue.getDefiningOp()); } } } } - for (auto* op : erasableOps) { - op->dropAllUses(); - eraseOp(op); - } - - // If an SLP node failed to vectorize completely, fail the pass. - for (auto const& entry: vectorizedOps) { - if (!entry.second) { - llvm::dbgs() << "Failed to vectorize vector:\n"; - dumpSLPNodeVector(*entry.first); - return failure(); - } - } - return success(); } @@ -218,7 +217,7 @@ LogicalResult VectorizeConstant::matchAndRewrite(ConstantOp constantOp, PatternR if (!vector->isUniform()) { auto vectorVal = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); - vectorizedOps[vector.get()] = vectorVal.getDefiningOp(); + vectorizedOps[vector] = vectorVal.getDefiningOp(); return success(); } @@ -230,7 +229,8 @@ LogicalResult VectorizeConstant::matchAndRewrite(ConstantOp constantOp, PatternR auto constVector = rewriter.create(constantOp->getLoc(), elements); - vectorizedOps[vector.get()] = constVector; + vectorizedOps[vector] = constVector; + erasableOps.insert(vector); return success(); } @@ -243,7 +243,7 @@ LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead batchReadOp, Patt if (!vector->isUniform() || !consecutiveLoads(vector->begin(), vector->end())) { auto vectorVal = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); - vectorizedOps[vector.get()] = vectorVal.getDefiningOp(); + vectorizedOps[vector] = vectorVal.getDefiningOp(); } else { auto batchReadLoc = batchReadOp->getLoc(); @@ -251,7 +251,8 @@ LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead batchReadOp, Patt ValueRange indices{batchReadOp.batchIndex(), memIndex}; auto vectorLoad = rewriter.create(batchReadLoc, vectorType, batchReadOp.batchMem(), indices); - vectorizedOps[vector.get()] = vectorLoad; + vectorizedOps[vector] = vectorLoad; + erasableOps.insert(vector); } return success(); @@ -260,22 +261,16 @@ LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead batchReadOp, Patt LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd addOp, PatternRewriter& rewriter) const { auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), addOp.getType()); - auto const& firstValue = firstOccurrence(vector->begin(), vector->end()); - if (firstValue.isa()) { - rewriter.setInsertionPointAfterValue(firstValue); - } else { - rewriter.setInsertionPoint(firstValue.getDefiningOp()); - } + rewriter.setInsertionPoint(firstUser(vector->begin(), vector->end())); if (!vector->isUniform()) { auto vectorVal = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); - vectorizedOps[vector.get()] = vectorVal.getDefiningOp(); + vectorizedOps[vector] = vectorVal.getDefiningOp(); return success(); } - llvm::SmallVector operands; - + SmallVector operands; for (unsigned i = 0; i < addOp.getNumOperands(); ++i) { auto* operandOp = vectorizedOps[vector->getOperand(i)]; if (!operandOp) { @@ -286,7 +281,8 @@ LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd addOp, PatternRewriter& rewri auto vectorAddOp = rewriter.create(addOp->getLoc(), vectorType, operands); - vectorizedOps[vector.get()] = vectorAddOp; + vectorizedOps[vector] = vectorAddOp; + erasableOps.insert(vector); return success(); } @@ -294,22 +290,16 @@ LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd addOp, PatternRewriter& rewri LogicalResult VectorizeMul::matchAndRewrite(SPNMul mulOp, PatternRewriter& rewriter) const { auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), mulOp.getType()); - auto const& firstValue = firstOccurrence(vector->begin(), vector->end()); - if (firstValue.isa()) { - rewriter.setInsertionPointAfterValue(firstValue); - } else { - rewriter.setInsertionPoint(firstValue.getDefiningOp()); - } + rewriter.setInsertionPoint(firstUser(vector->begin(), vector->end())); if (!vector->isUniform()) { auto vectorVal = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); - vectorizedOps[vector.get()] = vectorVal.getDefiningOp(); + vectorizedOps[vector] = vectorVal.getDefiningOp(); return success(); } - llvm::SmallVector operands; - + SmallVector operands; for (unsigned i = 0; i < mulOp.getNumOperands(); ++i) { auto* operandOp = vectorizedOps[vector->getOperand(i)]; if (!operandOp) { @@ -320,7 +310,8 @@ LogicalResult VectorizeMul::matchAndRewrite(SPNMul mulOp, PatternRewriter& rewri auto vectorAddOp = rewriter.create(mulOp->getLoc(), vectorType, operands); - vectorizedOps[vector.get()] = vectorAddOp; + vectorizedOps[vector] = vectorAddOp; + erasableOps.insert(vector); return success(); } @@ -338,7 +329,7 @@ LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, Pat if (!vector->isUniform()) { auto vectorVal = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); - vectorizedOps[vector.get()] = vectorVal.getDefiningOp(); + vectorizedOps[vector] = vectorVal.getDefiningOp(); return success(); } @@ -401,7 +392,8 @@ LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, Pat auto coefficientVector = rewriter.create(gaussianLoc, coefficients); gaussianVector = rewriter.create(gaussianLoc, coefficientVector, gaussianVector); - vectorizedOps[vector.get()] = gaussianVector.getDefiningOp(); + vectorizedOps[vector] = gaussianVector.getDefiningOp(); + erasableOps.insert(vector); return success(); } From 5f0c1d1974da3bbb10a5d0c1e24aa9b7c75d46fe Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Tue, 20 Apr 2021 04:57:02 +0200 Subject: [PATCH 097/385] Some commenting & code movement. --- .../Vectorization/SLP/SLPVectorizationPass.cpp | 4 ++-- .../Vectorization/SLP/SLPVectorizationPatterns.cpp | 14 ++++++-------- 2 files changed, 8 insertions(+), 10 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp index 0909780f..d1f3ed96 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp @@ -28,10 +28,10 @@ void SLPVectorizationPass::runOnOperation() { auto const& seed = seedAnalysis.getSeed(width, SearchMode::UseBeforeDef); assert(!seed.empty() && "couldn't find a seed!"); - dumpOpTree(seed); + // dumpOpTree(seed); SLPGraphBuilder builder{3}; auto graph = builder.build(seed); - dumpSLPGraph(*graph); + // dumpSLPGraph(*graph); // ==== // SLPVectorPatternRewriter rewriter(&getContext()); diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp index cd71a204..442ae869 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp @@ -141,9 +141,14 @@ LogicalResult SLPVectorPatternRewriter::rewrite(SLPNode* root) { DenseMap vectorizedOps; // Marks operations as potentially erasable (potentially because they might have escaping uses). - // We delete them *after* SLP graph conversion to avoid running into NULL operands during conversion. SmallPtrSet erasableOps; + // Stores escaping uses for vector extractions that might be necessary later on. + auto const& escapingLanes = getEscapingLanesMap(root); + + // Prevent extracting/removing values more than once (happens in splat mode, if they appear in multiple vectors, ...). + SmallPtrSet finishedValues; + OwningRewritePatternList patterns; populateSLPVectorizationPatterns(patterns, context, vector, vectorizedOps, erasableOps); FrozenRewritePatternList frozenPatterns(std::move(patterns)); @@ -151,19 +156,12 @@ LogicalResult SLPVectorPatternRewriter::rewrite(SLPNode* root) { PatternApplicator applicator{frozenPatterns}; applicator.applyDefaultCostModel(); - // Stores escaping uses for vector extractions that might be necessary later on. - auto const& escapingLanes = getEscapingLanesMap(root); - - // No need to extract/remove values more than once in case they appear more than once (e.g. in splat mode). - SmallPtrSet finishedValues; - // Traverse the SLP graph in postorder and apply the vectorization patterns. for (auto* node : postOrder(root)) { // Also traverse nodes in postorder to properly handle multinodes. for (size_t vectorIndex = node->numVectors(); vectorIndex-- > 0;) { vector = node->getVector(vectorIndex); - // Rewrite vector by applying any matching pattern. if (!valuesVectorizable(vector->begin(), vector->end())) { continue; } From a255ae58d6b35d2a67228dffb15b763e8de87a1e Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Tue, 20 Apr 2021 16:39:01 +0200 Subject: [PATCH 098/385] Removed vectorization strategy option. --- .../mlir/conversion/LoSPNtoCPUConversion.cpp | 6 ++---- .../mlir/conversion/LoSPNtoCPUConversion.h | 16 +++++++++++++++- compiler/src/driver/option/GlobalOptions.cpp | 6 ------ compiler/src/driver/toolchain/CPUToolchain.cpp | 12 +----------- 4 files changed, 18 insertions(+), 22 deletions(-) diff --git a/compiler/src/codegen/mlir/conversion/LoSPNtoCPUConversion.cpp b/compiler/src/codegen/mlir/conversion/LoSPNtoCPUConversion.cpp index 9b397505..7eb84043 100644 --- a/compiler/src/codegen/mlir/conversion/LoSPNtoCPUConversion.cpp +++ b/compiler/src/codegen/mlir/conversion/LoSPNtoCPUConversion.cpp @@ -19,12 +19,10 @@ void spnc::LoSPNtoCPUConversion::initializePassPipeline(mlir::PassManager* pm, m if (useShuffle) { pm->addPass(mlir::spn::createReplaceGatherWithShufflePass()); } - auto vectorizationStrategy = spnc::option::vectorizationStrategy.get(*this->config); - if(vectorizationStrategy == spnc::option::VectorizationStrategy::SLP) { + if (kernelInfo->batchSize == 1) { pm->addNestedPass(mlir::spn::createSLPVectorizationPass()); - } else if(vectorizationStrategy == spnc::option::VectorizationStrategy::BATCH) { - pm->addPass(mlir::spn::createLoSPNNodeVectorizationPass()); } + pm->addPass(mlir::spn::createLoSPNNodeVectorizationPass()); if (useShuffle) { // We need another run of the canonicalizer here to remove lo_spn.to_scalar // operations introduced by the replacement of gathers and that should diff --git a/compiler/src/codegen/mlir/conversion/LoSPNtoCPUConversion.h b/compiler/src/codegen/mlir/conversion/LoSPNtoCPUConversion.h index 19b0ec48..c8a5e701 100644 --- a/compiler/src/codegen/mlir/conversion/LoSPNtoCPUConversion.h +++ b/compiler/src/codegen/mlir/conversion/LoSPNtoCPUConversion.h @@ -6,16 +6,30 @@ #ifndef SPNC_COMPILER_SRC_CODEGEN_MLIR_CONVERSION_LOSPNTOCPUCONVERSION_H #define SPNC_COMPILER_SRC_CODEGEN_MLIR_CONVERSION_LOSPNTOCPUCONVERSION_H +#include "driver/Job.h" #include "../MLIRPassPipeline.h" namespace spnc { struct LoSPNtoCPUConversion : public MLIRPipelineBase { - using MLIRPipelineBase::MLIRPipelineBase; + public: + + LoSPNtoCPUConversion(ActionWithOutput& _input, + std::shared_ptr ctx, + std::shared_ptr handler, + std::shared_ptr kernelInformation) : + MLIRPipelineBase(_input, std::move(ctx), std::move(handler)), + kernelInfo{std::move(kernelInformation)} {} void initializePassPipeline(mlir::PassManager* pm, mlir::MLIRContext* ctx); + private: + + std::shared_ptr kernelInfo; + + using MLIRPipelineBase::MLIRPipelineBase; + }; } diff --git a/compiler/src/driver/option/GlobalOptions.cpp b/compiler/src/driver/option/GlobalOptions.cpp index c2618406..33a8634d 100644 --- a/compiler/src/driver/option/GlobalOptions.cpp +++ b/compiler/src/driver/option/GlobalOptions.cpp @@ -22,12 +22,6 @@ EnumOpt spnc::option::compilationTarget{"target", Option spnc::option::cpuVectorize{"cpu-vectorize", false}; -using spnc::option::VectorizationStrategy; -EnumOpt spnc::option::vectorizationStrategy{"vectorization-strategy", BATCH, - {EnumVal(BATCH, "BATCH"), - EnumVal(SLP, "SLP")}, - {depends(spnc::option::cpuVectorize, true)}}; - using spnc::option::VectorLibrary; EnumOpt spnc::option::vectorLibrary{"vector-library", NONE, {EnumVal(SVML, "SVML"), diff --git a/compiler/src/driver/toolchain/CPUToolchain.cpp b/compiler/src/driver/toolchain/CPUToolchain.cpp index c0a64edd..9aa9e288 100644 --- a/compiler/src/driver/toolchain/CPUToolchain.cpp +++ b/compiler/src/driver/toolchain/CPUToolchain.cpp @@ -35,16 +35,6 @@ std::unique_ptr > CPUToolchain::constructJobFromFile(const std::stri auto diagHandler = setupDiagnosticHandler(ctx.get()); auto cpuVectorize = spnc::option::cpuVectorize.get(*config); SPDLOG_INFO("CPU Vectorization enabled: {}", cpuVectorize); - if(cpuVectorize) { - auto vectorizationStrategy = spnc::option::vectorizationStrategy.get(*config); - std::string strategyName; - switch (vectorizationStrategy) { - case spnc::option::VectorizationStrategy::BATCH: strategyName = "BATCH"; break; - case spnc::option::VectorizationStrategy::SLP: strategyName = "SLP"; break; - default:SPNC_FATAL_ERROR("Unknown vectorization strategy"); - } - SPDLOG_INFO("CPU Vectorization strategy: {}", strategyName); - } auto targetMachine = createTargetMachine(cpuVectorize); auto kernelInfo = std::make_shared(); kernelInfo->target = KernelTarget::CPU; @@ -65,7 +55,7 @@ std::unique_ptr > CPUToolchain::constructJobFromFile(const std::stri //spnPipelineResult = &joinAction; } auto& lospnTransform = job->insertAction(hispn2lospn, ctx, diagHandler, kernelInfo); - auto& lospn2cpu = job->insertAction(lospnTransform, ctx, diagHandler); + auto& lospn2cpu = job->insertAction(lospnTransform, ctx, diagHandler, kernelInfo); auto& cpu2llvm = job->insertAction(lospn2cpu, ctx, diagHandler); // Convert the MLIR module to a LLVM-IR module. From 64e3ac09f9dcdd23b3a6204d2da6c00875fa8fd5 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Tue, 20 Apr 2021 21:11:27 +0200 Subject: [PATCH 099/385] Removed leftover option. --- compiler/include/driver/GlobalOptions.h | 8 -------- 1 file changed, 8 deletions(-) diff --git a/compiler/include/driver/GlobalOptions.h b/compiler/include/driver/GlobalOptions.h index bf5af770..c271be7d 100644 --- a/compiler/include/driver/GlobalOptions.h +++ b/compiler/include/driver/GlobalOptions.h @@ -39,14 +39,6 @@ namespace spnc { /// Flag to indicate whether the code generated for the CPU should be vectorized. extern Option cpuVectorize; - /// Available vectorization strategies - enum VectorizationStrategy { - BATCH, - SLP - }; - - extern EnumOpt vectorizationStrategy; - /// Available vector libraries enum VectorLibrary { SVML, From 20fa79ae1421fa65ac53ff7ec399602d00ceb538 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Wed, 21 Apr 2021 01:40:35 +0200 Subject: [PATCH 100/385] Pipeline update to only SLP vectorize tasks with a batch size of 1. --- .../mlir/conversion/LoSPNtoCPUConversion.cpp | 7 +------ .../mlir/conversion/LoSPNtoCPUConversion.h | 18 +---------------- .../src/driver/toolchain/CPUToolchain.cpp | 2 +- .../Conversion/LoSPNtoCPU/StructurePatterns.h | 18 ++++++++++++++--- .../Vectorization/SLP/SLPVectorizationPass.h | 16 +++++---------- .../LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp | 20 ++++++++++++++++--- .../LoSPNtoCPU/StructurePatterns.cpp | 1 + .../SLP/SLPVectorizationPass.cpp | 2 +- 8 files changed, 42 insertions(+), 42 deletions(-) diff --git a/compiler/src/codegen/mlir/conversion/LoSPNtoCPUConversion.cpp b/compiler/src/codegen/mlir/conversion/LoSPNtoCPUConversion.cpp index 7eb84043..a3ad3d24 100644 --- a/compiler/src/codegen/mlir/conversion/LoSPNtoCPUConversion.cpp +++ b/compiler/src/codegen/mlir/conversion/LoSPNtoCPUConversion.cpp @@ -6,10 +6,8 @@ #include "LoSPNtoCPUConversion.h" #include "LoSPNtoCPU/LoSPNtoCPUConversionPasses.h" #include "LoSPNtoCPU/Vectorization/VectorOptimizationPasses.h" -#include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h" #include "mlir/Transforms/Passes.h" #include "mlir/Dialect/Tensor/Transforms/Passes.h" -#include void spnc::LoSPNtoCPUConversion::initializePassPipeline(mlir::PassManager* pm, mlir::MLIRContext* ctx) { bool vectorize = spnc::option::cpuVectorize.get(*this->config); @@ -19,9 +17,6 @@ void spnc::LoSPNtoCPUConversion::initializePassPipeline(mlir::PassManager* pm, m if (useShuffle) { pm->addPass(mlir::spn::createReplaceGatherWithShufflePass()); } - if (kernelInfo->batchSize == 1) { - pm->addNestedPass(mlir::spn::createSLPVectorizationPass()); - } pm->addPass(mlir::spn::createLoSPNNodeVectorizationPass()); if (useShuffle) { // We need another run of the canonicalizer here to remove lo_spn.to_scalar @@ -41,4 +36,4 @@ void spnc::LoSPNtoCPUConversion::initializePassPipeline(mlir::PassManager* pm, m pm->nest().addPass(mlir::createFinalizingBufferizePass()); pm->nest().addPass(mlir::createBufferDeallocationPass()); pm->addPass(mlir::createCopyRemovalPass()); -} \ No newline at end of file +} diff --git a/compiler/src/codegen/mlir/conversion/LoSPNtoCPUConversion.h b/compiler/src/codegen/mlir/conversion/LoSPNtoCPUConversion.h index c8a5e701..e03f6add 100644 --- a/compiler/src/codegen/mlir/conversion/LoSPNtoCPUConversion.h +++ b/compiler/src/codegen/mlir/conversion/LoSPNtoCPUConversion.h @@ -12,24 +12,8 @@ namespace spnc { struct LoSPNtoCPUConversion : public MLIRPipelineBase { - - public: - - LoSPNtoCPUConversion(ActionWithOutput& _input, - std::shared_ptr ctx, - std::shared_ptr handler, - std::shared_ptr kernelInformation) : - MLIRPipelineBase(_input, std::move(ctx), std::move(handler)), - kernelInfo{std::move(kernelInformation)} {} - - void initializePassPipeline(mlir::PassManager* pm, mlir::MLIRContext* ctx); - - private: - - std::shared_ptr kernelInfo; - using MLIRPipelineBase::MLIRPipelineBase; - + void initializePassPipeline(mlir::PassManager* pm, mlir::MLIRContext* ctx); }; } diff --git a/compiler/src/driver/toolchain/CPUToolchain.cpp b/compiler/src/driver/toolchain/CPUToolchain.cpp index 9aa9e288..33e51688 100644 --- a/compiler/src/driver/toolchain/CPUToolchain.cpp +++ b/compiler/src/driver/toolchain/CPUToolchain.cpp @@ -55,7 +55,7 @@ std::unique_ptr > CPUToolchain::constructJobFromFile(const std::stri //spnPipelineResult = &joinAction; } auto& lospnTransform = job->insertAction(hispn2lospn, ctx, diagHandler, kernelInfo); - auto& lospn2cpu = job->insertAction(lospnTransform, ctx, diagHandler, kernelInfo); + auto& lospn2cpu = job->insertAction(lospnTransform, ctx, diagHandler); auto& cpu2llvm = job->insertAction(lospn2cpu, ctx, diagHandler); // Convert the MLIR module to a LLVM-IR module. diff --git a/mlir/include/Conversion/LoSPNtoCPU/StructurePatterns.h b/mlir/include/Conversion/LoSPNtoCPU/StructurePatterns.h index d8127fbd..8dc3dd31 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/StructurePatterns.h +++ b/mlir/include/Conversion/LoSPNtoCPU/StructurePatterns.h @@ -10,6 +10,7 @@ #include "LoSPN/LoSPNDialect.h" #include "LoSPN/LoSPNOps.h" #include "llvm/Support/Debug.h" +#include "llvm/ADT/SmallPtrSet.h" namespace mlir { namespace spn { @@ -34,11 +35,19 @@ namespace mlir { struct SingleTaskLowering : OpConversionPattern { + SingleTaskLowering(TypeConverter& typeConverter, + MLIRContext* context, + SmallPtrSet& singleBatchFunctions) + : OpConversionPattern(typeConverter, context), singleBatchFunctions{singleBatchFunctions} {} + using OpConversionPattern::OpConversionPattern; LogicalResult matchAndRewrite(low::SPNTask op, ArrayRef operands, ConversionPatternRewriter& rewriter) const override; + + private: + SmallPtrSet& singleBatchFunctions; }; struct BodyLowering : OpConversionPattern { @@ -50,9 +59,12 @@ namespace mlir { ConversionPatternRewriter& rewriter) const override; }; - static void populateLoSPNtoCPUStructurePatterns(OwningRewritePatternList& patterns, MLIRContext* context, - TypeConverter& typeConverter) { - patterns.insert(typeConverter, context); + static void populateLoSPNtoCPUStructurePatterns(OwningRewritePatternList& patterns, + MLIRContext* context, + TypeConverter& typeConverter, + SmallPtrSet& singleBatchFunctions) { + patterns.insert(typeConverter, context); + patterns.insert(typeConverter, context, singleBatchFunctions); patterns.insert(typeConverter, context); } } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h index 0b17c9b5..3b753caa 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h @@ -7,7 +7,6 @@ #define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPVECTORIZATIONPASS_H #include -#include "mlir/IR/Operation.h" #include "mlir/Pass/Pass.h" namespace mlir { @@ -15,21 +14,16 @@ namespace mlir { namespace low { namespace slp { - struct SLPVectorizationPass : public PassWrapper> { - - public: + struct SLPVectorizationPass : public PassWrapper { protected: - void runOnOperation() - override; - - private: + void runOnFunction() override; - }; + }; + } } + std::unique_ptr createSLPVectorizationPass(); } - std::unique_ptr createSLPVectorizationPass(); -} } #endif //SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPVECTORIZATIONPASS_H diff --git a/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp b/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp index 49fafffe..1a37e388 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp @@ -10,9 +10,11 @@ #include "LoSPNtoCPU/NodePatterns.h" #include "LoSPNtoCPU/Vectorization/VectorizationPatterns.h" #include "LoSPNtoCPU/Vectorization/LoSPNVectorizationTypeConverter.h" +#include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h" #include "mlir/Dialect/SCF/SCF.h" #include "mlir/Dialect/Vector/VectorOps.h" #include "mlir/Dialect/Linalg/IR/LinalgOps.h" +#include "mlir/Pass/PassManager.h" void mlir::spn::LoSPNtoCPUStructureConversionPass::runOnOperation() { ConversionTarget target(getContext()); @@ -30,17 +32,29 @@ void mlir::spn::LoSPNtoCPUStructureConversionPass::runOnOperation() { target.addIllegalOp(); OwningRewritePatternList patterns; + // Stores functions that can be SLP-vectorized. + SmallPtrSet singleBatchFunctions; if (vectorize) { // Try to vectorize tasks if vectorization was requested. mlir::spn::populateLoSPNCPUVectorizationStructurePatterns(patterns, &getContext(), typeConverter); } - mlir::spn::populateLoSPNtoCPUStructurePatterns(patterns, &getContext(), typeConverter); + mlir::spn::populateLoSPNtoCPUStructurePatterns(patterns, &getContext(), typeConverter, singleBatchFunctions); - auto op = getOperation(); FrozenRewritePatternList frozenPatterns(std::move(patterns)); - if (failed(applyPartialConversion(op, target, frozenPatterns))) { + if (failed(applyPartialConversion(getOperation(), target, frozenPatterns))) { signalPassFailure(); } + + if (vectorize && !singleBatchFunctions.empty()) { + OpPassManager dynamicPipeline("SLP pipeline"); + dynamicPipeline.addPass(mlir::spn::createSLPVectorizationPass()); + for (auto const& function : singleBatchFunctions) { + if (failed(runPipeline(dynamicPipeline, function))) { + signalPassFailure(); + } + } + } + } std::unique_ptr mlir::spn::createLoSPNtoCPUStructureConversionPass(bool enableVectorization) { diff --git a/mlir/lib/Conversion/LoSPNtoCPU/StructurePatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/StructurePatterns.cpp index 1e332f6c..b11687a8 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/StructurePatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/StructurePatterns.cpp @@ -90,6 +90,7 @@ mlir::LogicalResult mlir::spn::SingleTaskLowering::matchAndRewrite(mlir::spn::lo auto taskFunc = rewriter.create(op->getLoc(), Twine("task_", std::to_string(taskCount++)).str(), funcType); auto taskBlock = taskFunc.addEntryBlock(); + singleBatchFunctions.insert(taskFunc); rewriter.setInsertionPointToStart(taskBlock); // Collect the values replacing the block values of old block inside the task. diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp index d1f3ed96..f56ad728 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp @@ -13,7 +13,7 @@ using namespace mlir::spn::low::slp; -void SLPVectorizationPass::runOnOperation() { +void SLPVectorizationPass::runOnFunction() { llvm::StringRef funcName = getOperation().getName(); if (!funcName.contains("task_")) { return; From eb8020caf8b32a44e94ad47f0cb9a0f2103cd5a7 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Wed, 21 Apr 2021 03:21:04 +0200 Subject: [PATCH 101/385] Fix: reordering failing because block arguments don't have defining ops. --- .../LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp index 3669c0df..1c5b3f31 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp @@ -255,6 +255,12 @@ std::pair SLPGraphBuilder::getBest(Mode const& mode, int SLPGraphBuilder::getLookAheadScore(Value const& last, Value const& candidate, unsigned maxLevel) const { if (maxLevel == 0) { + if (last == candidate) { + return 1; + } + if (last.isa() || candidate.isa()) { + return 0; + } if (last.getDefiningOp()->getName() != candidate.getDefiningOp()->getName()) { return 0; } From 354a61ca861927aa15f5c7c7a50bc84b28ff4b97 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Wed, 21 Apr 2021 03:22:44 +0200 Subject: [PATCH 102/385] Fix: wrong operation name in SLP dynamic pipeline. --- mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp b/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp index 1a37e388..599d2459 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp @@ -46,7 +46,7 @@ void mlir::spn::LoSPNtoCPUStructureConversionPass::runOnOperation() { } if (vectorize && !singleBatchFunctions.empty()) { - OpPassManager dynamicPipeline("SLP pipeline"); + OpPassManager dynamicPipeline("func"); dynamicPipeline.addPass(mlir::spn::createSLPVectorizationPass()); for (auto const& function : singleBatchFunctions) { if (failed(runPipeline(dynamicPipeline, function))) { From 42f1f4dfd9fb44209157be8bb347283f142209e8 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Thu, 22 Apr 2021 02:45:58 +0200 Subject: [PATCH 103/385] Incorporated SLP vectorization patterns into SingleTask lowering. --- .../HiSPNtoLoSPNConversionPasses.h | 36 ++-- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.h | 6 + .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.h | 24 ++- .../SLP/SLPVectorizationPatterns.h | 51 +++-- .../Vectorization/VectorizationPatterns.h | 47 +++- mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt | 1 - .../LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp | 12 -- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp | 70 +++++- .../Vectorization/SLP/SLPSeeding.cpp | 10 +- .../SLP/SLPVectorizationPass.cpp | 48 ----- .../SLP/SLPVectorizationPatterns.cpp | 181 ++-------------- .../VectorizeStructurePatterns.cpp | 201 ++++++++++++++---- mlir/tools/spnc-opt/spnc-opt.cpp | 6 - 13 files changed, 378 insertions(+), 315 deletions(-) delete mode 100644 mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp diff --git a/mlir/include/Conversion/HiSPNtoLoSPN/HiSPNtoLoSPNConversionPasses.h b/mlir/include/Conversion/HiSPNtoLoSPN/HiSPNtoLoSPNConversionPasses.h index 9b51faf1..96be4e80 100644 --- a/mlir/include/Conversion/HiSPNtoLoSPN/HiSPNtoLoSPNConversionPasses.h +++ b/mlir/include/Conversion/HiSPNtoLoSPN/HiSPNtoLoSPNConversionPasses.h @@ -12,40 +12,38 @@ namespace mlir { namespace spn { struct HiSPNtoLoSPNNodeConversionPass : public PassWrapper> { + OperationPass> { public: - explicit HiSPNtoLoSPNNodeConversionPass(bool useLogSpaceComputation) : computeLogSpace{useLogSpaceComputation} {} + explicit HiSPNtoLoSPNNodeConversionPass(bool useLogSpaceComputation) : computeLogSpace{useLogSpaceComputation} {} protected: - void runOnOperation() - override; + void runOnOperation() override; private: - bool computeLogSpace; + bool computeLogSpace; - }; + }; - std::unique_ptr createHiSPNtoLoSPNNodeConversionPass(bool useLogSpaceComputation); + std::unique_ptr createHiSPNtoLoSPNNodeConversionPass(bool useLogSpaceComputation); - struct HiSPNtoLoSPNQueryConversionPass : public PassWrapper> { + struct HiSPNtoLoSPNQueryConversionPass : public PassWrapper> { - public: - explicit HiSPNtoLoSPNQueryConversionPass(bool useLogSpaceComputation) : computeLogSpace{useLogSpaceComputation} {} + public: + explicit HiSPNtoLoSPNQueryConversionPass(bool useLogSpaceComputation) : computeLogSpace{useLogSpaceComputation} {} - protected: - void runOnOperation() - override; + protected: + void runOnOperation() override; - private: - bool computeLogSpace; + private: + bool computeLogSpace; -}; + }; -std::unique_ptr createHiSPNtoLoSPNQueryConversionPass(bool useLogSpaceComputation); + std::unique_ptr createHiSPNtoLoSPNQueryConversionPass(bool useLogSpaceComputation); -} + } } #endif //SPNC_MLIR_INCLUDE_CONVERSION_HISPNTOLOSPN_HISPNTOLOSPNCONVERSIONPASSES_H diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h index 0e65e21f..2f8a0919 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h @@ -26,6 +26,8 @@ namespace mlir { bool isUniform() const; bool contains(Value const& value) const; + bool containsBlockArgs() const; + bool vectorizable() const; size_t numLanes() const; @@ -69,6 +71,10 @@ namespace mlir { SLPNode* getOperand(size_t index) const; std::vector getOperands() const; size_t numOperands() const; + + static SmallVector postOrder(SLPNode* root); + static DenseMap> escapingLanesMap(SLPNode* root); + /* bool operator==(SLPNode const& other) { return std::tie(vectors) == std::tie(other.vectors); diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h index bb4786fa..5529e568 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h @@ -6,9 +6,11 @@ #ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPUTIL_H #define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPUTIL_H -#include "mlir/IR/Operation.h" -#include "LoSPN/LoSPNOps.h" #include "SLPNode.h" +#include "LoSPN/LoSPNOps.h" +#include "mlir/IR/Operation.h" +#include "mlir/IR/PatternMatch.h" +#include "mlir/Dialect/Vector/VectorOps.h" namespace mlir { namespace spn { @@ -95,6 +97,11 @@ namespace mlir { Operation* firstUser = nullptr; while (begin != end) { for (auto* user : begin->getUsers()) { + if (firstUser) { + // TODO: proper dominance analysis + firstUser->dump(); + } + user->dump(); if (!firstUser || user->isBeforeInBlock(firstUser)) { firstUser = user; } @@ -104,6 +111,19 @@ namespace mlir { return firstUser; } + template + Value broadcastFirstInsertRest(ValueIterator begin, + ValueIterator end, + VectorType const& vectorType, + PatternRewriter& rewriter) { + Value vectorOp = rewriter.create(begin->getLoc(), vectorType, *begin); + unsigned position = 1; + while (++begin != end) { + vectorOp = rewriter.create(begin->getLoc(), *begin, vectorOp, position++); + } + return vectorOp; + } + void dumpOpTree(vector_t const& values); void dumpSLPGraph(SLPNode const& root); void dumpSLPNode(SLPNode const& node); diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h index 51456593..032719c0 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h @@ -13,6 +13,7 @@ #include "LoSPN/LoSPNOps.h" #include "mlir/Dialect/StandardOps/IR/Ops.h" #include "mlir/IR/PatternMatch.h" +#include "mlir/Transforms/DialectConversion.h" #include "mlir/Rewrite/FrozenRewritePatternList.h" #include "mlir/Rewrite/PatternApplicator.h" #include "llvm/ADT/SmallPtrSet.h" @@ -23,65 +24,73 @@ namespace mlir { namespace low { namespace slp { - /// A custom PatternRewriter for rewriting SLP node vectors. - class SLPVectorPatternRewriter : public mlir::PatternRewriter { - public: - explicit SLPVectorPatternRewriter(mlir::MLIRContext* ctx); - LogicalResult rewrite(SLPNode* root); - }; - template - class SLPVectorizationPattern : public OpRewritePattern { + class SLPVectorizationPattern : public OpConversionPattern { public: - SLPVectorizationPattern(MLIRContext* context, - PatternBenefit benefit, + SLPVectorizationPattern(TypeConverter& typeConverter, + MLIRContext* context, NodeVector* const& vector, DenseMap& vectorizedOps, - SmallPtrSet& erasableOps) : OpRewritePattern{ - context, benefit}, vector{vector}, vectorizedOps{vectorizedOps}, erasableOps{erasableOps} {} + SmallPtrSet& erasableOps) : OpConversionPattern{context}, + vector{vector}, + vectorizedOps{vectorizedOps}, + erasableOps{erasableOps} {} protected: /// The current vector being transformed. NodeVector* const& vector; + /// Vector operations created for the SLP node vectors. DenseMap& vectorizedOps; + SmallPtrSet& erasableOps; }; struct VectorizeConstant : public SLPVectorizationPattern { using SLPVectorizationPattern::SLPVectorizationPattern; - LogicalResult matchAndRewrite(ConstantOp constantOp, PatternRewriter& rewriter) const override; + LogicalResult matchAndRewrite(ConstantOp constantOp, + ArrayRef operands, + ConversionPatternRewriter& rewriter) const override; }; struct VectorizeBatchRead : public SLPVectorizationPattern { using SLPVectorizationPattern::SLPVectorizationPattern; - LogicalResult matchAndRewrite(SPNBatchRead batchReadOp, PatternRewriter& rewriter) const override; + LogicalResult matchAndRewrite(SPNBatchRead batchReadOp, + ArrayRef operands, + ConversionPatternRewriter& rewriter) const override; }; struct VectorizeAdd : public SLPVectorizationPattern { using SLPVectorizationPattern::SLPVectorizationPattern; - LogicalResult matchAndRewrite(SPNAdd addOp, PatternRewriter& rewriter) const override; + LogicalResult matchAndRewrite(SPNAdd addOp, + ArrayRef operands, + ConversionPatternRewriter& rewriter) const override; }; struct VectorizeMul : public SLPVectorizationPattern { using SLPVectorizationPattern::SLPVectorizationPattern; - LogicalResult matchAndRewrite(SPNMul mulOp, PatternRewriter& rewriter) const override; + LogicalResult matchAndRewrite(SPNMul mulOp, + ArrayRef operands, + ConversionPatternRewriter& rewriter) const override; }; struct VectorizeGaussian : public SLPVectorizationPattern { using SLPVectorizationPattern::SLPVectorizationPattern; - LogicalResult matchAndRewrite(SPNGaussianLeaf gaussianOp, PatternRewriter& rewriter) const override; + LogicalResult matchAndRewrite(SPNGaussianLeaf gaussianOp, + ArrayRef operands, + ConversionPatternRewriter& rewriter) const override; }; static void populateSLPVectorizationPatterns(OwningRewritePatternList& patterns, + TypeConverter& typeConverter, MLIRContext* context, NodeVector* const& vector, DenseMap& vectorizedOps, SmallPtrSet& erasableOps) { - patterns.insert(context, 2, vector, vectorizedOps, erasableOps); - patterns.insert(context, 2, vector, vectorizedOps, erasableOps); - patterns.insert(context, 2, vector, vectorizedOps, erasableOps); - patterns.insert(context, 2, vector, vectorizedOps, erasableOps); + patterns.insert(typeConverter, context, vector, vectorizedOps, erasableOps); + patterns.insert(typeConverter, context, vector, vectorizedOps, erasableOps); + patterns.insert(typeConverter, context, vector, vectorizedOps, erasableOps); + patterns.insert(typeConverter, context, vector, vectorizedOps, erasableOps); } } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/VectorizationPatterns.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/VectorizationPatterns.h index 57f2c49c..0a61e221 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/VectorizationPatterns.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/VectorizationPatterns.h @@ -14,10 +14,51 @@ namespace mlir { namespace spn { - struct VectorizeBatchTask : OpConversionPattern { - + struct VectorizeTask : public OpConversionPattern { + + public: + VectorizeTask(TypeConverter& typeConverter, + MLIRContext* context, + PatternBenefit benefit, + bool requireAllOpsVectorizable) : OpConversionPattern< + low::SPNTask>(typeConverter, context, benefit), requireAllOpsVectorizable{requireAllOpsVectorizable} {} + + protected: + LogicalResult createFunctionIfVectorizable(low::SPNTask task, + ArrayRef operands, + ConversionPatternRewriter& rewriter, + FuncOp* function) const; + + private: using OpConversionPattern::OpConversionPattern; + bool requireAllOpsVectorizable; + }; + + struct VectorizeSingleTask : public VectorizeTask { + + public: + VectorizeSingleTask(TypeConverter& typeConverter, MLIRContext* context, PatternBenefit benefit) : VectorizeTask( + typeConverter, + context, + benefit, + false) {} + + protected: + LogicalResult matchAndRewrite(low::SPNTask task, + ArrayRef operands, + ConversionPatternRewriter& rewriter) const override; + }; + + struct VectorizeBatchTask : public VectorizeTask { + + public: + VectorizeBatchTask(TypeConverter& typeConverter, MLIRContext* context, PatternBenefit benefit) : VectorizeTask( + typeConverter, + context, + benefit, + true) {} + protected: LogicalResult matchAndRewrite(low::SPNTask op, ArrayRef operands, ConversionPatternRewriter& rewriter) const override; @@ -26,7 +67,7 @@ namespace mlir { static void populateLoSPNCPUVectorizationStructurePatterns(OwningRewritePatternList& patterns, MLIRContext* context, TypeConverter& typeConverter) { - patterns.insert(typeConverter, context, 5); + patterns.insert(typeConverter, context, 5); } struct VectorizeBatchRead : public OpConversionPattern { diff --git a/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt b/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt index 0d743cea..4d6feafe 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt +++ b/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt @@ -10,7 +10,6 @@ add_mlir_conversion_library(MLIRLoSPNtoCPUTransforms Vectorization/SLP/SLPSeeding.cpp Vectorization/SLP/SLPGraphBuilder.cpp Vectorization/SLP/SLPUtil.cpp - Vectorization/SLP/SLPVectorizationPass.cpp Vectorization/SLP/SLPVectorizationPatterns.cpp LoSPNtoCPUConversionPasses.cpp diff --git a/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp b/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp index 599d2459..17bb2304 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp @@ -10,11 +10,9 @@ #include "LoSPNtoCPU/NodePatterns.h" #include "LoSPNtoCPU/Vectorization/VectorizationPatterns.h" #include "LoSPNtoCPU/Vectorization/LoSPNVectorizationTypeConverter.h" -#include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h" #include "mlir/Dialect/SCF/SCF.h" #include "mlir/Dialect/Vector/VectorOps.h" #include "mlir/Dialect/Linalg/IR/LinalgOps.h" -#include "mlir/Pass/PassManager.h" void mlir::spn::LoSPNtoCPUStructureConversionPass::runOnOperation() { ConversionTarget target(getContext()); @@ -45,16 +43,6 @@ void mlir::spn::LoSPNtoCPUStructureConversionPass::runOnOperation() { signalPassFailure(); } - if (vectorize && !singleBatchFunctions.empty()) { - OpPassManager dynamicPipeline("func"); - dynamicPipeline.addPass(mlir::spn::createSLPVectorizationPass()); - for (auto const& function : singleBatchFunctions) { - if (failed(runPipeline(dynamicPipeline, function))) { - signalPassFailure(); - } - } - } - } std::unique_ptr mlir::spn::createLoSPNtoCPUStructureConversionPass(bool enableVectorization) { diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp index 18de42a8..f4d27e97 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp @@ -4,6 +4,7 @@ // #include "LoSPNtoCPU/Vectorization/SLP/SLPNode.h" +#include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" #include "LoSPN/LoSPNOps.h" #include "mlir/Dialect/StandardOps/IR/Ops.h" @@ -12,7 +13,7 @@ using namespace mlir::spn; using namespace mlir::spn::low; using namespace mlir::spn::low::slp; -// === NodeVector === +// === NodeVector === // NodeVector::NodeVector(vector_t const& values) { assert(!values.empty()); @@ -43,6 +44,18 @@ bool NodeVector::contains(Value const& value) const { return std::find(std::begin(values), std::end(values), value) != std::end(values); } +bool NodeVector::containsBlockArgs() const { + return std::any_of(std::begin(values), std::end(values), [&](Value const& value) { + return value.isa(); + }); +} + +bool NodeVector::vectorizable() const { + return std::all_of(std::begin(values), std::end(values), [&](Value const& value) { + return low::slp::vectorizable(value); + }); +} + size_t NodeVector::numLanes() const { return values.size(); } @@ -73,7 +86,7 @@ Value const& NodeVector::operator[](size_t lane) const { return values[lane]; } -// === SLPNode === +// === SLPNode === // SLPNode::SLPNode(SmallVector const& operations) { addVector(operations); @@ -159,3 +172,56 @@ std::vector SLPNode::getOperands() const { size_t SLPNode::numOperands() const { return operandNodes.size(); } + +// === Utilities === // + +SmallVector SLPNode::postOrder(SLPNode* root) { + SmallVector order; + for (auto* operand : root->getOperands()) { + assert(std::find(std::begin(order), std::end(order), operand) == std::end(order)); + order.append(postOrder(operand)); + } + order.emplace_back(root); + return order; +} + +/// Traverse the entire graph starting at root and gather lanes of each node vector that has escaping uses. +DenseMap> SLPNode::escapingLanesMap(SLPNode* root) { + DenseMap> outsideUses; + for (auto* node : postOrder(root)) { + for (size_t i = 0; i < node->numVectors(); ++i) { + auto* vector = node->getVector(i); + for (size_t lane = 0; lane < vector->numLanes(); ++lane) { + auto const& element = vector->getElement(lane); + // Skip duplicate (splat) values. + if (outsideUses[vector].count(element)) { + continue; + } + outsideUses[vector][element] = std::distance(std::begin(element.getUses()), std::end(element.getUses())); + for (size_t j = 0; j < vector->numOperands(); ++j) { + auto* operand = vector->getOperand(j); + assert(outsideUses[operand][operand->getElement(lane)] > 0); + outsideUses[operand][operand->getElement(lane)]--; + } + } + } + } + + DenseMap> escapingLanes; + for (auto const& entry : outsideUses) { + auto* vector = entry.first; + for (auto const& useAndCount : entry.second) { + auto const& value = useAndCount.first; + auto const& count = useAndCount.second; + if (count > 0) { + for (size_t lane = 0; lane < vector->numLanes(); ++lane) { + if (vector->getElement(lane) == value) { + escapingLanes[vector].emplace_back(lane); + break; + } + } + } + } + } + return escapingLanes; +} diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp index e797befa..510e7050 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp @@ -39,16 +39,20 @@ vector_t SeedAnalysis::getSeed(unsigned width, SearchMode const& mode) const { rootOp->walk([&](Operation* op) { if (!vectorizable(op)) { - return; + return WalkResult::advance(); } - auto const& value = op->getResult(0); + auto value = op->getResult(0); auto const& depth = opDepths.lookup(value); if (depth < log2(width)) { - return; + return WalkResult::advance(); } bool needsNewSeed = true; for (auto& seed : seedsByOpName[op->getName().getStringRef()]) { if (seed.size() < width && opDepths.lookup(seed.front()) == depth) { + // Cannot use values for seeds that are defined in different scopes. + if (seed.front().getParentRegion() != value.getParentRegion()) { + continue; + } seed.emplace_back(value); needsNewSeed = false; break; diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp deleted file mode 100644 index f56ad728..00000000 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.cpp +++ /dev/null @@ -1,48 +0,0 @@ -// -// This file is part of the SPNC project. -// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. -// - -#include "LoSPN/LoSPNDialect.h" -#include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h" -#include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" -#include "LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h" -#include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h" -#include "LoSPNtoCPU/Vectorization/TargetInformation.h" -#include "mlir/Dialect/Linalg/IR/LinalgOps.h" - -using namespace mlir::spn::low::slp; - -void SLPVectorizationPass::runOnFunction() { - llvm::StringRef funcName = getOperation().getName(); - if (!funcName.contains("task_")) { - return; - } - - auto function = getOperation(); - - auto& seedAnalysis = getAnalysis(); - - auto computationType = function.getArguments().back().getType().dyn_cast().getElementType(); - auto width = TargetInformation::nativeCPUTarget().getHWVectorEntries(computationType); - - auto const& seed = seedAnalysis.getSeed(width, SearchMode::UseBeforeDef); - assert(!seed.empty() && "couldn't find a seed!"); - // dumpOpTree(seed); - SLPGraphBuilder builder{3}; - auto graph = builder.build(seed); - // dumpSLPGraph(*graph); - // ==== // - - SLPVectorPatternRewriter rewriter(&getContext()); - if (failed(rewriter.rewrite(graph.get()))) { - signalPassFailure(); - } - - getOperation()->dump(); - -} - -std::unique_ptr mlir::spn::createSLPVectorizationPass() { - return std::make_unique(); -} diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp index 442ae869..688b12f3 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp @@ -18,19 +18,6 @@ using namespace mlir::spn::low::slp; // Helper functions in anonymous namespace. namespace { - template - Value broadcastFirstInsertRest(ValueIterator begin, - ValueIterator end, - VectorType const& vectorType, - PatternRewriter& rewriter) { - Value vectorOp = rewriter.create(begin->getLoc(), vectorType, *begin); - unsigned position = 1; - while (++begin != end) { - vectorOp = rewriter.create(begin->getLoc(), *begin, vectorOp, position++); - } - return vectorOp; - } - template DenseElementsAttr denseFloatingPoints(AttributeIterator begin, AttributeIterator end, VectorType const& vectorType) { if (vectorType.getElementType().cast().getWidth() == 32) { @@ -65,149 +52,11 @@ namespace { return DenseElementsAttr::get(vectorType, static_cast>(array)); } - llvm::SmallVector postOrder(SLPNode* root) { - llvm::SmallVector order; - for (auto* operand : root->getOperands()) { - assert(std::find(std::begin(order), std::end(order), operand) == std::end(order)); - order.append(postOrder(operand)); - } - order.emplace_back(root); - return order; - } - - DenseMap> getEscapingLanesMap(SLPNode* root) { - DenseMap> outsideUses; - for (auto* node : postOrder(root)) { - for (size_t i = 0; i < node->numVectors(); ++i) { - auto* vector = node->getVector(i); - for (size_t lane = 0; lane < vector->numLanes(); ++lane) { - auto const& element = vector->getElement(lane); - // Skip duplicate (splat) values. - if (outsideUses[vector].count(element)) { - continue; - } - outsideUses[vector][element] = std::distance(std::begin(element.getUses()), std::end(element.getUses())); - for (size_t j = 0; j < vector->numOperands(); ++j) { - auto* operand = vector->getOperand(j); - assert(outsideUses[operand][operand->getElement(lane)] > 0); - outsideUses[operand][operand->getElement(lane)]--; - } - } - } - } - - DenseMap> escapingLanes; - for (auto const& entry : outsideUses) { - auto* vector = entry.first; - for (auto const& useAndCount : entry.second) { - auto const& value = useAndCount.first; - auto const& count = useAndCount.second; - if (count > 0) { - for (size_t lane = 0; lane < vector->numLanes(); ++lane) { - if (vector->getElement(lane) == value) { - escapingLanes[vector].emplace_back(lane); - break; - } - } - } - } - } - return escapingLanes; - } - - bool containsBlockArgs(NodeVector* vector) { - return std::any_of(vector->begin(), vector->end(), [&](Value const& value) { - return value.isa(); - }); - } - - template - bool valuesVectorizable(ValueIterator begin, ValueIterator end) { - return std::all_of(begin, end, [&](Value const& value) { - return vectorizable(value); - }); - } - -} - -SLPVectorPatternRewriter::SLPVectorPatternRewriter(mlir::MLIRContext* ctx) : PatternRewriter{ctx} {} - -LogicalResult SLPVectorPatternRewriter::rewrite(SLPNode* root) { - - // The current vector being transformed. - NodeVector* vector = nullptr; - - // Maps individual SLP node vectors to finished vector operations. - DenseMap vectorizedOps; - - // Marks operations as potentially erasable (potentially because they might have escaping uses). - SmallPtrSet erasableOps; - - // Stores escaping uses for vector extractions that might be necessary later on. - auto const& escapingLanes = getEscapingLanesMap(root); - - // Prevent extracting/removing values more than once (happens in splat mode, if they appear in multiple vectors, ...). - SmallPtrSet finishedValues; - - OwningRewritePatternList patterns; - populateSLPVectorizationPatterns(patterns, context, vector, vectorizedOps, erasableOps); - FrozenRewritePatternList frozenPatterns(std::move(patterns)); - - PatternApplicator applicator{frozenPatterns}; - applicator.applyDefaultCostModel(); - - // Traverse the SLP graph in postorder and apply the vectorization patterns. - for (auto* node : postOrder(root)) { - - // Also traverse nodes in postorder to properly handle multinodes. - for (size_t vectorIndex = node->numVectors(); vectorIndex-- > 0;) { - vector = node->getVector(vectorIndex); - if (!valuesVectorizable(vector->begin(), vector->end())) { - continue; - } - if (containsBlockArgs(vector)) { - auto const& elementType = vector->getElement(0).getType(); - auto const& type = VectorType::get(static_cast(vector->numLanes()), elementType); - setInsertionPoint(firstUser(vector->begin(), vector->end())); - auto vectorOp = broadcastFirstInsertRest(vector->begin(), vector->end(), type, *this); - vectorizedOps[vector] = vectorOp.getDefiningOp(); - } else { - auto* vectorOp = vector->begin()->getDefiningOp(); - if (failed(applicator.matchAndRewrite(vectorOp, *this))) { - vectorOp->emitOpError("SLP pattern application failed (did you forget to specify the pattern?)"); - } - } - } - - // Create vector extractions for escaping uses & erase superfluous operations. - for (size_t vectorIndex = node->numVectors(); vectorIndex-- > 0;) { - vector = node->getVector(vectorIndex); - if (vectorizedOps.count(vector) && erasableOps.count(vector)) { - for (size_t lane = 0; lane < vector->numLanes(); ++lane) { - auto const& vectorValue = vector->getElement(lane); - if (finishedValues.contains(vectorValue)) { - continue; - } - auto const& lanesToExtract = escapingLanes.lookup(vector); - if (std::find(std::begin(lanesToExtract), std::end(lanesToExtract), lane) != std::end(lanesToExtract)) { - auto const& source = vectorizedOps[vector]->getResult(0); - setInsertionPoint(firstOccurrence(std::begin(vectorValue.getUsers()), std::end(vectorValue.getUsers()))); - auto extractOp = create(vectorValue.getLoc(), source, lane); - vectorValue.replaceAllUsesWith(extractOp.result()); - } else { - vectorValue.dropAllUses(); - } - finishedValues.insert(vectorValue); - eraseOp(vectorValue.getDefiningOp()); - } - } - } - } - - return success(); } -LogicalResult VectorizeConstant::matchAndRewrite(ConstantOp constantOp, PatternRewriter& rewriter) const { +LogicalResult VectorizeConstant::matchAndRewrite(ConstantOp constantOp, + ArrayRef operands, + ConversionPatternRewriter& rewriter) const { auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), constantOp.getType()); @@ -233,7 +82,9 @@ LogicalResult VectorizeConstant::matchAndRewrite(ConstantOp constantOp, PatternR return success(); } -LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead batchReadOp, PatternRewriter& rewriter) const { +LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead batchReadOp, + ArrayRef operands, + ConversionPatternRewriter& rewriter) const { auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), batchReadOp.getType()); @@ -256,8 +107,10 @@ LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead batchReadOp, Patt return success(); } -LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd addOp, PatternRewriter& rewriter) const { - +LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd addOp, + ArrayRef operands, + ConversionPatternRewriter& rewriter) const { +/* auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), addOp.getType()); rewriter.setInsertionPoint(firstUser(vector->begin(), vector->end())); @@ -281,12 +134,14 @@ LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd addOp, PatternRewriter& rewri vectorizedOps[vector] = vectorAddOp; erasableOps.insert(vector); - +*/ return success(); } -LogicalResult VectorizeMul::matchAndRewrite(SPNMul mulOp, PatternRewriter& rewriter) const { - +LogicalResult VectorizeMul::matchAndRewrite(SPNMul mulOp, + ArrayRef operands, + ConversionPatternRewriter& rewriter) const { +/* auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), mulOp.getType()); rewriter.setInsertionPoint(firstUser(vector->begin(), vector->end())); @@ -310,11 +165,13 @@ LogicalResult VectorizeMul::matchAndRewrite(SPNMul mulOp, PatternRewriter& rewri vectorizedOps[vector] = vectorAddOp; erasableOps.insert(vector); - +*/ return success(); } -LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, PatternRewriter& rewriter) const { +LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, + ArrayRef operands, + ConversionPatternRewriter& rewriter) const { auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), gaussianOp.getType()); auto const& firstValue = firstOccurrence(vector->begin(), vector->end()); diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp index 50e293b7..a37b7c50 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp @@ -3,72 +3,201 @@ // Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. // -#include #include "LoSPNtoCPU/Vectorization/VectorizationPatterns.h" #include "LoSPNtoCPU/Vectorization/TargetInformation.h" -#include "llvm/Support/FormatVariadic.h" -#include "mlir/Dialect/StandardOps/IR/Ops.h" +#include "LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h" +#include "LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h" +#include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" +#include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h" #include "mlir/Dialect/SCF/SCF.h" -#include "mlir/IR/BuiltinOps.h" +#include "mlir/IR/BlockAndValueMapping.h" +#include "mlir/Transforms/GreedyPatternRewriteDriver.h" +#include "llvm/Support/FormatVariadic.h" -mlir::LogicalResult mlir::spn::VectorizeBatchTask::matchAndRewrite(mlir::spn::low::SPNTask op, - llvm::ArrayRef operands, - mlir::ConversionPatternRewriter& rewriter) const { +mlir::LogicalResult mlir::spn::VectorizeTask::createFunctionIfVectorizable(low::SPNTask task, + ArrayRef operands, + ConversionPatternRewriter& rewriter, + FuncOp* function) const { static int taskCount = 0; - if (op.batchSize() <= 1) { - return rewriter.notifyMatchFailure(op, - "Specialized for batch vectorization, does not match for batchSize == 1"); - } - assert(operands.back().getType().isa()); auto computationType = operands.back().getType().dyn_cast().getElementType(); auto hwVectorWidth = TargetInformation::nativeCPUTarget().getHWVectorEntries(computationType); if (hwVectorWidth <= 1) { - return rewriter.notifyMatchFailure(op, + return rewriter.notifyMatchFailure(task, llvm::formatv( "No vectorization possible for data-type {} on the requested target", computationType)); } - // Check if all nodes can be vectorized before trying to do so. - auto allVectorizable = op.body().walk([hwVectorWidth](low::LoSPNVectorizable vOp) { - if (!vOp.isVectorizable(hwVectorWidth)) { - vOp.emitRemark() << "Operation cannot be vectorized with vector width " << hwVectorWidth; - return WalkResult::interrupt(); - } - return WalkResult::advance(); - }); + if (requireAllOpsVectorizable) { + // Check if all nodes can be vectorized before trying to do so. + auto allVectorizable = task.body().walk([hwVectorWidth](low::LoSPNVectorizable vOp) { + if (!vOp.isVectorizable(hwVectorWidth)) { + vOp.emitRemark() << "Operation cannot be vectorized with vector width " << hwVectorWidth; + return WalkResult::interrupt(); + } + return WalkResult::advance(); + }); - if (allVectorizable.wasInterrupted()) { - return rewriter.notifyMatchFailure(op, "Not all nested operations can be vectorized, aborting vectorization"); + if (allVectorizable.wasInterrupted()) { + return rewriter.notifyMatchFailure(task, "Not all nested operations can be vectorized, aborting vectorization"); + } } // Let the user know which vector width will be used. - op->emitRemark() << "Attempting to vectorize with vector width " << hwVectorWidth - << " for data-type " << computationType; + task->emitRemark() << "Attempting to vectorize with vector width " << hwVectorWidth << " for data-type " + << computationType; // Emit a warning if the target vector width does not divide the requested batch size. // This will cause a part of each batch (batchSize % vectorWidth elements) to be processed // by the scalar epilog loop instead of the vectorized loop. - if ((op.batchSize() % hwVectorWidth) != 0) { - op.emitWarning() << "The target vector width " << hwVectorWidth - << " does not divide the requested batch size " << op.batchSize() - << "; This can result in degraded performance. " - << "Choose the batch size as a multiple of the vector width " - << hwVectorWidth; + if ((task.batchSize() % hwVectorWidth) != 0) { + task.emitWarning() << "The target vector width " << hwVectorWidth << " does not divide the requested batch size " + << task.batchSize() << "; This can result in degraded performance. " + << "Choose the batch size as a multiple of the vector width " << hwVectorWidth; } - auto restore = rewriter.saveInsertionPoint(); - rewriter.setInsertionPointToStart(op->getParentOfType().getBody()); + rewriter.setInsertionPointToStart(task->getParentOfType().getBody()); SmallVector inputTypes; for (auto operand : operands) { inputTypes.push_back(operand.getType()); } auto funcType = FunctionType::get(rewriter.getContext(), inputTypes, {}); - auto taskFunc = rewriter.create(op->getLoc(), Twine("vec_task_", std::to_string(taskCount++)).str(), - funcType); + *function = rewriter.create(task->getLoc(), Twine("vec_task_", std::to_string(taskCount++)).str(), funcType); + return success(); +} + +mlir::LogicalResult mlir::spn::VectorizeSingleTask::matchAndRewrite(mlir::spn::low::SPNTask task, + llvm::ArrayRef operands, + mlir::ConversionPatternRewriter& rewriter) const { + + if (task.batchSize() > 1) { + return rewriter.notifyMatchFailure(task, + "Specialized for single batch vectorization, does not match for batchSize > 1"); + } + + auto restore = rewriter.saveInsertionPoint(); + + FuncOp taskFunc; + if (failed(createFunctionIfVectorizable(task, operands, rewriter, &taskFunc))) { + return failure(); + } + + assert(operands.back().getType().isa()); + auto computationType = operands.back().getType().dyn_cast().getElementType(); + auto hwVectorWidth = TargetInformation::nativeCPUTarget().getHWVectorEntries(computationType); + + auto taskBlock = taskFunc.addEntryBlock(); + rewriter.setInsertionPointToStart(taskBlock); + rewriter.mergeBlocks(&task.body().front(), taskBlock, /*task.body().front().getArguments()*/{}); + + // Apply SLP vectorization. + task->emitRemark() << "Computing new seed for SLP vectorization..."; + low::slp::SeedAnalysis seedAnalysis{taskFunc}; + auto const& seed = seedAnalysis.getSeed(hwVectorWidth, low::slp::SearchMode::UseBeforeDef); + assert(!seed.empty() && "couldn't find a seed!"); + low::slp::dumpOpTree(seed); + seed.front().getDefiningOp()->getBlock()->dump(); + low::slp::SLPGraphBuilder builder{3}; + auto graph = builder.build(seed); + + task->emitRemark() << "Transforming SLP graph back into vectorized ops..."; + // Maps individual SLP node vectors to finished vector operations. + DenseMap vectorizedOps; + // Marks operations as potentially erasable (potentially because they might have escaping uses). + SmallPtrSet erasableOps; + // Stores escaping uses for vector extractions that might be necessary later on. + auto const& escapingLanes = low::slp::SLPNode::escapingLanesMap(graph.get()); + // Prevent extracting/removing values more than once (happens in splat mode, if they appear in multiple vectors, ...). + SmallPtrSet finishedValues; + // The current vector being transformed. + low::slp::NodeVector* vector = nullptr; + + OwningRewritePatternList patterns; + populateSLPVectorizationPatterns(patterns, *typeConverter, rewriter.getContext(), vector, vectorizedOps, erasableOps); + FrozenRewritePatternList frozenPatterns(std::move(patterns)); + + // Traverse the SLP graph in postorder and apply the vectorization patterns. + for (auto* node : low::slp::SLPNode::postOrder(graph.get())) { + + // Also traverse nodes in postorder to properly handle multinodes. + for (size_t vectorIndex = node->numVectors(); vectorIndex-- > 0;) { + vector = node->getVector(vectorIndex); + // This assumes that there exist other vectors that use the current node vector directly (or don't need it), e.g. + // batch reads that get lowered to vector loads instead of using a vector of indices as input. + if (!vector->vectorizable()) { + continue; + } + if (vector->containsBlockArgs()) { + auto const& type = VectorType::get(static_cast(vector->numLanes()), computationType); + auto const& insertionPoint = rewriter.saveInsertionPoint(); + rewriter.setInsertionPoint(low::slp::firstUser(vector->begin(), vector->end())); + auto vectorOp = low::slp::broadcastFirstInsertRest(vector->begin(), vector->end(), type, rewriter); + rewriter.restoreInsertionPoint(insertionPoint); + vectorizedOps[vector] = vectorOp.getDefiningOp(); + } else { + auto* vectorOp = vector->begin()->getDefiningOp(); + if (failed(applyOpPatternsAndFold(vectorOp, frozenPatterns))) { + vectorOp->emitOpError("SLP pattern application failed (did you forget to specify the pattern?)"); + } + } + } + + // Create vector extractions for escaping uses & erase superfluous operations. + for (size_t vectorIndex = node->numVectors(); vectorIndex-- > 0;) { + vector = node->getVector(vectorIndex); + if (vectorizedOps.count(vector) && erasableOps.count(vector)) { + for (size_t lane = 0; lane < vector->numLanes(); ++lane) { + auto const& vectorValue = vector->getElement(lane); + if (finishedValues.contains(vectorValue)) { + continue; + } + auto const& lanesToExtract = escapingLanes.lookup(vector); + if (std::find(std::begin(lanesToExtract), std::end(lanesToExtract), lane) != std::end(lanesToExtract)) { + auto const& source = vectorizedOps[vector]->getResult(0); + rewriter.setInsertionPoint(low::slp::firstOccurrence(std::begin(vectorValue.getUsers()), + std::end(vectorValue.getUsers()))); + auto extractOp = rewriter.create(vectorValue.getLoc(), source, lane); + vectorValue.replaceAllUsesWith(extractOp.result()); + } else { + vectorValue.dropAllUses(); + } + finishedValues.insert(vectorValue); + rewriter.eraseOp(vectorValue.getDefiningOp()); + } + } + } + } + + rewriter.restoreInsertionPoint(restore); + rewriter.replaceOpWithNewOp(task, taskFunc, operands); + return success(); + +} + +mlir::LogicalResult mlir::spn::VectorizeBatchTask::matchAndRewrite(mlir::spn::low::SPNTask op, + llvm::ArrayRef operands, + mlir::ConversionPatternRewriter& rewriter) const { + static int taskCount = 0; + + if (op.batchSize() <= 1) { + return rewriter.notifyMatchFailure(op, + "Specialized for batch vectorization, does not match for batchSize == 1"); + } + + auto restore = rewriter.saveInsertionPoint(); + + FuncOp taskFunc; + if (failed(createFunctionIfVectorizable(op, operands, rewriter, &taskFunc))) { + return failure(); + } + + assert(operands.back().getType().isa()); + auto computationType = operands.back().getType().dyn_cast().getElementType(); + auto hwVectorWidth = TargetInformation::nativeCPUTarget().getHWVectorEntries(computationType); + auto taskBlock = taskFunc.addEntryBlock(); rewriter.setInsertionPointToStart(taskBlock); auto numSamples = rewriter.create(op.getLoc(), taskBlock->getArgument(0), 0); @@ -97,7 +226,7 @@ mlir::LogicalResult mlir::spn::VectorizeBatchTask::matchAndRewrite(mlir::spn::lo if (isa(&node)) { continue; } - auto copy = rewriter.clone(node, mapVectorTaskArgs); + rewriter.clone(node, mapVectorTaskArgs); } // Mark all operations contained in the vectorized loop as vectorized. diff --git a/mlir/tools/spnc-opt/spnc-opt.cpp b/mlir/tools/spnc-opt/spnc-opt.cpp index 4315f046..85c1378b 100644 --- a/mlir/tools/spnc-opt/spnc-opt.cpp +++ b/mlir/tools/spnc-opt/spnc-opt.cpp @@ -17,7 +17,6 @@ #include "LoSPN/LoSPNPasses.h" #include "HiSPNtoLoSPN/HiSPNtoLoSPNConversionPasses.h" #include "LoSPNtoCPU/LoSPNtoCPUConversionPasses.h" -#include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h" static llvm::cl::opt inputFilename(llvm::cl::Positional, llvm::cl::desc(""), @@ -99,11 +98,6 @@ int main(int argc, char** argv) { return mlir::spn::createLoSPNNodeVectorizationPass(); }); - mlir::registerPass("slp-vectorize", "Run SLP vectorization of the structure for CPU target", - []() -> std::unique_ptr { - return mlir::spn::createSLPVectorizationPass(); - }); - llvm::InitLLVM y(argc, argv); // Register any pass manager command line options. From ee66a0b889cf996304e472fc2bf2156dc031ccb8 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Thu, 22 Apr 2021 03:02:45 +0200 Subject: [PATCH 104/385] Removed SLPMode files. --- .../Vectorization/SLP/SLPGraphBuilder.h | 15 +++++++- .../LoSPNtoCPU/Vectorization/SLP/SLPMode.h | 36 ------------------- .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.h | 5 --- .../Vectorization/SLP/SLPVectorizationPass.h | 29 --------------- mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt | 1 - .../Vectorization/SLP/SLPGraphBuilder.cpp | 19 ++++++++-- .../LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp | 23 ------------ 7 files changed, 30 insertions(+), 98 deletions(-) delete mode 100644 mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.h delete mode 100644 mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h delete mode 100644 mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h index c6251f47..f5461fbf 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h @@ -7,7 +7,6 @@ #define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPGRAPHBUILDER_H #include "mlir/IR/Operation.h" -#include "SLPMode.h" #include "SLPNode.h" #include "SLPSeeding.h" @@ -26,6 +25,20 @@ namespace mlir { private: + enum Mode { + // look for a constant + CONST, + // look for a consecutive load to that in the previous lane + LOAD, + // look for an operation of the same opcode + OPCODE, + // look for the exact same operation + SPLAT, + // vectorization has failed, give higher priority to others + FAILED + }; + static Mode modeFromValue(Value const& value); + void buildGraph(NodeVector* vector, SLPNode* currentNode) const; void reorderOperands(SLPNode* multinode) const; diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.h deleted file mode 100644 index b89edad8..00000000 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.h +++ /dev/null @@ -1,36 +0,0 @@ -// -// This file is part of the SPNC project. -// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. -// - -#ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPMODE_H -#define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPMODE_H - -#include "mlir/IR/Operation.h" - -namespace mlir { - namespace spn { - namespace low { - namespace slp { - - enum Mode { - // look for a constant - CONST, - // look for a consecutive load to that in the previous lane - LOAD, - // look for an operation of the same opcode - OPCODE, - // look for the exact same operation - SPLAT, - // vectorization has failed, give higher priority to others - FAILED - }; - - Mode modeFromValue(Value const& value); - - } - } - } -} - -#endif //SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPMODE_H diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h index 5529e568..5c926649 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h @@ -97,11 +97,6 @@ namespace mlir { Operation* firstUser = nullptr; while (begin != end) { for (auto* user : begin->getUsers()) { - if (firstUser) { - // TODO: proper dominance analysis - firstUser->dump(); - } - user->dump(); if (!firstUser || user->isBeforeInBlock(firstUser)) { firstUser = user; } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h deleted file mode 100644 index 3b753caa..00000000 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPass.h +++ /dev/null @@ -1,29 +0,0 @@ -// -// This file is part of the SPNC project. -// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. -// - -#ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPVECTORIZATIONPASS_H -#define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPVECTORIZATIONPASS_H - -#include -#include "mlir/Pass/Pass.h" - -namespace mlir { - namespace spn { - namespace low { - namespace slp { - - struct SLPVectorizationPass : public PassWrapper { - - protected: - void runOnFunction() override; - - }; - } - } - std::unique_ptr createSLPVectorizationPass(); - } -} - -#endif //SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPVECTORIZATIONPASS_H diff --git a/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt b/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt index 4d6feafe..f704dc85 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt +++ b/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt @@ -5,7 +5,6 @@ add_mlir_conversion_library(MLIRLoSPNtoCPUTransforms Vectorization/VectorizeNodePatterns.cpp Vectorization/TargetInformation.cpp Vectorization/ReplaceGatherWithShuffle.cpp - Vectorization/SLP/SLPMode.cpp Vectorization/SLP/SLPNode.cpp Vectorization/SLP/SLPSeeding.cpp Vectorization/SLP/SLPGraphBuilder.cpp diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp index 1c5b3f31..113eda9a 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp @@ -180,9 +180,9 @@ void SLPGraphBuilder::reorderOperands(SLPNode* multinode) const { } } -std::pair SLPGraphBuilder::getBest(Mode const& mode, - Value const& last, - SmallVector& candidates) const { +std::pair SLPGraphBuilder::getBest(Mode const& mode, + Value const& last, + SmallVector& candidates) const { Value best; Mode resultMode = mode; SmallVector bestCandidates; @@ -282,3 +282,16 @@ int SLPGraphBuilder::getLookAheadScore(Value const& last, Value const& candidate } return scoreSum; } + +SLPGraphBuilder::Mode SLPGraphBuilder::modeFromValue(Value const& value) { + if (value.isa()) { + return SPLAT; + } + auto* definingOp = value.getDefiningOp(); + if (definingOp->hasTrait()) { + return CONST; + } else if (dyn_cast(definingOp)) { + return LOAD; + } + return OPCODE; +} diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp deleted file mode 100644 index 85f8e427..00000000 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPMode.cpp +++ /dev/null @@ -1,23 +0,0 @@ -// -// This file is part of the SPNC project. -// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. -// - -#include "LoSPN/LoSPNOps.h" -#include "LoSPNtoCPU/Vectorization/SLP/SLPMode.h" - -using namespace mlir; - -mlir::spn::low::slp::Mode mlir::spn::low::slp::modeFromValue(Value const& value) { - if (value.isa()) { - return SPLAT; - } - auto* definingOp = value.getDefiningOp(); - if (definingOp->hasTrait()) { - return CONST; - } else if (dyn_cast(definingOp)) { - return LOAD; - } - return OPCODE; -} - From aa2df7860e60013fcd7cd79c8ec7190d5e554c74 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Thu, 22 Apr 2021 05:14:17 +0200 Subject: [PATCH 105/385] Removed unnecessary single task function tracking. --- .../Conversion/LoSPNtoCPU/StructurePatterns.h | 13 ++----------- .../LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp | 4 +--- .../lib/Conversion/LoSPNtoCPU/StructurePatterns.cpp | 1 - 3 files changed, 3 insertions(+), 15 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/StructurePatterns.h b/mlir/include/Conversion/LoSPNtoCPU/StructurePatterns.h index 8dc3dd31..c6e2fff3 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/StructurePatterns.h +++ b/mlir/include/Conversion/LoSPNtoCPU/StructurePatterns.h @@ -35,19 +35,11 @@ namespace mlir { struct SingleTaskLowering : OpConversionPattern { - SingleTaskLowering(TypeConverter& typeConverter, - MLIRContext* context, - SmallPtrSet& singleBatchFunctions) - : OpConversionPattern(typeConverter, context), singleBatchFunctions{singleBatchFunctions} {} - using OpConversionPattern::OpConversionPattern; LogicalResult matchAndRewrite(low::SPNTask op, ArrayRef operands, ConversionPatternRewriter& rewriter) const override; - - private: - SmallPtrSet& singleBatchFunctions; }; struct BodyLowering : OpConversionPattern { @@ -61,10 +53,9 @@ namespace mlir { static void populateLoSPNtoCPUStructurePatterns(OwningRewritePatternList& patterns, MLIRContext* context, - TypeConverter& typeConverter, - SmallPtrSet& singleBatchFunctions) { + TypeConverter& typeConverter) { patterns.insert(typeConverter, context); - patterns.insert(typeConverter, context, singleBatchFunctions); + patterns.insert(typeConverter, context); patterns.insert(typeConverter, context); } } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp b/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp index 17bb2304..022ed2d4 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp @@ -30,13 +30,11 @@ void mlir::spn::LoSPNtoCPUStructureConversionPass::runOnOperation() { target.addIllegalOp(); OwningRewritePatternList patterns; - // Stores functions that can be SLP-vectorized. - SmallPtrSet singleBatchFunctions; if (vectorize) { // Try to vectorize tasks if vectorization was requested. mlir::spn::populateLoSPNCPUVectorizationStructurePatterns(patterns, &getContext(), typeConverter); } - mlir::spn::populateLoSPNtoCPUStructurePatterns(patterns, &getContext(), typeConverter, singleBatchFunctions); + mlir::spn::populateLoSPNtoCPUStructurePatterns(patterns, &getContext(), typeConverter); FrozenRewritePatternList frozenPatterns(std::move(patterns)); if (failed(applyPartialConversion(getOperation(), target, frozenPatterns))) { diff --git a/mlir/lib/Conversion/LoSPNtoCPU/StructurePatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/StructurePatterns.cpp index b11687a8..1e332f6c 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/StructurePatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/StructurePatterns.cpp @@ -90,7 +90,6 @@ mlir::LogicalResult mlir::spn::SingleTaskLowering::matchAndRewrite(mlir::spn::lo auto taskFunc = rewriter.create(op->getLoc(), Twine("task_", std::to_string(taskCount++)).str(), funcType); auto taskBlock = taskFunc.addEntryBlock(); - singleBatchFunctions.insert(taskFunc); rewriter.setInsertionPointToStart(taskBlock); // Collect the values replacing the block values of old block inside the task. From ef32e829e7b0a27cadb839340d578bdfe7dcc871 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Fri, 23 Apr 2021 01:15:24 +0200 Subject: [PATCH 106/385] Fix: seeding depth computation returning too small depths. --- .../LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp index 510e7050..148dd0a5 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp @@ -17,12 +17,11 @@ namespace { DenseMap opDepths; rootOp->walk([&](Operation* op) { unsigned depth = 0; - for (auto const& value : op->getOperands()) { - if (!opDepths.count(value)) { - opDepths[value] = 0; - } else { - depth = std::max(depth, opDepths[value] + 1); + for (auto const& operand : op->getOperands()) { + if (!opDepths.count(operand)) { + opDepths[operand] = 0; } + depth = std::max(depth, opDepths[operand] + 1); } for (auto const& result : op->getResults()) { opDepths[result] = depth; @@ -62,6 +61,7 @@ vector_t SeedAnalysis::getSeed(unsigned width, SearchMode const& mode) const { vector_t seed{value}; seedsByOpName[op->getName().getStringRef()].emplace_back(seed); } + return WalkResult::advance(); }); SmallVector seeds; From 87d6cf5ad207afe982e99e1f99e1ff68d19e6ddb Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Fri, 23 Apr 2021 01:17:18 +0200 Subject: [PATCH 107/385] Fix: avoid assertion fail when no seeds can be found. --- .../Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp index 148dd0a5..7f1ab503 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp @@ -74,6 +74,11 @@ vector_t SeedAnalysis::getSeed(unsigned width, SearchMode const& mode) const { seeds.emplace_back(seed); } } + + if (seeds.empty()) { + return {}; + } + // Sort the seeds such that either the seeds closest to the beginning of the function come first (DefBeforeUse), // or those closest to the return statement (UseBeforeDef). std::sort(seeds.begin(), seeds.end(), [&](auto const& seed1, auto const& seed2) { From 8c0558c8a901cc1970e29063dff03e9e806ba9c2 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sat, 24 Apr 2021 17:14:23 +0200 Subject: [PATCH 108/385] Nested SLP pattern application progress. --- .../Conversion/LoSPNtoCPU/StructurePatterns.h | 9 +- .../SLP/SLPVectorizationPatterns.h | 43 ++++------ .../Vectorization/VectorizationPatterns.h | 11 ++- .../LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp | 23 +++-- .../LoSPNtoCPU/StructurePatterns.cpp | 5 +- .../SLP/SLPVectorizationPatterns.cpp | 86 ++++++++++--------- .../VectorizeStructurePatterns.cpp | 73 ++++++++++------ 7 files changed, 140 insertions(+), 110 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/StructurePatterns.h b/mlir/include/Conversion/LoSPNtoCPU/StructurePatterns.h index c6e2fff3..f28d54d2 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/StructurePatterns.h +++ b/mlir/include/Conversion/LoSPNtoCPU/StructurePatterns.h @@ -54,10 +54,15 @@ namespace mlir { static void populateLoSPNtoCPUStructurePatterns(OwningRewritePatternList& patterns, MLIRContext* context, TypeConverter& typeConverter) { - patterns.insert(typeConverter, context); - patterns.insert(typeConverter, context); + patterns.insert(typeConverter, context); patterns.insert(typeConverter, context); } + + static void populateLoSPNtoCPUTaskPatterns(OwningRewritePatternList& patterns, + MLIRContext* context, + TypeConverter& typeConverter) { + patterns.insert(typeConverter, context); + } } } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h index 032719c0..20923ac8 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h @@ -13,7 +13,6 @@ #include "LoSPN/LoSPNOps.h" #include "mlir/Dialect/StandardOps/IR/Ops.h" #include "mlir/IR/PatternMatch.h" -#include "mlir/Transforms/DialectConversion.h" #include "mlir/Rewrite/FrozenRewritePatternList.h" #include "mlir/Rewrite/PatternApplicator.h" #include "llvm/ADT/SmallPtrSet.h" @@ -25,14 +24,13 @@ namespace mlir { namespace slp { template - class SLPVectorizationPattern : public OpConversionPattern { + class SLPVectorizationPattern : public OpRewritePattern { public: - SLPVectorizationPattern(TypeConverter& typeConverter, - MLIRContext* context, + SLPVectorizationPattern(MLIRContext* context, NodeVector* const& vector, - DenseMap& vectorizedOps, - SmallPtrSet& erasableOps) : OpConversionPattern{context}, + DenseMap& vectorizedOps, + SmallPtrSet& erasableOps) : OpRewritePattern{context}, vector{vector}, vectorizedOps{vectorizedOps}, erasableOps{erasableOps} {} @@ -41,56 +39,45 @@ namespace mlir { /// The current vector being transformed. NodeVector* const& vector; /// Vector operations created for the SLP node vectors. - DenseMap& vectorizedOps; + DenseMap& vectorizedOps; SmallPtrSet& erasableOps; }; struct VectorizeConstant : public SLPVectorizationPattern { using SLPVectorizationPattern::SLPVectorizationPattern; - LogicalResult matchAndRewrite(ConstantOp constantOp, - ArrayRef operands, - ConversionPatternRewriter& rewriter) const override; + LogicalResult matchAndRewrite(ConstantOp constantOp, PatternRewriter& rewriter) const override; }; struct VectorizeBatchRead : public SLPVectorizationPattern { using SLPVectorizationPattern::SLPVectorizationPattern; - LogicalResult matchAndRewrite(SPNBatchRead batchReadOp, - ArrayRef operands, - ConversionPatternRewriter& rewriter) const override; + LogicalResult matchAndRewrite(SPNBatchRead batchReadOp, PatternRewriter& rewriter) const override; }; struct VectorizeAdd : public SLPVectorizationPattern { using SLPVectorizationPattern::SLPVectorizationPattern; - LogicalResult matchAndRewrite(SPNAdd addOp, - ArrayRef operands, - ConversionPatternRewriter& rewriter) const override; + LogicalResult matchAndRewrite(SPNAdd addOp, PatternRewriter& rewriter) const override; }; struct VectorizeMul : public SLPVectorizationPattern { using SLPVectorizationPattern::SLPVectorizationPattern; - LogicalResult matchAndRewrite(SPNMul mulOp, - ArrayRef operands, - ConversionPatternRewriter& rewriter) const override; + LogicalResult matchAndRewrite(SPNMul mulOp, PatternRewriter& rewriter) const override; }; struct VectorizeGaussian : public SLPVectorizationPattern { using SLPVectorizationPattern::SLPVectorizationPattern; - LogicalResult matchAndRewrite(SPNGaussianLeaf gaussianOp, - ArrayRef operands, - ConversionPatternRewriter& rewriter) const override; + LogicalResult matchAndRewrite(SPNGaussianLeaf gaussianOp, PatternRewriter& rewriter) const override; }; static void populateSLPVectorizationPatterns(OwningRewritePatternList& patterns, - TypeConverter& typeConverter, MLIRContext* context, NodeVector* const& vector, - DenseMap& vectorizedOps, + DenseMap& vectorizedOps, SmallPtrSet& erasableOps) { - patterns.insert(typeConverter, context, vector, vectorizedOps, erasableOps); - patterns.insert(typeConverter, context, vector, vectorizedOps, erasableOps); - patterns.insert(typeConverter, context, vector, vectorizedOps, erasableOps); - patterns.insert(typeConverter, context, vector, vectorizedOps, erasableOps); + patterns.insert(context, vector, vectorizedOps, erasableOps); + patterns.insert(context, vector, vectorizedOps, erasableOps); + patterns.insert(context, vector, vectorizedOps, erasableOps); + patterns.insert(context, vector, vectorizedOps, erasableOps); } } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/VectorizationPatterns.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/VectorizationPatterns.h index 0a61e221..1dc44b61 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/VectorizationPatterns.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/VectorizationPatterns.h @@ -9,7 +9,6 @@ #include "mlir/Transforms/DialectConversion.h" #include "LoSPN/LoSPNDialect.h" #include "LoSPN/LoSPNOps.h" -#include "llvm/Support/Debug.h" namespace mlir { namespace spn { @@ -24,8 +23,8 @@ namespace mlir { low::SPNTask>(typeConverter, context, benefit), requireAllOpsVectorizable{requireAllOpsVectorizable} {} protected: - LogicalResult createFunctionIfVectorizable(low::SPNTask task, - ArrayRef operands, + LogicalResult createFunctionIfVectorizable(low::SPNTask& task, + llvm::ArrayRef const& operands, ConversionPatternRewriter& rewriter, FuncOp* function) const; @@ -64,9 +63,9 @@ namespace mlir { ConversionPatternRewriter& rewriter) const override; }; - static void populateLoSPNCPUVectorizationStructurePatterns(OwningRewritePatternList& patterns, - MLIRContext* context, - TypeConverter& typeConverter) { + static void populateLoSPNtoCPUVectorizationTaskPatterns(OwningRewritePatternList& patterns, + MLIRContext* context, + TypeConverter& typeConverter) { patterns.insert(typeConverter, context, 5); } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp b/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp index 022ed2d4..cc92b563 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp @@ -27,16 +27,29 @@ void mlir::spn::LoSPNtoCPUStructureConversionPass::runOnOperation() { target.addLegalDialect(); target.addIllegalOp(); - target.addIllegalOp(); + target.addIllegalOp(); OwningRewritePatternList patterns; + mlir::spn::populateLoSPNtoCPUStructurePatterns(patterns, &getContext(), typeConverter); + FrozenRewritePatternList frozenPatterns(std::move(patterns)); + + // We split this pass into two conversion pattern applications because the single task vectorization relies on + // the structure being converted beforehand. Otherwise, the SPNBatchReads wouldn't be converted into vector loads + // since they aren't contained in the SPNBody region like the other operations. + if (failed(applyPartialConversion(getOperation(), target, frozenPatterns))) { + signalPassFailure(); + } + + target.addIllegalOp(); + + patterns = OwningRewritePatternList(); if (vectorize) { - // Try to vectorize tasks if vectorization was requested. - mlir::spn::populateLoSPNCPUVectorizationStructurePatterns(patterns, &getContext(), typeConverter); + mlir::spn::populateLoSPNtoCPUVectorizationTaskPatterns(patterns, &getContext(), typeConverter); + } else { + mlir::spn::populateLoSPNtoCPUTaskPatterns(patterns, &getContext(), typeConverter); } - mlir::spn::populateLoSPNtoCPUStructurePatterns(patterns, &getContext(), typeConverter); + frozenPatterns = FrozenRewritePatternList(std::move(patterns)); - FrozenRewritePatternList frozenPatterns(std::move(patterns)); if (failed(applyPartialConversion(getOperation(), target, frozenPatterns))) { signalPassFailure(); } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/StructurePatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/StructurePatterns.cpp index 1e332f6c..2a7d7eca 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/StructurePatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/StructurePatterns.cpp @@ -7,7 +7,6 @@ #include "LoSPNtoCPU/StructurePatterns.h" #include "mlir/Dialect/StandardOps/IR/Ops.h" #include "mlir/Dialect/SCF/SCF.h" -#include "mlir/IR/BuiltinOps.h" mlir::LogicalResult mlir::spn::KernelLowering::matchAndRewrite(mlir::spn::low::SPNKernel op, llvm::ArrayRef operands, @@ -80,6 +79,7 @@ mlir::LogicalResult mlir::spn::SingleTaskLowering::matchAndRewrite(mlir::spn::lo if (op.batchSize() != 1) { return rewriter.notifyMatchFailure(op, "Match only single (batchSize == 1) execution"); } + auto restore = rewriter.saveInsertionPoint(); rewriter.setInsertionPointToStart(op->getParentOfType().getBody()); SmallVector inputTypes; @@ -87,8 +87,7 @@ mlir::LogicalResult mlir::spn::SingleTaskLowering::matchAndRewrite(mlir::spn::lo inputTypes.push_back(operand.getType()); } auto funcType = FunctionType::get(rewriter.getContext(), inputTypes, {}); - auto taskFunc = rewriter.create(op->getLoc(), Twine("task_", std::to_string(taskCount++)).str(), - funcType); + auto taskFunc = rewriter.create(op->getLoc(), Twine("task_", std::to_string(taskCount++)).str(), funcType); auto taskBlock = taskFunc.addEntryBlock(); rewriter.setInsertionPointToStart(taskBlock); diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp index 688b12f3..1b7eeb1d 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp @@ -5,7 +5,6 @@ #include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" -#include "mlir/Dialect/Vector/VectorOps.h" #include "mlir/Dialect/Math/IR/Math.h" #include "mlir/IR/BlockAndValueMapping.h" #include "llvm/Support/FormatVariadic.h" @@ -54,9 +53,11 @@ namespace { } -LogicalResult VectorizeConstant::matchAndRewrite(ConstantOp constantOp, - ArrayRef operands, - ConversionPatternRewriter& rewriter) const { +LogicalResult VectorizeConstant::matchAndRewrite(ConstantOp constantOp, PatternRewriter& rewriter) const { + + if (vectorizedOps.count(vector)) { + return rewriter.notifyMatchFailure(constantOp, "operation's vector has already been created"); + } auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), constantOp.getType()); @@ -64,7 +65,7 @@ LogicalResult VectorizeConstant::matchAndRewrite(ConstantOp constantOp, if (!vector->isUniform()) { auto vectorVal = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); - vectorizedOps[vector] = vectorVal.getDefiningOp(); + vectorizedOps[vector] = vectorVal; return success(); } @@ -82,9 +83,11 @@ LogicalResult VectorizeConstant::matchAndRewrite(ConstantOp constantOp, return success(); } -LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead batchReadOp, - ArrayRef operands, - ConversionPatternRewriter& rewriter) const { +LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead batchReadOp, PatternRewriter& rewriter) const { + + if (vectorizedOps.count(vector)) { + return rewriter.notifyMatchFailure(batchReadOp, "operation's vector has already been created"); + } auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), batchReadOp.getType()); @@ -92,7 +95,7 @@ LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead batchReadOp, if (!vector->isUniform() || !consecutiveLoads(vector->begin(), vector->end())) { auto vectorVal = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); - vectorizedOps[vector] = vectorVal.getDefiningOp(); + vectorizedOps[vector] = vectorVal; } else { auto batchReadLoc = batchReadOp->getLoc(); @@ -107,71 +110,77 @@ LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead batchReadOp, return success(); } -LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd addOp, - ArrayRef operands, - ConversionPatternRewriter& rewriter) const { -/* +LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd addOp, PatternRewriter& rewriter) const { + + if (vectorizedOps.count(vector)) { + return rewriter.notifyMatchFailure(addOp, "operation's vector has already been created"); + } + auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), addOp.getType()); rewriter.setInsertionPoint(firstUser(vector->begin(), vector->end())); if (!vector->isUniform()) { auto vectorVal = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); - vectorizedOps[vector] = vectorVal.getDefiningOp(); + vectorizedOps[vector] = vectorVal; return success(); } SmallVector operands; for (unsigned i = 0; i < addOp.getNumOperands(); ++i) { - auto* operandOp = vectorizedOps[vector->getOperand(i)]; - if (!operandOp) { - return rewriter.notifyMatchFailure(addOp, "operation's operands have not yet been (fully) vectorized"); + if (!vectorizedOps.count(vector->getOperand(i))) { + return rewriter.notifyMatchFailure(addOp, "operation's operands have not yet been vectorized"); } - operands.emplace_back(operandOp->getResult(0)); + auto const& operand = vectorizedOps[vector->getOperand(i)]; + operands.emplace_back(operand); } auto vectorAddOp = rewriter.create(addOp->getLoc(), vectorType, operands); vectorizedOps[vector] = vectorAddOp; erasableOps.insert(vector); -*/ + return success(); } -LogicalResult VectorizeMul::matchAndRewrite(SPNMul mulOp, - ArrayRef operands, - ConversionPatternRewriter& rewriter) const { -/* +LogicalResult VectorizeMul::matchAndRewrite(SPNMul mulOp, PatternRewriter& rewriter) const { + + if (vectorizedOps.count(vector)) { + return rewriter.notifyMatchFailure(mulOp, "operation's vector has already been created"); + } + auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), mulOp.getType()); rewriter.setInsertionPoint(firstUser(vector->begin(), vector->end())); if (!vector->isUniform()) { auto vectorVal = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); - vectorizedOps[vector] = vectorVal.getDefiningOp(); + vectorizedOps[vector] = vectorVal; return success(); } SmallVector operands; for (unsigned i = 0; i < mulOp.getNumOperands(); ++i) { - auto* operandOp = vectorizedOps[vector->getOperand(i)]; - if (!operandOp) { - return rewriter.notifyMatchFailure(mulOp, "operation's operands have not yet been (fully) vectorized"); + if (!vectorizedOps.count(vector->getOperand(i))) { + return rewriter.notifyMatchFailure(mulOp, "operation's operands have not yet been vectorized"); } - operands.emplace_back(operandOp->getResult(0)); + auto const& operand = vectorizedOps[vector->getOperand(i)]; + operands.emplace_back(operand); } auto vectorAddOp = rewriter.create(mulOp->getLoc(), vectorType, operands); vectorizedOps[vector] = vectorAddOp; erasableOps.insert(vector); -*/ + return success(); } -LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, - ArrayRef operands, - ConversionPatternRewriter& rewriter) const { +LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, PatternRewriter& rewriter) const { + + if (vectorizedOps.count(vector)) { + return rewriter.notifyMatchFailure(gaussianOp, "operation's vector has already been created"); + } auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), gaussianOp.getType()); auto const& firstValue = firstOccurrence(vector->begin(), vector->end()); @@ -184,7 +193,7 @@ LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, if (!vector->isUniform()) { auto vectorVal = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); - vectorizedOps[vector] = vectorVal.getDefiningOp(); + vectorizedOps[vector] = vectorVal; return success(); } @@ -220,11 +229,10 @@ LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, auto const& stddevs = denseFloatingPoints(std::begin(stddevAttributes), std::end(stddevAttributes), vectorType); // Grab the input vector. - auto* inputOp = vectorizedOps[vector->getOperand(0)]; - if (!inputOp) { - return rewriter.notifyMatchFailure(gaussianOp, "operation's operands have not yet been (fully) vectorized"); + if (!vectorizedOps.count(vector->getOperand(0))) { + return rewriter.notifyMatchFailure(gaussianOp, "input vector has not yet been vectorized"); } - Value inputVector = inputOp->getResult(0); + Value const& inputVector = vectorizedOps[vector->getOperand(0)]; // Calculate Gaussian distribution using e^(-0.5 * ((x - mean) / stddev)^2)) / (stddev * sqrt(2 * PI)) auto const& gaussianLoc = gaussianOp->getLoc(); @@ -241,13 +249,13 @@ LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, // e^(-0.5 * ((x - mean) / stddev)^2)) auto halfVector = rewriter.create(gaussianLoc, denseFloatingPoints(-0.5, vectorType)); gaussianVector = rewriter.create(gaussianLoc, vectorType, halfVector, gaussianVector); - gaussianVector = rewriter.create(gaussianLoc, gaussianVector); + gaussianVector = rewriter.create(gaussianLoc, vectorType, gaussianVector); // e^(-0.5 * ((x - mean) / stddev)^2)) / (stddev * sqrt(2 * PI)) auto coefficientVector = rewriter.create(gaussianLoc, coefficients); gaussianVector = rewriter.create(gaussianLoc, coefficientVector, gaussianVector); - vectorizedOps[vector] = gaussianVector.getDefiningOp(); + vectorizedOps[vector] = gaussianVector; erasableOps.insert(vector); return success(); diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp index a37b7c50..c0687dd1 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp @@ -11,11 +11,10 @@ #include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h" #include "mlir/Dialect/SCF/SCF.h" #include "mlir/IR/BlockAndValueMapping.h" -#include "mlir/Transforms/GreedyPatternRewriteDriver.h" #include "llvm/Support/FormatVariadic.h" -mlir::LogicalResult mlir::spn::VectorizeTask::createFunctionIfVectorizable(low::SPNTask task, - ArrayRef operands, +mlir::LogicalResult mlir::spn::VectorizeTask::createFunctionIfVectorizable(low::SPNTask& task, + ArrayRef const& operands, ConversionPatternRewriter& rewriter, FuncOp* function) const { static int taskCount = 0; @@ -50,15 +49,7 @@ mlir::LogicalResult mlir::spn::VectorizeTask::createFunctionIfVectorizable(low:: task->emitRemark() << "Attempting to vectorize with vector width " << hwVectorWidth << " for data-type " << computationType; - // Emit a warning if the target vector width does not divide the requested batch size. - // This will cause a part of each batch (batchSize % vectorWidth elements) to be processed - // by the scalar epilog loop instead of the vectorized loop. - if ((task.batchSize() % hwVectorWidth) != 0) { - task.emitWarning() << "The target vector width " << hwVectorWidth << " does not divide the requested batch size " - << task.batchSize() << "; This can result in degraded performance. " - << "Choose the batch size as a multiple of the vector width " << hwVectorWidth; - } - + auto const& insertionPoint = rewriter.saveInsertionPoint(); rewriter.setInsertionPointToStart(task->getParentOfType().getBody()); SmallVector inputTypes; for (auto operand : operands) { @@ -66,6 +57,7 @@ mlir::LogicalResult mlir::spn::VectorizeTask::createFunctionIfVectorizable(low:: } auto funcType = FunctionType::get(rewriter.getContext(), inputTypes, {}); *function = rewriter.create(task->getLoc(), Twine("vec_task_", std::to_string(taskCount++)).str(), funcType); + rewriter.restoreInsertionPoint(insertionPoint); return success(); } @@ -78,23 +70,32 @@ mlir::LogicalResult mlir::spn::VectorizeSingleTask::matchAndRewrite(mlir::spn::l "Specialized for single batch vectorization, does not match for batchSize > 1"); } - auto restore = rewriter.saveInsertionPoint(); + auto const& callPoint = rewriter.saveInsertionPoint(); FuncOp taskFunc; if (failed(createFunctionIfVectorizable(task, operands, rewriter, &taskFunc))) { return failure(); } - assert(operands.back().getType().isa()); - auto computationType = operands.back().getType().dyn_cast().getElementType(); - auto hwVectorWidth = TargetInformation::nativeCPUTarget().getHWVectorEntries(computationType); - auto taskBlock = taskFunc.addEntryBlock(); rewriter.setInsertionPointToStart(taskBlock); - rewriter.mergeBlocks(&task.body().front(), taskBlock, /*task.body().front().getArguments()*/{}); + + // Collect the values replacing the block values of old block inside the task. + // The first argument is the batch index, in this case (for a single execution), + // we can simply set it to constant zero. + // The other arguments are the arguments of the entry block of this function. + SmallVector blockReplacementArgs; + blockReplacementArgs.push_back(rewriter.create(task.getLoc(), rewriter.getIndexAttr(0))); + for (auto bArg : taskBlock->getArguments()) { + blockReplacementArgs.push_back(bArg); + } + // Inline the content of the Task into the function. + rewriter.mergeBlocks(&task.body().front(), taskBlock, blockReplacementArgs); // Apply SLP vectorization. task->emitRemark() << "Computing new seed for SLP vectorization..."; + auto computationType = operands.back().getType().dyn_cast().getElementType(); + auto hwVectorWidth = TargetInformation::nativeCPUTarget().getHWVectorEntries(computationType); low::slp::SeedAnalysis seedAnalysis{taskFunc}; auto const& seed = seedAnalysis.getSeed(hwVectorWidth, low::slp::SearchMode::UseBeforeDef); assert(!seed.empty() && "couldn't find a seed!"); @@ -102,23 +103,32 @@ mlir::LogicalResult mlir::spn::VectorizeSingleTask::matchAndRewrite(mlir::spn::l seed.front().getDefiningOp()->getBlock()->dump(); low::slp::SLPGraphBuilder builder{3}; auto graph = builder.build(seed); + low::slp::dumpSLPGraph(*graph); task->emitRemark() << "Transforming SLP graph back into vectorized ops..."; // Maps individual SLP node vectors to finished vector operations. - DenseMap vectorizedOps; + DenseMap vectorizedOps; // Marks operations as potentially erasable (potentially because they might have escaping uses). SmallPtrSet erasableOps; // Stores escaping uses for vector extractions that might be necessary later on. auto const& escapingLanes = low::slp::SLPNode::escapingLanesMap(graph.get()); // Prevent extracting/removing values more than once (happens in splat mode, if they appear in multiple vectors, ...). - SmallPtrSet finishedValues; + SmallPtrSet finishedValues; // The current vector being transformed. low::slp::NodeVector* vector = nullptr; OwningRewritePatternList patterns; - populateSLPVectorizationPatterns(patterns, *typeConverter, rewriter.getContext(), vector, vectorizedOps, erasableOps); + populateSLPVectorizationPatterns(patterns, rewriter.getContext(), vector, vectorizedOps, erasableOps); FrozenRewritePatternList frozenPatterns(std::move(patterns)); + // Use a custom pattern driver that does *not* perform folding automatically (which the other rewrite drivers + // such as applyOpPatternsAndFold() would do). Folding would mess up identified SLP-vectorizable constants, which + // aren't necessarily located at the front of the function's body yet. The drivers delete those that aren't at the + // front (including those we identified as SLP-vectorizable!) and create new ones there, resulting in our constant + // SLP-vectorization patterns not being applied to them and messing up operand handling further down the SLP graph. + PatternApplicator applicator(frozenPatterns); + applicator.applyDefaultCostModel(); + // Traverse the SLP graph in postorder and apply the vectorization patterns. for (auto* node : low::slp::SLPNode::postOrder(graph.get())) { @@ -136,10 +146,10 @@ mlir::LogicalResult mlir::spn::VectorizeSingleTask::matchAndRewrite(mlir::spn::l rewriter.setInsertionPoint(low::slp::firstUser(vector->begin(), vector->end())); auto vectorOp = low::slp::broadcastFirstInsertRest(vector->begin(), vector->end(), type, rewriter); rewriter.restoreInsertionPoint(insertionPoint); - vectorizedOps[vector] = vectorOp.getDefiningOp(); + vectorizedOps[vector] = vectorOp; } else { auto* vectorOp = vector->begin()->getDefiningOp(); - if (failed(applyOpPatternsAndFold(vectorOp, frozenPatterns))) { + if (failed(applicator.matchAndRewrite(vectorOp, rewriter))) { vectorOp->emitOpError("SLP pattern application failed (did you forget to specify the pattern?)"); } } @@ -156,13 +166,13 @@ mlir::LogicalResult mlir::spn::VectorizeSingleTask::matchAndRewrite(mlir::spn::l } auto const& lanesToExtract = escapingLanes.lookup(vector); if (std::find(std::begin(lanesToExtract), std::end(lanesToExtract), lane) != std::end(lanesToExtract)) { - auto const& source = vectorizedOps[vector]->getResult(0); + auto const& source = vectorizedOps[vector]; rewriter.setInsertionPoint(low::slp::firstOccurrence(std::begin(vectorValue.getUsers()), std::end(vectorValue.getUsers()))); auto extractOp = rewriter.create(vectorValue.getLoc(), source, lane); vectorValue.replaceAllUsesWith(extractOp.result()); } else { - vectorValue.dropAllUses(); + vectorValue.replaceAllUsesWith(vectorizedOps[vector]); } finishedValues.insert(vectorValue); rewriter.eraseOp(vectorValue.getDefiningOp()); @@ -171,8 +181,8 @@ mlir::LogicalResult mlir::spn::VectorizeSingleTask::matchAndRewrite(mlir::spn::l } } - rewriter.restoreInsertionPoint(restore); - rewriter.replaceOpWithNewOp(task, taskFunc, operands); + rewriter.restoreInsertionPoint(callPoint); + rewriter.replaceOpWithNewOp(task, taskFunc, operands).dump(); return success(); } @@ -198,6 +208,15 @@ mlir::LogicalResult mlir::spn::VectorizeBatchTask::matchAndRewrite(mlir::spn::lo auto computationType = operands.back().getType().dyn_cast().getElementType(); auto hwVectorWidth = TargetInformation::nativeCPUTarget().getHWVectorEntries(computationType); + // Emit a warning if the target vector width does not divide the requested batch size. + // This will cause a part of each batch (batchSize % vectorWidth elements) to be processed + // by the scalar epilog loop instead of the vectorized loop. + if ((op.batchSize() % hwVectorWidth) != 0) { + op.emitWarning() << "The target vector width " << hwVectorWidth << " does not divide the requested batch size " + << op.batchSize() << "; This can result in degraded performance. " + << "Choose the batch size as a multiple of the vector width " << hwVectorWidth; + } + auto taskBlock = taskFunc.addEntryBlock(); rewriter.setInsertionPointToStart(taskBlock); auto numSamples = rewriter.create(op.getLoc(), taskBlock->getArgument(0), 0); From 1fa7715701898695f1d60ff248a85dee72f1738f Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sat, 24 Apr 2021 17:16:28 +0200 Subject: [PATCH 109/385] Removed some namespace prefixes. --- .../LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp b/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp index cc92b563..05fded42 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp @@ -18,19 +18,19 @@ void mlir::spn::LoSPNtoCPUStructureConversionPass::runOnOperation() { ConversionTarget target(getContext()); target.addLegalDialect(); - target.addLegalDialect(); - target.addLegalDialect(); + target.addLegalDialect(); + target.addLegalDialect(); target.addLegalOp(); target.addLegalOp(); LoSPNtoCPUTypeConverter typeConverter; - target.addLegalDialect(); - target.addIllegalOp(); - target.addIllegalOp(); + target.addLegalDialect(); + target.addIllegalOp(); + target.addIllegalOp(); OwningRewritePatternList patterns; - mlir::spn::populateLoSPNtoCPUStructurePatterns(patterns, &getContext(), typeConverter); + spn::populateLoSPNtoCPUStructurePatterns(patterns, &getContext(), typeConverter); FrozenRewritePatternList frozenPatterns(std::move(patterns)); // We split this pass into two conversion pattern applications because the single task vectorization relies on @@ -40,13 +40,13 @@ void mlir::spn::LoSPNtoCPUStructureConversionPass::runOnOperation() { signalPassFailure(); } - target.addIllegalOp(); + target.addIllegalOp(); patterns = OwningRewritePatternList(); if (vectorize) { - mlir::spn::populateLoSPNtoCPUVectorizationTaskPatterns(patterns, &getContext(), typeConverter); + spn::populateLoSPNtoCPUVectorizationTaskPatterns(patterns, &getContext(), typeConverter); } else { - mlir::spn::populateLoSPNtoCPUTaskPatterns(patterns, &getContext(), typeConverter); + spn::populateLoSPNtoCPUTaskPatterns(patterns, &getContext(), typeConverter); } frozenPatterns = FrozenRewritePatternList(std::move(patterns)); From b1215d15e05e3102943c257aeb564960f0947577 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sat, 24 Apr 2021 17:17:06 +0200 Subject: [PATCH 110/385] Fix: SPNTask operations not being removed because of illegal gaussian exp ops. --- mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp b/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp index 05fded42..b7600d70 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp @@ -41,6 +41,7 @@ void mlir::spn::LoSPNtoCPUStructureConversionPass::runOnOperation() { } target.addIllegalOp(); + target.addLegalOp(); patterns = OwningRewritePatternList(); if (vectorize) { From 8d3a71eff71392c9381f1e974f7926533de45301 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 25 Apr 2021 02:20:24 +0200 Subject: [PATCH 111/385] Better vector/array parameters. --- .../Vectorization/SLP/SLPGraphBuilder.h | 2 +- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.h | 22 ++++++++--------- .../LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h | 2 +- .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.h | 2 +- .../Vectorization/SLP/SLPGraphBuilder.cpp | 8 +++---- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp | 24 +++++++++---------- .../Vectorization/SLP/SLPSeeding.cpp | 8 +++---- .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp | 2 +- 8 files changed, 33 insertions(+), 37 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h index f5461fbf..94b02f32 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h @@ -21,7 +21,7 @@ namespace mlir { explicit SLPGraphBuilder(size_t maxLookAhead); - std::unique_ptr build(vector_t const& seed) const; + std::unique_ptr build(ArrayRef const& seed) const; private: diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h index 2f8a0919..09371720 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h @@ -13,16 +13,14 @@ namespace mlir { namespace low { namespace slp { - typedef SmallVector vector_t; - class NodeVector { friend class SLPNode; public: - explicit NodeVector(vector_t const& values); - explicit NodeVector(SmallVector const& operations); + explicit NodeVector(ArrayRef const& values); + explicit NodeVector(ArrayRef const& operations); bool isUniform() const; bool contains(Value const& value) const; @@ -34,14 +32,14 @@ namespace mlir { size_t numOperands() const; NodeVector* getOperand(size_t index) const; - vector_t::const_iterator begin() const; - vector_t::const_iterator end() const; + SmallVectorImpl::const_iterator begin() const; + SmallVectorImpl::const_iterator end() const; Value const& getElement(size_t lane) const; Value const& operator[](size_t lane) const; private: - vector_t values; + SmallVector values; SmallVector> operands; }; @@ -49,8 +47,8 @@ namespace mlir { public: - explicit SLPNode(vector_t const& values); - explicit SLPNode(SmallVector const& operations); + explicit SLPNode(ArrayRef const& values); + explicit SLPNode(ArrayRef const& operations); Value getValue(size_t lane, size_t index) const; void setValue(size_t lane, size_t index, Value const& newValue); @@ -63,11 +61,11 @@ namespace mlir { size_t numLanes() const; size_t numVectors() const; - NodeVector* addVector(vector_t const& values, NodeVector* definingVector); - NodeVector* addVector(SmallVector const& operations); + NodeVector* addVector(ArrayRef const& values, NodeVector* definingVector); + NodeVector* addVector(ArrayRef const& operations); NodeVector* getVector(size_t index) const; - SLPNode* addOperand(vector_t const& values, NodeVector* definingVector); + SLPNode* addOperand(ArrayRef const& values, NodeVector* definingVector); SLPNode* getOperand(size_t index) const; std::vector getOperands() const; size_t numOperands() const; diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h index d9ce4783..01d88ea0 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h @@ -30,7 +30,7 @@ namespace mlir { explicit SeedAnalysis(Operation* rootOp); - vector_t getSeed(unsigned width, SearchMode const& mode) const; + ArrayRef getSeed(unsigned width, SearchMode const& mode) const; private: Operation* rootOp; diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h index 5c926649..af766262 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h @@ -119,7 +119,7 @@ namespace mlir { return vectorOp; } - void dumpOpTree(vector_t const& values); + void dumpOpTree(ArrayRef const& values); void dumpSLPGraph(SLPNode const& root); void dumpSLPNode(SLPNode const& node); void dumpSLPNodeVector(NodeVector const& nodeVector); diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp index 113eda9a..dacae3bf 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp @@ -13,7 +13,7 @@ using namespace mlir::spn::low::slp; SLPGraphBuilder::SLPGraphBuilder(size_t maxLookAhead) : maxLookAhead{maxLookAhead} {} -std::unique_ptr SLPGraphBuilder::build(vector_t const& seed) const { +std::unique_ptr SLPGraphBuilder::build(ArrayRef const& seed) const { auto root = std::make_unique(SLPNode{seed}); buildGraph(root->getVector(0), root.get()); return root; @@ -90,7 +90,7 @@ void SLPGraphBuilder::buildGraph(NodeVector* vector, SLPNode* currentNode) const if (std::all_of(std::begin(allOperands), std::end(allOperands), [&](SmallVector const& operands) { return operands[i].getDefiningOp()->getName() == currentOpCode && !escapesMultinode(operands[i], currentNode); })) { - vector_t vectorValues; + SmallVector vectorValues; for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { vectorValues.emplace_back(allOperands[lane][i]); } @@ -98,7 +98,7 @@ void SLPGraphBuilder::buildGraph(NodeVector* vector, SLPNode* currentNode) const buildGraph(newVector, currentNode); } else { // TODO: here might be a good place to implement variable vector width - vector_t operandValues; + SmallVector operandValues; for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { operandValues.emplace_back(allOperands[lane][i]); } @@ -116,7 +116,7 @@ void SLPGraphBuilder::buildGraph(NodeVector* vector, SLPNode* currentNode) const // 2. Non-Commutative else { for (size_t i = 0; i < arity; ++i) { - vector_t operandValues; + SmallVector operandValues; for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { auto operand = currentNode->getValue(lane, 0).getDefiningOp()->getOperand(i); operandValues.emplace_back(operand); diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp index f4d27e97..3739f034 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp @@ -5,8 +5,6 @@ #include "LoSPNtoCPU/Vectorization/SLP/SLPNode.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" -#include "LoSPN/LoSPNOps.h" -#include "mlir/Dialect/StandardOps/IR/Ops.h" using namespace mlir; using namespace mlir::spn; @@ -15,7 +13,7 @@ using namespace mlir::spn::low::slp; // === NodeVector === // -NodeVector::NodeVector(vector_t const& values) { +NodeVector::NodeVector(ArrayRef const& values) { assert(!values.empty()); for (auto const& value : values) { assert(value.isa() || value.getDefiningOp()->hasTrait()); @@ -23,7 +21,7 @@ NodeVector::NodeVector(vector_t const& values) { } } -NodeVector::NodeVector(SmallVector const& operations) { +NodeVector::NodeVector(ArrayRef const& operations) { assert(!operations.empty()); for (auto* op : operations) { assert(op->hasTrait()); @@ -69,11 +67,11 @@ NodeVector* NodeVector::getOperand(size_t index) const { return operands[index].get(); } -vector_t::const_iterator NodeVector::begin() const { +SmallVectorImpl::const_iterator NodeVector::begin() const { return values.begin(); } -vector_t::const_iterator NodeVector::end() const { +SmallVectorImpl::const_iterator NodeVector::end() const { return values.end(); } @@ -88,12 +86,12 @@ Value const& NodeVector::operator[](size_t lane) const { // === SLPNode === // -SLPNode::SLPNode(SmallVector const& operations) { - addVector(operations); +SLPNode::SLPNode(ArrayRef const& values) { + addVector(values, nullptr); } -SLPNode::SLPNode(vector_t const& values) { - addVector(values, nullptr); +SLPNode::SLPNode(ArrayRef const& operations) { + addVector(operations); } Value SLPNode::getValue(size_t lane, size_t index) const { @@ -130,7 +128,7 @@ size_t SLPNode::numVectors() const { return vectors.size(); } -NodeVector* SLPNode::addVector(vector_t const& values, NodeVector* definingVector) { +NodeVector* SLPNode::addVector(ArrayRef const& values, NodeVector* definingVector) { auto* newVector = vectors.emplace_back(std::make_shared(values)).get(); if (definingVector) { definingVector->operands.emplace_back(newVector); @@ -138,7 +136,7 @@ NodeVector* SLPNode::addVector(vector_t const& values, NodeVector* definingVecto return newVector; } -NodeVector* SLPNode::addVector(SmallVector const& operations) { +NodeVector* SLPNode::addVector(ArrayRef const& operations) { return vectors.emplace_back(std::make_shared(operations)).get(); } @@ -147,7 +145,7 @@ NodeVector* SLPNode::getVector(size_t index) const { return vectors[index].get(); } -SLPNode* SLPNode::addOperand(vector_t const& values, NodeVector* definingVector) { +SLPNode* SLPNode::addOperand(ArrayRef const& values, NodeVector* definingVector) { auto* operandNode = operandNodes.emplace_back(std::make_unique(values)).get(); if (definingVector) { definingVector->operands.emplace_back(operandNode->vectors.front()); diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp index 7f1ab503..a2486431 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp @@ -31,9 +31,9 @@ namespace { } } -vector_t SeedAnalysis::getSeed(unsigned width, SearchMode const& mode) const { +llvm::ArrayRef SeedAnalysis::getSeed(unsigned width, SearchMode const& mode) const { - llvm::StringMap> seedsByOpName; + llvm::StringMap>> seedsByOpName; auto const& opDepths = getOpDepths(rootOp); rootOp->walk([&](Operation* op) { @@ -58,13 +58,13 @@ vector_t SeedAnalysis::getSeed(unsigned width, SearchMode const& mode) const { } } if (needsNewSeed) { - vector_t seed{value}; + SmallVector seed{value}; seedsByOpName[op->getName().getStringRef()].emplace_back(seed); } return WalkResult::advance(); }); - SmallVector seeds; + SmallVector> seeds; // Flatten the map that maps opcode to seeds. for (auto const& entry : seedsByOpName) { for (auto const& seed : entry.second) { diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp index 95f6c477..a0c831bb 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp @@ -99,7 +99,7 @@ void mlir::spn::low::slp::dumpSLPNodeVector(const mlir::spn::low::slp::NodeVecto llvm::dbgs() << "\n"; } -void mlir::spn::low::slp::dumpOpTree(const vector_t& values) { +void mlir::spn::low::slp::dumpOpTree(ArrayRef const& values) { DenseMap nodes; SmallVector> edges; From 4a6829c84570184e3952c1065655eec31266bb85 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 25 Apr 2021 02:20:39 +0200 Subject: [PATCH 112/385] Better SLP pattern failure handling. --- .../SLP/SLPVectorizationPatterns.cpp | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp index 1b7eeb1d..b0e89c97 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp @@ -56,7 +56,7 @@ namespace { LogicalResult VectorizeConstant::matchAndRewrite(ConstantOp constantOp, PatternRewriter& rewriter) const { if (vectorizedOps.count(vector)) { - return rewriter.notifyMatchFailure(constantOp, "operation's vector has already been created"); + return constantOp->emitError("operation's vector has already been created"); } auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), constantOp.getType()); @@ -86,7 +86,7 @@ LogicalResult VectorizeConstant::matchAndRewrite(ConstantOp constantOp, PatternR LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead batchReadOp, PatternRewriter& rewriter) const { if (vectorizedOps.count(vector)) { - return rewriter.notifyMatchFailure(batchReadOp, "operation's vector has already been created"); + return batchReadOp->emitError("operation's vector has already been created"); } auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), batchReadOp.getType()); @@ -113,7 +113,7 @@ LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead batchReadOp, Patt LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd addOp, PatternRewriter& rewriter) const { if (vectorizedOps.count(vector)) { - return rewriter.notifyMatchFailure(addOp, "operation's vector has already been created"); + return addOp->emitError("operation's vector has already been created"); } auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), addOp.getType()); @@ -129,7 +129,7 @@ LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd addOp, PatternRewriter& rewri SmallVector operands; for (unsigned i = 0; i < addOp.getNumOperands(); ++i) { if (!vectorizedOps.count(vector->getOperand(i))) { - return rewriter.notifyMatchFailure(addOp, "operation's operands have not yet been vectorized"); + return addOp->emitError("operand #" + std::to_string(i) + " has not yet been vectorized"); } auto const& operand = vectorizedOps[vector->getOperand(i)]; operands.emplace_back(operand); @@ -146,7 +146,7 @@ LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd addOp, PatternRewriter& rewri LogicalResult VectorizeMul::matchAndRewrite(SPNMul mulOp, PatternRewriter& rewriter) const { if (vectorizedOps.count(vector)) { - return rewriter.notifyMatchFailure(mulOp, "operation's vector has already been created"); + return mulOp->emitError("operation's vector has already been created"); } auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), mulOp.getType()); @@ -162,7 +162,7 @@ LogicalResult VectorizeMul::matchAndRewrite(SPNMul mulOp, PatternRewriter& rewri SmallVector operands; for (unsigned i = 0; i < mulOp.getNumOperands(); ++i) { if (!vectorizedOps.count(vector->getOperand(i))) { - return rewriter.notifyMatchFailure(mulOp, "operation's operands have not yet been vectorized"); + return mulOp->emitError("operand #" + std::to_string(i) + " has not yet been vectorized"); } auto const& operand = vectorizedOps[vector->getOperand(i)]; operands.emplace_back(operand); @@ -179,7 +179,7 @@ LogicalResult VectorizeMul::matchAndRewrite(SPNMul mulOp, PatternRewriter& rewri LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, PatternRewriter& rewriter) const { if (vectorizedOps.count(vector)) { - return rewriter.notifyMatchFailure(gaussianOp, "operation's vector has already been created"); + return gaussianOp->emitError("operation's vector has already been created"); } auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), gaussianOp.getType()); @@ -230,7 +230,7 @@ LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, Pat // Grab the input vector. if (!vectorizedOps.count(vector->getOperand(0))) { - return rewriter.notifyMatchFailure(gaussianOp, "input vector has not yet been vectorized"); + return gaussianOp->emitError("input vector has not yet been vectorized"); } Value const& inputVector = vectorizedOps[vector->getOperand(0)]; From f0242ac05b3ff306116762aeab2daa78d8b641fb Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 25 Apr 2021 02:58:44 +0200 Subject: [PATCH 113/385] Fix: multinode dumping printing too many newlines. --- mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp index a0c831bb..2a9c861d 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp @@ -63,8 +63,8 @@ bool mlir::spn::low::slp::consecutiveLoads(Value const& lhs, Value const& rhs) { void mlir::spn::low::slp::dumpSLPNode(mlir::spn::low::slp::SLPNode const& node) { for (size_t i = node.numVectors(); i-- > 0;) { dumpSLPNodeVector(*node.getVector(i)); - llvm::dbgs() << "\n"; } + llvm::dbgs() << "\n"; } // Helper functions in an anonymous namespace. From 4e92484d9868a91b91fd53f48bbebcde3d4d179a Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 25 Apr 2021 02:59:44 +0200 Subject: [PATCH 114/385] Fix: getLookAheadScore failing when block arguments are involved. --- .../Vectorization/SLP/SLPGraphBuilder.cpp | 22 ++++++------------- 1 file changed, 7 insertions(+), 15 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp index dacae3bf..0cc44980 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp @@ -254,30 +254,22 @@ std::pair SLPGraphBuilder::getBest(Mode const& mod } int SLPGraphBuilder::getLookAheadScore(Value const& last, Value const& candidate, unsigned maxLevel) const { - if (maxLevel == 0) { + if (maxLevel == 0 || last.isa() || candidate.isa()) { if (last == candidate) { return 1; } - if (last.isa() || candidate.isa()) { - return 0; - } - if (last.getDefiningOp()->getName() != candidate.getDefiningOp()->getName()) { - return 0; + if (last.getDefiningOp() && consecutiveLoads(last, candidate)) { + return 1; } - if (last.getDefiningOp()) { - return consecutiveLoads(last, candidate) ? 1 : 0; + if (!last.isa() && !candidate.isa()) { + return last.getDefiningOp()->getName() == candidate.getDefiningOp()->getName(); } - return 1; + return 0; } auto scoreSum = 0; for (auto& lastOperand : getOperands(last)) { for (auto& candidateOperand : getOperands(candidate)) { - // Can be null if the operand is a block argument. - if (!lastOperand || !candidateOperand) { - scoreSum += getLookAheadScore(last, candidate, 0); - } else { - scoreSum += getLookAheadScore(lastOperand, candidateOperand, maxLevel - 1); - } + scoreSum += getLookAheadScore(lastOperand, candidateOperand, maxLevel - 1); } } return scoreSum; From 9d4db4a18b712e3797729ba885c6e62e6f397b50 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 25 Apr 2021 04:19:05 +0200 Subject: [PATCH 115/385] SLP graph output now includes operand numbers. --- mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp index 2a9c861d..d80fdae9 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp @@ -208,8 +208,9 @@ void mlir::spn::low::slp::dumpSLPGraph(SLPNode const& root) { llvm::dbgs() << "\t\n"; llvm::dbgs() << ">];\n"; - for (auto const& operand : node->getOperands()) { - llvm::dbgs() << "node_" << node << "->" << "node_" << operand << ";\n"; + for (size_t i = 0; i < node->numOperands(); ++i) { + auto const& operand = node->getOperand(i); + llvm::dbgs() << "node_" << node << "->" << "node_" << operand << "[label=\"" << std::to_string(i) << "\"];\n"; worklist.emplace(operand); } } From 35567498b28eb0b38104117a75714513d382e693 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 25 Apr 2021 04:20:05 +0200 Subject: [PATCH 116/385] Fix: escaping lanes computation crashing, --- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp | 29 ++++++++----------- 1 file changed, 12 insertions(+), 17 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp index 3739f034..a1dc6a7c 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp @@ -185,38 +185,33 @@ SmallVector SLPNode::postOrder(SLPNode* root) { /// Traverse the entire graph starting at root and gather lanes of each node vector that has escaping uses. DenseMap> SLPNode::escapingLanesMap(SLPNode* root) { - DenseMap> outsideUses; + DenseMap outsideUses; for (auto* node : postOrder(root)) { - for (size_t i = 0; i < node->numVectors(); ++i) { + for (size_t i = node->numVectors(); i-- > 0;) { auto* vector = node->getVector(i); for (size_t lane = 0; lane < vector->numLanes(); ++lane) { auto const& element = vector->getElement(lane); // Skip duplicate (splat) values. - if (outsideUses[vector].count(element)) { + if (outsideUses.count(element)) { continue; } - outsideUses[vector][element] = std::distance(std::begin(element.getUses()), std::end(element.getUses())); + outsideUses[element] = std::distance(std::begin(element.getUses()), std::end(element.getUses())); for (size_t j = 0; j < vector->numOperands(); ++j) { auto* operand = vector->getOperand(j); - assert(outsideUses[operand][operand->getElement(lane)] > 0); - outsideUses[operand][operand->getElement(lane)]--; + assert(outsideUses[operand->getElement(lane)] > 0); + outsideUses[operand->getElement(lane)]--; } } } } DenseMap> escapingLanes; - for (auto const& entry : outsideUses) { - auto* vector = entry.first; - for (auto const& useAndCount : entry.second) { - auto const& value = useAndCount.first; - auto const& count = useAndCount.second; - if (count > 0) { - for (size_t lane = 0; lane < vector->numLanes(); ++lane) { - if (vector->getElement(lane) == value) { - escapingLanes[vector].emplace_back(lane); - break; - } + for (auto* node : postOrder(root)) { + for (size_t i = 0; i < node->numVectors(); ++i) { + auto* vector = node->getVector(i); + for (size_t lane = 0; lane < vector->numLanes(); ++lane) { + if (outsideUses[vector->getElement(lane)] > 0) { + escapingLanes[vector].emplace_back(lane); } } } From 90bdf4f036f5699863b8aae0b01981684ffb2c2b Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 25 Apr 2021 04:49:39 +0200 Subject: [PATCH 117/385] Removed some unused code. --- .../Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h index 09371720..0b2228ac 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h @@ -73,16 +73,6 @@ namespace mlir { static SmallVector postOrder(SLPNode* root); static DenseMap> escapingLanesMap(SLPNode* root); -/* - bool operator==(SLPNode const& other) { - return std::tie(vectors) == std::tie(other.vectors); - } - - bool operator!=(SLPNode const& other) { - return !(*this == other); - } -*/ - private: SmallVector> vectors; SmallVector> operandNodes; From 5f0b4be503f97b3250560243f190498d5e84a7ca Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 25 Apr 2021 04:50:19 +0200 Subject: [PATCH 118/385] Fix: double free error due to SLPNodes not copying shared pointers. --- .../Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp index a1dc6a7c..ae1b4cbc 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp @@ -129,11 +129,11 @@ size_t SLPNode::numVectors() const { } NodeVector* SLPNode::addVector(ArrayRef const& values, NodeVector* definingVector) { - auto* newVector = vectors.emplace_back(std::make_shared(values)).get(); + auto newVector = vectors.emplace_back(std::make_shared(values)); if (definingVector) { definingVector->operands.emplace_back(newVector); } - return newVector; + return newVector.get(); } NodeVector* SLPNode::addVector(ArrayRef const& operations) { @@ -146,11 +146,11 @@ NodeVector* SLPNode::getVector(size_t index) const { } SLPNode* SLPNode::addOperand(ArrayRef const& values, NodeVector* definingVector) { - auto* operandNode = operandNodes.emplace_back(std::make_unique(values)).get(); + auto const& operandNode = operandNodes.emplace_back(std::make_unique(values)); if (definingVector) { definingVector->operands.emplace_back(operandNode->vectors.front()); } - return operandNode; + return operandNode.get(); } SLPNode* SLPNode::getOperand(size_t index) const { From 4f2e86ea9b5a99e116a6bc490e8d9b7ee6578c66 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 25 Apr 2021 14:35:54 +0200 Subject: [PATCH 119/385] Use sets instead of vectors. --- .../Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h | 3 ++- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp | 11 ++++++++--- .../Vectorization/VectorizeStructurePatterns.cpp | 11 +++++------ 3 files changed, 15 insertions(+), 10 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h index 0b2228ac..ab91b6c3 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h @@ -7,6 +7,7 @@ #define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPNODE_H #include "mlir/IR/Operation.h" +#include "llvm/ADT/SmallPtrSet.h" namespace mlir { namespace spn { @@ -71,7 +72,7 @@ namespace mlir { size_t numOperands() const; static SmallVector postOrder(SLPNode* root); - static DenseMap> escapingLanesMap(SLPNode* root); + static DenseMap>> escapingLanesMap(SLPNode* root); private: SmallVector> vectors; diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp index ae1b4cbc..4381b254 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp @@ -184,7 +184,7 @@ SmallVector SLPNode::postOrder(SLPNode* root) { } /// Traverse the entire graph starting at root and gather lanes of each node vector that has escaping uses. -DenseMap> SLPNode::escapingLanesMap(SLPNode* root) { +DenseMap>> SLPNode::escapingLanesMap(SLPNode* root) { DenseMap outsideUses; for (auto* node : postOrder(root)) { for (size_t i = node->numVectors(); i-- > 0;) { @@ -205,13 +205,18 @@ DenseMap> SLPNode::escapingLanesMap(SLPNode* } } - DenseMap> escapingLanes; + DenseMap>> escapingLanes; for (auto* node : postOrder(root)) { for (size_t i = 0; i < node->numVectors(); ++i) { auto* vector = node->getVector(i); for (size_t lane = 0; lane < vector->numLanes(); ++lane) { if (outsideUses[vector->getElement(lane)] > 0) { - escapingLanes[vector].emplace_back(lane); + if (escapingLanes.count(vector)) { + escapingLanes[vector]->insert(lane); + } else { + SmallPtrSet lanes{lane}; + escapingLanes[vector] = std::make_shared>(lanes); + } } } } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp index c0687dd1..dfe9cbd7 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp @@ -99,11 +99,11 @@ mlir::LogicalResult mlir::spn::VectorizeSingleTask::matchAndRewrite(mlir::spn::l low::slp::SeedAnalysis seedAnalysis{taskFunc}; auto const& seed = seedAnalysis.getSeed(hwVectorWidth, low::slp::SearchMode::UseBeforeDef); assert(!seed.empty() && "couldn't find a seed!"); - low::slp::dumpOpTree(seed); - seed.front().getDefiningOp()->getBlock()->dump(); + // low::slp::dumpOpTree(seed); + // seed.front().getDefiningOp()->getBlock()->dump(); low::slp::SLPGraphBuilder builder{3}; auto graph = builder.build(seed); - low::slp::dumpSLPGraph(*graph); + // low::slp::dumpSLPGraph(*graph); task->emitRemark() << "Transforming SLP graph back into vectorized ops..."; // Maps individual SLP node vectors to finished vector operations. @@ -164,8 +164,7 @@ mlir::LogicalResult mlir::spn::VectorizeSingleTask::matchAndRewrite(mlir::spn::l if (finishedValues.contains(vectorValue)) { continue; } - auto const& lanesToExtract = escapingLanes.lookup(vector); - if (std::find(std::begin(lanesToExtract), std::end(lanesToExtract), lane) != std::end(lanesToExtract)) { + if (escapingLanes.count(vector) && escapingLanes.lookup(vector)->contains(lane)) { auto const& source = vectorizedOps[vector]; rewriter.setInsertionPoint(low::slp::firstOccurrence(std::begin(vectorValue.getUsers()), std::end(vectorValue.getUsers()))); @@ -182,7 +181,7 @@ mlir::LogicalResult mlir::spn::VectorizeSingleTask::matchAndRewrite(mlir::spn::l } rewriter.restoreInsertionPoint(callPoint); - rewriter.replaceOpWithNewOp(task, taskFunc, operands).dump(); + rewriter.replaceOpWithNewOp(task, taskFunc, operands); return success(); } From 98336b624f75af6a259c5db0769e69b79b45162b Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Mon, 26 Apr 2021 01:43:09 +0200 Subject: [PATCH 120/385] Introduced ConversionState class to keep track of vector information. --- .../Vectorization/SLP/SLPConversion.h | 59 ++++++++ .../LoSPNtoCPU/Vectorization/SLP/SLPNode.h | 6 +- .../LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h | 1 - .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.h | 39 ------ .../SLP/SLPVectorizationPatterns.h | 26 ++-- mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt | 1 + .../Vectorization/SLP/SLPConversion.cpp | 116 ++++++++++++++++ .../LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp | 71 ++-------- .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp | 31 ++--- .../SLP/SLPVectorizationPatterns.cpp | 128 ++++++++---------- .../VectorizeStructurePatterns.cpp | 125 +++++++++-------- 11 files changed, 337 insertions(+), 266 deletions(-) create mode 100644 mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h create mode 100644 mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h new file mode 100644 index 00000000..bed62e77 --- /dev/null +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h @@ -0,0 +1,59 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPCONVERSION_H +#define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPCONVERSION_H + +#include "SLPNode.h" + +namespace mlir { + namespace spn { + namespace low { + namespace slp { + + enum CreationMode { + Constant, + Splat, + BroadcastInsert, + ConsecutiveLoad, + Default + }; + + // Storage for vector information (in anonymous namespace to hide it). + namespace { + struct NodeVectorData { + /// The operation that was created for this node vector. + Optional operation; + /// The way it was created. + Optional mode; + /// The first (i.e. smaller Loc) escaping use for each lane. + DenseMap firstEscapingUses; + }; + } + + class ConversionState { + + public: + + explicit ConversionState(SLPNode* root); + + Value getInsertionPoint(NodeVector* vector) const; + void update(NodeVector* vector, Value const& operation, CreationMode const& mode); + bool isConverted(NodeVector* vector) const; + + Value getValue(NodeVector* vector) const; + CreationMode getCreationMode(NodeVector* vector) const; + Optional getFirstEscapingUse(NodeVector* vector, size_t lane) const; + + private: + DenseMap vectorData; + Value earliestInsertionPoint = nullptr; + }; + } + } + } +} + +#endif //SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPCONVERSION_H diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h index ab91b6c3..f2bf71f1 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h @@ -23,13 +23,13 @@ namespace mlir { explicit NodeVector(ArrayRef const& values); explicit NodeVector(ArrayRef const& operations); - bool isUniform() const; bool contains(Value const& value) const; bool containsBlockArgs() const; bool vectorizable() const; + bool splattable() const; + bool isLeaf() const; size_t numLanes() const; - size_t numOperands() const; NodeVector* getOperand(size_t index) const; @@ -54,7 +54,6 @@ namespace mlir { Value getValue(size_t lane, size_t index) const; void setValue(size_t lane, size_t index, Value const& newValue); - bool isUniform() const; bool contains(Value const& value) const; bool isRootOfNode(NodeVector const& vector) const; @@ -72,7 +71,6 @@ namespace mlir { size_t numOperands() const; static SmallVector postOrder(SLPNode* root); - static DenseMap>> escapingLanesMap(SLPNode* root); private: SmallVector> vectors; diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h index 01d88ea0..299aafc0 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h @@ -8,7 +8,6 @@ #include "mlir/IR/Operation.h" #include "SLPNode.h" -#include namespace mlir { namespace spn { diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h index af766262..b52e9a6d 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h @@ -20,9 +20,6 @@ namespace mlir { bool vectorizable(Operation* op); bool vectorizable(Value const& value); - bool isBeforeInBlock(Operation* lhs, Operation* rhs); - bool isBeforeInBlock(Value const& lhs, Value const& rhs); - bool consecutiveLoads(Value const& lhs, Value const& rhs); template @@ -70,42 +67,6 @@ namespace mlir { return true; } - template - typename std::iterator_traits::value_type firstOccurrence(Iterator begin, Iterator end) { - typename std::iterator_traits::value_type first = *begin; - while (++begin != end) { - if (!isBeforeInBlock(first, *begin)) { - first = *begin; - } - } - return first; - } - - template - typename std::iterator_traits::value_type lastOccurrence(Iterator begin, Iterator end) { - typename std::iterator_traits::value_type last = *begin; - while (++begin != end) { - if (isBeforeInBlock(last, *begin)) { - last = *begin; - } - } - return last; - } - - template - Operation* firstUser(UsableIterator begin, UsableIterator end) { - Operation* firstUser = nullptr; - while (begin != end) { - for (auto* user : begin->getUsers()) { - if (!firstUser || user->isBeforeInBlock(firstUser)) { - firstUser = user; - } - } - ++begin; - } - return firstUser; - } - template Value broadcastFirstInsertRest(ValueIterator begin, ValueIterator end, diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h index 20923ac8..41861ff9 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h @@ -9,6 +9,7 @@ #include #include "SLPNode.h" +#include "SLPConversion.h" #include "LoSPN/LoSPNDialect.h" #include "LoSPN/LoSPNOps.h" #include "mlir/Dialect/StandardOps/IR/Ops.h" @@ -27,21 +28,13 @@ namespace mlir { class SLPVectorizationPattern : public OpRewritePattern { public: - SLPVectorizationPattern(MLIRContext* context, - NodeVector* const& vector, - DenseMap& vectorizedOps, - SmallPtrSet& erasableOps) : OpRewritePattern{context}, - vector{vector}, - vectorizedOps{vectorizedOps}, - erasableOps{erasableOps} {} + SLPVectorizationPattern(MLIRContext* context, NodeVector* const& vector, ConversionState& conversionState) + : OpRewritePattern{context}, vector{vector}, conversionState{conversionState} {} protected: /// The current vector being transformed. NodeVector* const& vector; - /// Vector operations created for the SLP node vectors. - DenseMap& vectorizedOps; - - SmallPtrSet& erasableOps; + ConversionState& conversionState; }; struct VectorizeConstant : public SLPVectorizationPattern { @@ -72,12 +65,11 @@ namespace mlir { static void populateSLPVectorizationPatterns(OwningRewritePatternList& patterns, MLIRContext* context, NodeVector* const& vector, - DenseMap& vectorizedOps, - SmallPtrSet& erasableOps) { - patterns.insert(context, vector, vectorizedOps, erasableOps); - patterns.insert(context, vector, vectorizedOps, erasableOps); - patterns.insert(context, vector, vectorizedOps, erasableOps); - patterns.insert(context, vector, vectorizedOps, erasableOps); + ConversionState& conversionState) { + patterns.insert(context, vector, conversionState); + patterns.insert(context, vector, conversionState); + patterns.insert(context, vector, conversionState); + patterns.insert(context, vector, conversionState); } } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt b/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt index f704dc85..cc6fc50b 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt +++ b/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt @@ -10,6 +10,7 @@ add_mlir_conversion_library(MLIRLoSPNtoCPUTransforms Vectorization/SLP/SLPGraphBuilder.cpp Vectorization/SLP/SLPUtil.cpp Vectorization/SLP/SLPVectorizationPatterns.cpp + Vectorization/SLP/SLPConversion.cpp LoSPNtoCPUConversionPasses.cpp ADDITIONAL_HEADER_DIRS diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp new file mode 100644 index 00000000..8f80c2da --- /dev/null +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp @@ -0,0 +1,116 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#include "LoSPNtoCPU/Vectorization/SLP/SLPConversion.h" + +using namespace mlir; +using namespace mlir::spn::low::slp; + +// Helper functions in anonymous namespace. +namespace { + bool isBeforeInBlock(Value const& lhs, Value const& rhs) { + if (lhs.isa()) { + return !rhs.isa(); + } else if (rhs.isa()) { + return false; + } + return lhs.getDefiningOp()->isBeforeInBlock(rhs.getDefiningOp()); + } + + Value firstUser(Value const& element) { + Operation* first = nullptr; + for (auto* user : element.getUsers()) { + if (!first || user->isBeforeInBlock(first)) { + first = user; + } + } + return first->getResult(0); + } + +} + +ConversionState::ConversionState(SLPNode* root) { + DenseMap outsideUses; + for (auto* node : SLPNode::postOrder(root)) { + for (size_t i = node->numVectors(); i-- > 0;) { + auto* vector = node->getVector(i); + for (size_t lane = 0; lane < vector->numLanes(); ++lane) { + auto const& element = vector->getElement(lane); + // Store the graph's last input to determine the earliest possible insertion point. + if (!earliestInsertionPoint || (vector->isLeaf() && isBeforeInBlock(earliestInsertionPoint, element))) { + earliestInsertionPoint = element; + } + // Skip duplicate (splat) values. + if (outsideUses.count(element)) { + continue; + } + outsideUses[element] = std::distance(std::begin(element.getUsers()), std::end(element.getUsers())); + for (size_t j = 0; j < vector->numOperands(); ++j) { + auto* operand = vector->getOperand(j); + assert(outsideUses[operand->getElement(lane)] > 0); + outsideUses[operand->getElement(lane)]--; + } + } + } + } + + for (auto* node : SLPNode::postOrder(root)) { + for (size_t i = 0; i < node->numVectors(); ++i) { + auto* vector = node->getVector(i); + for (size_t lane = 0; lane < vector->numLanes(); ++lane) { + auto const& element = vector->getElement(lane); + if (outsideUses[element] > 0) { + vectorData[vector].firstEscapingUses.insert(std::make_pair(lane, firstUser(element))); + } + } + } + } +} + +Value ConversionState::getInsertionPoint(NodeVector* vector) const { + Value insertionPoint = earliestInsertionPoint; + for (size_t i = 0; i < vector->numOperands(); ++i) { + auto* operand = vector->getOperand(i); + if (!vectorData.count(operand)) { + continue; + } + assert(vectorData.lookup(operand).operation.hasValue() && "operand has not yet been converted"); + auto const& operandValue = vectorData.lookup(operand).operation.getValue(); + if (isBeforeInBlock(insertionPoint, operandValue)) { + insertionPoint = operandValue; + } + } + return insertionPoint; +} + +void ConversionState::update(NodeVector* vector, Value const& operation, CreationMode const& mode) { + auto& data = vectorData[vector]; + assert(!data.operation.hasValue() && !data.mode.hasValue() && "vector has been converted already"); + data.operation = operation; + data.mode = mode; + if (vector->isLeaf()) { + if (isBeforeInBlock(earliestInsertionPoint, operation)) { + earliestInsertionPoint = operation; + } + } +} + +bool ConversionState::isConverted(NodeVector* vector) const { + return vectorData.lookup(vector).operation.hasValue(); +} + +Value ConversionState::getValue(NodeVector* vector) const { + assert(!isConverted(vector) && "vector has not yet been converted"); + return vectorData.lookup(vector).operation.getValue(); +} + +CreationMode ConversionState::getCreationMode(NodeVector* vector) const { + assert(vectorData.lookup(vector).mode.hasValue() && "vector has not yet been converted"); + return vectorData.lookup(vector).mode.getValue(); +} + +Optional ConversionState::getFirstEscapingUse(NodeVector* vector, size_t lane) const { + return vectorData.lookup(vector).firstEscapingUses.lookup(lane); +} diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp index 4381b254..c9810d33 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp @@ -29,15 +29,6 @@ NodeVector::NodeVector(ArrayRef const& operations) { } } -bool NodeVector::isUniform() const { - return std::all_of(std::begin(values), std::end(values), [&](Value const& value) { - if (value.isa()) { - return false; - } - return value.getDefiningOp()->getName() == values.front().getDefiningOp()->getName(); - }); -} - bool NodeVector::contains(Value const& value) const { return std::find(std::begin(values), std::end(values), value) != std::end(values); } @@ -54,6 +45,21 @@ bool NodeVector::vectorizable() const { }); } +bool NodeVector::splattable() const { + if (containsBlockArgs()) { + return std::all_of(std::begin(values), std::end(values), [&](Value const& element) { + return element == values.front(); + }); + } + return std::all_of(std::begin(values), std::end(values), [&](Value const& element) { + return OperationEquivalence::isEquivalentTo(element.getDefiningOp(), values.front().getDefiningOp()); + }); +} + +bool NodeVector::isLeaf() const { + return operands.empty(); +} + size_t NodeVector::numLanes() const { return values.size(); } @@ -104,12 +110,6 @@ void SLPNode::setValue(size_t lane, size_t index, Value const& newValue) { vectors[index]->values[lane] = newValue; } -bool SLPNode::isUniform() const { - return std::all_of(std::begin(vectors), std::end(vectors), [&](auto const& nodeVector) { - return nodeVector->isUniform(); - }); -} - bool SLPNode::contains(Value const& value) const { return std::any_of(std::begin(vectors), std::end(vectors), [&](auto const& nodeVector) { return nodeVector->contains(value); @@ -182,44 +182,3 @@ SmallVector SLPNode::postOrder(SLPNode* root) { order.emplace_back(root); return order; } - -/// Traverse the entire graph starting at root and gather lanes of each node vector that has escaping uses. -DenseMap>> SLPNode::escapingLanesMap(SLPNode* root) { - DenseMap outsideUses; - for (auto* node : postOrder(root)) { - for (size_t i = node->numVectors(); i-- > 0;) { - auto* vector = node->getVector(i); - for (size_t lane = 0; lane < vector->numLanes(); ++lane) { - auto const& element = vector->getElement(lane); - // Skip duplicate (splat) values. - if (outsideUses.count(element)) { - continue; - } - outsideUses[element] = std::distance(std::begin(element.getUses()), std::end(element.getUses())); - for (size_t j = 0; j < vector->numOperands(); ++j) { - auto* operand = vector->getOperand(j); - assert(outsideUses[operand->getElement(lane)] > 0); - outsideUses[operand->getElement(lane)]--; - } - } - } - } - - DenseMap>> escapingLanes; - for (auto* node : postOrder(root)) { - for (size_t i = 0; i < node->numVectors(); ++i) { - auto* vector = node->getVector(i); - for (size_t lane = 0; lane < vector->numLanes(); ++lane) { - if (outsideUses[vector->getElement(lane)] > 0) { - if (escapingLanes.count(vector)) { - escapingLanes[vector]->insert(lane); - } else { - SmallPtrSet lanes{lane}; - escapingLanes[vector] = std::make_shared>(lanes); - } - } - } - } - } - return escapingLanes; -} diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp index d80fdae9..93459108 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp @@ -5,18 +5,20 @@ #include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" #include "mlir/Dialect/StandardOps/IR/Ops.h" -#include "llvm/ADT/SmallPtrSet.h" #include #include using namespace mlir; +using namespace mlir::spn; +using namespace mlir::spn::low; +using namespace mlir::spn::low::slp; -bool mlir::spn::low::slp::vectorizable(Operation* op) { +bool slp::vectorizable(Operation* op) { return (op->hasTrait() || op->hasTrait()) && op->hasTrait() && op->getResult(0).getType().isIntOrFloat(); } -bool mlir::spn::low::slp::vectorizable(Value const& value) { +bool slp::vectorizable(Value const& value) { // Block arguments don't have defining ops (they can still be put in a vector by other means). if (auto* definingOp = value.getDefiningOp()) { if (!vectorizable(definingOp)) { @@ -26,20 +28,7 @@ bool mlir::spn::low::slp::vectorizable(Value const& value) { return value.getType().isIntOrFloat(); } -bool mlir::spn::low::slp::isBeforeInBlock(Operation* lhs, Operation* rhs) { - return lhs->isBeforeInBlock(rhs); -} - -bool mlir::spn::low::slp::isBeforeInBlock(Value const& lhs, Value const& rhs) { - if (lhs.isa()) { - return !rhs.isa(); - } else if (rhs.isa()) { - return false; - } - return lhs.getDefiningOp()->isBeforeInBlock(rhs.getDefiningOp()); -} - -bool mlir::spn::low::slp::consecutiveLoads(Value const& lhs, Value const& rhs) { +bool slp::consecutiveLoads(Value const& lhs, Value const& rhs) { if (lhs == rhs || lhs.isa() || rhs.isa()) { return false; } @@ -60,7 +49,7 @@ bool mlir::spn::low::slp::consecutiveLoads(Value const& lhs, Value const& rhs) { return true; } -void mlir::spn::low::slp::dumpSLPNode(mlir::spn::low::slp::SLPNode const& node) { +void slp::dumpSLPNode(SLPNode const& node) { for (size_t i = node.numVectors(); i-- > 0;) { dumpSLPNodeVector(*node.getVector(i)); } @@ -85,7 +74,7 @@ namespace { } } -void mlir::spn::low::slp::dumpSLPNodeVector(const mlir::spn::low::slp::NodeVector& nodeVector) { +void slp::dumpSLPNodeVector(NodeVector const& nodeVector) { for (size_t lane = 0; lane < nodeVector.numLanes(); ++lane) { if (!nodeVector[lane].isa()) { llvm::dbgs() << nodeVector[lane] << " (" << nodeVector[lane].getDefiningOp() << ")"; @@ -99,7 +88,7 @@ void mlir::spn::low::slp::dumpSLPNodeVector(const mlir::spn::low::slp::NodeVecto llvm::dbgs() << "\n"; } -void mlir::spn::low::slp::dumpOpTree(ArrayRef const& values) { +void slp::dumpOpTree(ArrayRef const& values) { DenseMap nodes; SmallVector> edges; @@ -156,7 +145,7 @@ void mlir::spn::low::slp::dumpOpTree(ArrayRef const& values) { llvm::dbgs() << "}\n"; } -void mlir::spn::low::slp::dumpSLPGraph(SLPNode const& root) { +void slp::dumpSLPGraph(SLPNode const& root) { llvm::dbgs() << "digraph debug_graph {\n"; llvm::dbgs() << "rankdir = BT;\n"; diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp index b0e89c97..4fb7bea2 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp @@ -55,56 +55,51 @@ namespace { LogicalResult VectorizeConstant::matchAndRewrite(ConstantOp constantOp, PatternRewriter& rewriter) const { - if (vectorizedOps.count(vector)) { - return constantOp->emitError("operation's vector has already been created"); + if (conversionState.isConverted(vector)) { + return constantOp.emitError("operation's vector has already been created"); } auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), constantOp.getType()); - rewriter.setInsertionPoint(firstUser(vector->begin(), vector->end())); - - if (!vector->isUniform()) { - auto vectorVal = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); - vectorizedOps[vector] = vectorVal; - return success(); - } - SmallVector constants; for (auto const& value : *vector) { constants.emplace_back(value.getDefiningOp().value()); } - auto const& elements = denseFloatingPoints(std::begin(constants), std::end(constants), vectorType); - auto constVector = rewriter.create(constantOp->getLoc(), elements); + rewriter.setInsertionPointAfterValue(conversionState.getInsertionPoint(vector)); + auto const& elements = denseFloatingPoints(std::begin(constants), std::end(constants), vectorType); + auto constVector = rewriter.create(constantOp.getLoc(), elements); - vectorizedOps[vector] = constVector; - erasableOps.insert(vector); + conversionState.update(vector, constVector, CreationMode::Constant); return success(); } LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead batchReadOp, PatternRewriter& rewriter) const { - if (vectorizedOps.count(vector)) { - return batchReadOp->emitError("operation's vector has already been created"); + if (conversionState.isConverted(vector)) { + return batchReadOp.emitError("operation's vector has already been created"); } auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), batchReadOp.getType()); - rewriter.setInsertionPoint(firstUser(vector->begin(), vector->end())); + rewriter.setInsertionPointAfterValue(conversionState.getInsertionPoint(vector)); - if (!vector->isUniform() || !consecutiveLoads(vector->begin(), vector->end())) { - auto vectorVal = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); - vectorizedOps[vector] = vectorVal; + if (!consecutiveLoads(vector->begin(), vector->end())) { + if (vector->splattable()) { + auto const& element = vector->getElement(0); + auto broadcast = rewriter.create(batchReadOp.getLoc(), vectorType, element); + conversionState.update(vector, broadcast, CreationMode::Splat); + } else { + auto vectorOperation = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); + conversionState.update(vector, vectorOperation, CreationMode::BroadcastInsert); + } } else { - - auto batchReadLoc = batchReadOp->getLoc(); + auto batchReadLoc = batchReadOp.getLoc(); auto memIndex = rewriter.create(batchReadLoc, rewriter.getIndexAttr(batchReadOp.sampleIndex())); ValueRange indices{batchReadOp.batchIndex(), memIndex}; auto vectorLoad = rewriter.create(batchReadLoc, vectorType, batchReadOp.batchMem(), indices); - - vectorizedOps[vector] = vectorLoad; - erasableOps.insert(vector); + conversionState.update(vector, vectorLoad, CreationMode::ConsecutiveLoad); } return success(); @@ -112,88 +107,82 @@ LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead batchReadOp, Patt LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd addOp, PatternRewriter& rewriter) const { - if (vectorizedOps.count(vector)) { - return addOp->emitError("operation's vector has already been created"); + if (conversionState.isConverted(vector)) { + return addOp.emitError("operation's vector has already been created"); } auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), addOp.getType()); - rewriter.setInsertionPoint(firstUser(vector->begin(), vector->end())); + rewriter.setInsertionPointAfterValue(conversionState.getInsertionPoint(vector)); - if (!vector->isUniform()) { - auto vectorVal = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); - vectorizedOps[vector] = vectorVal; + if (vector->isLeaf()) { + assert(!vector->splattable() && "addition vector should not be a leaf vector if it is splattable"); + auto vectorOperation = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); + conversionState.update(vector, vectorOperation, CreationMode::BroadcastInsert); return success(); } SmallVector operands; for (unsigned i = 0; i < addOp.getNumOperands(); ++i) { - if (!vectorizedOps.count(vector->getOperand(i))) { - return addOp->emitError("operand #" + std::to_string(i) + " has not yet been vectorized"); + auto* operand = vector->getOperand(i); + if (!conversionState.isConverted(operand)) { + return addOp.emitError("operand #" + std::to_string(i) + " has not yet been vectorized"); } - auto const& operand = vectorizedOps[vector->getOperand(i)]; - operands.emplace_back(operand); + operands.emplace_back(conversionState.getValue(operand)); } - auto vectorAddOp = rewriter.create(addOp->getLoc(), vectorType, operands); - - vectorizedOps[vector] = vectorAddOp; - erasableOps.insert(vector); + auto vectorAdd = rewriter.create(addOp.getLoc(), vectorType, operands); + conversionState.update(vector, vectorAdd, CreationMode::Default); return success(); } LogicalResult VectorizeMul::matchAndRewrite(SPNMul mulOp, PatternRewriter& rewriter) const { - if (vectorizedOps.count(vector)) { - return mulOp->emitError("operation's vector has already been created"); + if (conversionState.isConverted(vector)) { + return mulOp.emitError("operation's vector has already been created"); } auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), mulOp.getType()); - rewriter.setInsertionPoint(firstUser(vector->begin(), vector->end())); + rewriter.setInsertionPointAfterValue(conversionState.getInsertionPoint(vector)); - if (!vector->isUniform()) { - auto vectorVal = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); - vectorizedOps[vector] = vectorVal; + if (vector->isLeaf()) { + assert(!vector->splattable() && "multiplication vector should not be a leaf vector if it is splattable"); + auto vectorOperation = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); + conversionState.update(vector, vectorOperation, CreationMode::BroadcastInsert); return success(); } SmallVector operands; for (unsigned i = 0; i < mulOp.getNumOperands(); ++i) { - if (!vectorizedOps.count(vector->getOperand(i))) { - return mulOp->emitError("operand #" + std::to_string(i) + " has not yet been vectorized"); + auto* operand = vector->getOperand(i); + if (!conversionState.isConverted(operand)) { + return mulOp.emitError("operand #" + std::to_string(i) + " has not yet been vectorized"); } - auto const& operand = vectorizedOps[vector->getOperand(i)]; - operands.emplace_back(operand); + operands.emplace_back(conversionState.getValue(operand)); } - auto vectorAddOp = rewriter.create(mulOp->getLoc(), vectorType, operands); - - vectorizedOps[vector] = vectorAddOp; - erasableOps.insert(vector); + auto vectorAdd = rewriter.create(mulOp.getLoc(), vectorType, operands); + conversionState.update(vector, vectorAdd, CreationMode::Default); return success(); } LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, PatternRewriter& rewriter) const { - if (vectorizedOps.count(vector)) { - return gaussianOp->emitError("operation's vector has already been created"); + if (conversionState.isConverted(vector)) { + return gaussianOp.emitError("operation's vector has already been created"); } auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), gaussianOp.getType()); - auto const& firstValue = firstOccurrence(vector->begin(), vector->end()); - if (firstValue.isa()) { - rewriter.setInsertionPointAfterValue(firstValue); - } else { - rewriter.setInsertionPoint(firstValue.getDefiningOp()); - } + rewriter.setInsertionPointAfterValue(conversionState.getInsertionPoint(vector)); - if (!vector->isUniform()) { - auto vectorVal = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); - vectorizedOps[vector] = vectorVal; + if (vector->isLeaf()) { + assert(!vector->splattable() && "gaussian vector should not be a leaf vector if it is splattable"); + auto vectorOperation = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); + conversionState.update(vector, vectorOperation, CreationMode::BroadcastInsert); return success(); } @@ -229,13 +218,13 @@ LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, Pat auto const& stddevs = denseFloatingPoints(std::begin(stddevAttributes), std::end(stddevAttributes), vectorType); // Grab the input vector. - if (!vectorizedOps.count(vector->getOperand(0))) { - return gaussianOp->emitError("input vector has not yet been vectorized"); + if (conversionState.isConverted(vector->getOperand(0))) { + return gaussianOp.emitError("input vector has not yet been vectorized"); } - Value const& inputVector = vectorizedOps[vector->getOperand(0)]; + Value const& inputVector = conversionState.getValue(vector->getOperand(0)); // Calculate Gaussian distribution using e^(-0.5 * ((x - mean) / stddev)^2)) / (stddev * sqrt(2 * PI)) - auto const& gaussianLoc = gaussianOp->getLoc(); + auto const& gaussianLoc = gaussianOp.getLoc(); // (x - mean) auto meanVector = rewriter.create(gaussianLoc, means); @@ -255,8 +244,7 @@ LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, Pat auto coefficientVector = rewriter.create(gaussianLoc, coefficients); gaussianVector = rewriter.create(gaussianLoc, coefficientVector, gaussianVector); - vectorizedOps[vector] = gaussianVector; - erasableOps.insert(vector); + conversionState.update(vector, gaussianVector, CreationMode::Default); return success(); } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp index dfe9cbd7..d3eb1f68 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp @@ -13,10 +13,15 @@ #include "mlir/IR/BlockAndValueMapping.h" #include "llvm/Support/FormatVariadic.h" -mlir::LogicalResult mlir::spn::VectorizeTask::createFunctionIfVectorizable(low::SPNTask& task, - ArrayRef const& operands, - ConversionPatternRewriter& rewriter, - FuncOp* function) const { +using namespace mlir; +using namespace mlir::spn; +using namespace mlir::spn::low; +using namespace mlir::spn::low::slp; + +LogicalResult VectorizeTask::createFunctionIfVectorizable(SPNTask& task, + ArrayRef const& operands, + ConversionPatternRewriter& rewriter, + FuncOp* function) const { static int taskCount = 0; assert(operands.back().getType().isa()); @@ -50,7 +55,7 @@ mlir::LogicalResult mlir::spn::VectorizeTask::createFunctionIfVectorizable(low:: << computationType; auto const& insertionPoint = rewriter.saveInsertionPoint(); - rewriter.setInsertionPointToStart(task->getParentOfType().getBody()); + rewriter.setInsertionPointToStart(task->getParentOfType().getBody()); SmallVector inputTypes; for (auto operand : operands) { inputTypes.push_back(operand.getType()); @@ -61,13 +66,12 @@ mlir::LogicalResult mlir::spn::VectorizeTask::createFunctionIfVectorizable(low:: return success(); } -mlir::LogicalResult mlir::spn::VectorizeSingleTask::matchAndRewrite(mlir::spn::low::SPNTask task, - llvm::ArrayRef operands, - mlir::ConversionPatternRewriter& rewriter) const { +LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, + llvm::ArrayRef operands, + ConversionPatternRewriter& rewriter) const { if (task.batchSize() > 1) { - return rewriter.notifyMatchFailure(task, - "Specialized for single batch vectorization, does not match for batchSize > 1"); + return rewriter.notifyMatchFailure(task, "Single batch vectorization does not match for batchSize > 1"); } auto const& callPoint = rewriter.saveInsertionPoint(); @@ -96,29 +100,26 @@ mlir::LogicalResult mlir::spn::VectorizeSingleTask::matchAndRewrite(mlir::spn::l task->emitRemark() << "Computing new seed for SLP vectorization..."; auto computationType = operands.back().getType().dyn_cast().getElementType(); auto hwVectorWidth = TargetInformation::nativeCPUTarget().getHWVectorEntries(computationType); - low::slp::SeedAnalysis seedAnalysis{taskFunc}; - auto const& seed = seedAnalysis.getSeed(hwVectorWidth, low::slp::SearchMode::UseBeforeDef); + SeedAnalysis seedAnalysis{taskFunc}; + auto const& seed = seedAnalysis.getSeed(hwVectorWidth, SearchMode::UseBeforeDef); assert(!seed.empty() && "couldn't find a seed!"); // low::slp::dumpOpTree(seed); // seed.front().getDefiningOp()->getBlock()->dump(); - low::slp::SLPGraphBuilder builder{3}; + SLPGraphBuilder builder{3}; auto graph = builder.build(seed); // low::slp::dumpSLPGraph(*graph); task->emitRemark() << "Transforming SLP graph back into vectorized ops..."; - // Maps individual SLP node vectors to finished vector operations. - DenseMap vectorizedOps; - // Marks operations as potentially erasable (potentially because they might have escaping uses). - SmallPtrSet erasableOps; - // Stores escaping uses for vector extractions that might be necessary later on. - auto const& escapingLanes = low::slp::SLPNode::escapingLanesMap(graph.get()); + // The current vector being transformed. + NodeVector* vector = nullptr; + + ConversionState conversionState{graph.get()}; + // Prevent extracting/removing values more than once (happens in splat mode, if they appear in multiple vectors, ...). SmallPtrSet finishedValues; - // The current vector being transformed. - low::slp::NodeVector* vector = nullptr; OwningRewritePatternList patterns; - populateSLPVectorizationPatterns(patterns, rewriter.getContext(), vector, vectorizedOps, erasableOps); + populateSLPVectorizationPatterns(patterns, rewriter.getContext(), vector, conversionState); FrozenRewritePatternList frozenPatterns(std::move(patterns)); // Use a custom pattern driver that does *not* perform folding automatically (which the other rewrite drivers @@ -130,23 +131,25 @@ mlir::LogicalResult mlir::spn::VectorizeSingleTask::matchAndRewrite(mlir::spn::l applicator.applyDefaultCostModel(); // Traverse the SLP graph in postorder and apply the vectorization patterns. - for (auto* node : low::slp::SLPNode::postOrder(graph.get())) { + for (auto* node : SLPNode::postOrder(graph.get())) { // Also traverse nodes in postorder to properly handle multinodes. for (size_t vectorIndex = node->numVectors(); vectorIndex-- > 0;) { vector = node->getVector(vectorIndex); - // This assumes that there exist other vectors that use the current node vector directly (or don't need it), e.g. - // batch reads that get lowered to vector loads instead of using a vector of indices as input. + dumpSLPNodeVector(*vector); + // Note: continuing here means that there exist other vectors that use the current node vector + // directly (or don't need it), e.g. batch reads that get lowered to vector loads instead of using + // a vector of indices as input. if (!vector->vectorizable()) { continue; } if (vector->containsBlockArgs()) { auto const& type = VectorType::get(static_cast(vector->numLanes()), computationType); auto const& insertionPoint = rewriter.saveInsertionPoint(); - rewriter.setInsertionPoint(low::slp::firstUser(vector->begin(), vector->end())); - auto vectorOp = low::slp::broadcastFirstInsertRest(vector->begin(), vector->end(), type, rewriter); + rewriter.setInsertionPointAfterValue(conversionState.getInsertionPoint(vector)); + auto vectorOp = broadcastFirstInsertRest(vector->begin(), vector->end(), type, rewriter); rewriter.restoreInsertionPoint(insertionPoint); - vectorizedOps[vector] = vectorOp; + conversionState.update(vector, vectorOp, CreationMode::BroadcastInsert); } else { auto* vectorOp = vector->begin()->getDefiningOp(); if (failed(applicator.matchAndRewrite(vectorOp, rewriter))) { @@ -158,24 +161,31 @@ mlir::LogicalResult mlir::spn::VectorizeSingleTask::matchAndRewrite(mlir::spn::l // Create vector extractions for escaping uses & erase superfluous operations. for (size_t vectorIndex = node->numVectors(); vectorIndex-- > 0;) { vector = node->getVector(vectorIndex); - if (vectorizedOps.count(vector) && erasableOps.count(vector)) { - for (size_t lane = 0; lane < vector->numLanes(); ++lane) { - auto const& vectorValue = vector->getElement(lane); - if (finishedValues.contains(vectorValue)) { - continue; - } - if (escapingLanes.count(vector) && escapingLanes.lookup(vector)->contains(lane)) { - auto const& source = vectorizedOps[vector]; - rewriter.setInsertionPoint(low::slp::firstOccurrence(std::begin(vectorValue.getUsers()), - std::end(vectorValue.getUsers()))); - auto extractOp = rewriter.create(vectorValue.getLoc(), source, lane); + // See note above. + if (!conversionState.isConverted(vector)) { + continue; + } + auto const& creationMode = conversionState.getCreationMode(vector); + for (size_t lane = 0; lane < vector->numLanes(); ++lane) { + auto const& vectorValue = vector->getElement(lane); + if (finishedValues.contains(vectorValue)) { + continue; + } + if (creationMode == CreationMode::BroadcastInsert || (creationMode == CreationMode::Splat && lane == 0)) { + finishedValues.insert(vectorValue); + continue; + } + if (auto const& firstEscapingUse = conversionState.getFirstEscapingUse(vector, lane)) { + if (creationMode != CreationMode::Constant) { + rewriter.setInsertionPoint(firstEscapingUse->getDefiningOp()); + auto const& source = conversionState.getValue(vector); + auto extractOp = rewriter.create(vectorValue.getLoc(), source, lane); vectorValue.replaceAllUsesWith(extractOp.result()); - } else { - vectorValue.replaceAllUsesWith(vectorizedOps[vector]); } - finishedValues.insert(vectorValue); - rewriter.eraseOp(vectorValue.getDefiningOp()); } + finishedValues.insert(vectorValue); + rewriter.eraseOp(vectorValue.getDefiningOp()); + } } } @@ -186,14 +196,13 @@ mlir::LogicalResult mlir::spn::VectorizeSingleTask::matchAndRewrite(mlir::spn::l } -mlir::LogicalResult mlir::spn::VectorizeBatchTask::matchAndRewrite(mlir::spn::low::SPNTask op, - llvm::ArrayRef operands, - mlir::ConversionPatternRewriter& rewriter) const { +LogicalResult VectorizeBatchTask::matchAndRewrite(SPNTask op, + llvm::ArrayRef operands, + ConversionPatternRewriter& rewriter) const { static int taskCount = 0; if (op.batchSize() <= 1) { - return rewriter.notifyMatchFailure(op, - "Specialized for batch vectorization, does not match for batchSize == 1"); + return rewriter.notifyMatchFailure(op, "Specialized for batch vectorization, does not match for batchSize == 1"); } auto restore = rewriter.saveInsertionPoint(); @@ -219,14 +228,14 @@ mlir::LogicalResult mlir::spn::VectorizeBatchTask::matchAndRewrite(mlir::spn::lo auto taskBlock = taskFunc.addEntryBlock(); rewriter.setInsertionPointToStart(taskBlock); auto numSamples = rewriter.create(op.getLoc(), taskBlock->getArgument(0), 0); - auto vectorWidthConst = rewriter.create(op.getLoc(), rewriter.getIndexAttr(hwVectorWidth)); - auto remainder = rewriter.create(op.getLoc(), numSamples, vectorWidthConst); - auto ubVectorized = rewriter.create(op.getLoc(), numSamples, remainder); + auto vectorWidthConst = rewriter.create(op.getLoc(), rewriter.getIndexAttr(hwVectorWidth)); + auto remainder = rewriter.create(op.getLoc(), numSamples, vectorWidthConst); + auto ubVectorized = rewriter.create(op.getLoc(), numSamples, remainder); // Create the vectorized loop, iterating from 0 to ubVectorized, in steps of hwVectorWidth. - auto lbVectorized = rewriter.create(op.getLoc(), rewriter.getIndexAttr(0)); - auto stepVectorized = rewriter.create(op.getLoc(), rewriter.getIndexAttr(hwVectorWidth)); - auto vectorizedLoop = rewriter.create(op.getLoc(), lbVectorized, ubVectorized, stepVectorized); + auto lbVectorized = rewriter.create(op.getLoc(), rewriter.getIndexAttr(0)); + auto stepVectorized = rewriter.create(op.getLoc(), rewriter.getIndexAttr(hwVectorWidth)); + auto vectorizedLoop = rewriter.create(op.getLoc(), lbVectorized, ubVectorized, stepVectorized); auto& vectorLoopBody = vectorizedLoop.getLoopBody().front(); auto restoreTask = rewriter.saveInsertionPoint(); @@ -255,8 +264,8 @@ mlir::LogicalResult mlir::spn::VectorizeBatchTask::matchAndRewrite(mlir::spn::lo rewriter.restoreInsertionPoint(restoreTask); // Create the scalar epilog loop, iterating from ubVectorized to numSamples, in steps of 1. - auto stepScalar = rewriter.create(op.getLoc(), rewriter.getIndexAttr(1)); - auto scalarLoop = rewriter.create(op.getLoc(), ubVectorized, numSamples, stepScalar); + auto stepScalar = rewriter.create(op.getLoc(), rewriter.getIndexAttr(1)); + auto scalarLoop = rewriter.create(op.getLoc(), ubVectorized, numSamples, stepScalar); auto& scalarLoopBody = scalarLoop.getLoopBody().front(); restoreTask = rewriter.saveInsertionPoint(); @@ -267,7 +276,7 @@ mlir::LogicalResult mlir::spn::VectorizeBatchTask::matchAndRewrite(mlir::spn::lo blockReplacementArgs.push_back(bArg); } rewriter.mergeBlockBefore(&op.body().front(), scalarLoopBody.getTerminator(), blockReplacementArgs); - scalarLoopBody.walk([&rewriter](low::SPNReturn ret) { + scalarLoopBody.walk([&rewriter](SPNReturn ret) { assert(ret.returnValues().empty() && "Task return should be empty"); rewriter.eraseOp(ret); }); @@ -276,7 +285,7 @@ mlir::LogicalResult mlir::spn::VectorizeBatchTask::matchAndRewrite(mlir::spn::lo rewriter.create(op->getLoc()); // Insert a call to the newly created task function. rewriter.restoreInsertionPoint(restore); - rewriter.replaceOpWithNewOp(op, taskFunc, operands); + rewriter.replaceOpWithNewOp(op, taskFunc, operands); return success(); } From 556bab07b8401ff6b085420b36305de8e4017ebb Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Mon, 26 Apr 2021 03:28:53 +0200 Subject: [PATCH 121/385] Fix: conversion state checking the opposite of what's needed. --- .../Vectorization/SLP/SLPConversion.h | 23 +++++++++---------- .../Vectorization/SLP/SLPConversion.cpp | 5 +++- 2 files changed, 15 insertions(+), 13 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h index bed62e77..f6e8caa1 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h @@ -21,18 +21,6 @@ namespace mlir { Default }; - // Storage for vector information (in anonymous namespace to hide it). - namespace { - struct NodeVectorData { - /// The operation that was created for this node vector. - Optional operation; - /// The way it was created. - Optional mode; - /// The first (i.e. smaller Loc) escaping use for each lane. - DenseMap firstEscapingUses; - }; - } - class ConversionState { public: @@ -48,6 +36,17 @@ namespace mlir { Optional getFirstEscapingUse(NodeVector* vector, size_t lane) const; private: + + struct NodeVectorData { + + /// The operation that was created for this node vector. + Optional operation{None}; + /// The way it was created. + Optional mode{None}; + /// The first (i.e. smaller Loc) escaping use for each lane. + DenseMap firstEscapingUses; + }; + DenseMap vectorData; Value earliestInsertionPoint = nullptr; }; diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp index 8f80c2da..d2f0d6f5 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp @@ -102,7 +102,7 @@ bool ConversionState::isConverted(NodeVector* vector) const { } Value ConversionState::getValue(NodeVector* vector) const { - assert(!isConverted(vector) && "vector has not yet been converted"); + assert(isConverted(vector) && "vector has not yet been converted"); return vectorData.lookup(vector).operation.getValue(); } @@ -112,5 +112,8 @@ CreationMode ConversionState::getCreationMode(NodeVector* vector) const { } Optional ConversionState::getFirstEscapingUse(NodeVector* vector, size_t lane) const { + if (!vectorData.lookup(vector).firstEscapingUses.count(lane)) { + return {None}; + } return vectorData.lookup(vector).firstEscapingUses.lookup(lane); } From 673439d2d570fcf1db284bc944e4a1805fedc9f3 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Mon, 26 Apr 2021 03:30:42 +0200 Subject: [PATCH 122/385] Some fixes and improvements to the N>1 conversion. --- .../SLP/SLPVectorizationPatterns.cpp | 12 ++++-- .../VectorizeStructurePatterns.cpp | 40 ++++++++----------- 2 files changed, 26 insertions(+), 26 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp index 4fb7bea2..ebcd1ec3 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp @@ -88,8 +88,8 @@ LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead batchReadOp, Patt if (!consecutiveLoads(vector->begin(), vector->end())) { if (vector->splattable()) { auto const& element = vector->getElement(0); - auto broadcast = rewriter.create(batchReadOp.getLoc(), vectorType, element); - conversionState.update(vector, broadcast, CreationMode::Splat); + auto vectorOperation = rewriter.create(batchReadOp.getLoc(), vectorType, element); + conversionState.update(vector, vectorOperation, CreationMode::Splat); } else { auto vectorOperation = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); conversionState.update(vector, vectorOperation, CreationMode::BroadcastInsert); @@ -102,6 +102,12 @@ LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead batchReadOp, Patt conversionState.update(vector, vectorLoad, CreationMode::ConsecutiveLoad); } + // Batch reads don't need their input vector operands. They're not vectorizable anyways (MemRefs and indices). + rewriter.eraseOp(conversionState.getValue(vector->getOperand(0)).getDefiningOp()); + rewriter.eraseOp(conversionState.getValue(vector->getOperand(1)).getDefiningOp()); + + assert(conversionState.isConverted(vector)); + return success(); } @@ -218,7 +224,7 @@ LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, Pat auto const& stddevs = denseFloatingPoints(std::begin(stddevAttributes), std::end(stddevAttributes), vectorType); // Grab the input vector. - if (conversionState.isConverted(vector->getOperand(0))) { + if (!conversionState.isConverted(vector->getOperand(0))) { return gaussianOp.emitError("input vector has not yet been vectorized"); } Value const& inputVector = conversionState.getValue(vector->getOperand(0)); diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp index d3eb1f68..337daf98 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp @@ -137,23 +137,21 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, for (size_t vectorIndex = node->numVectors(); vectorIndex-- > 0;) { vector = node->getVector(vectorIndex); dumpSLPNodeVector(*vector); - // Note: continuing here means that there exist other vectors that use the current node vector - // directly (or don't need it), e.g. batch reads that get lowered to vector loads instead of using - // a vector of indices as input. - if (!vector->vectorizable()) { - continue; - } if (vector->containsBlockArgs()) { - auto const& type = VectorType::get(static_cast(vector->numLanes()), computationType); - auto const& insertionPoint = rewriter.saveInsertionPoint(); + auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), computationType); rewriter.setInsertionPointAfterValue(conversionState.getInsertionPoint(vector)); - auto vectorOp = broadcastFirstInsertRest(vector->begin(), vector->end(), type, rewriter); - rewriter.restoreInsertionPoint(insertionPoint); - conversionState.update(vector, vectorOp, CreationMode::BroadcastInsert); + if (vector->splattable()) { + auto const& element = vector->getElement(0); + auto vectorOp = rewriter.create(element.getLoc(), vectorType, element); + conversionState.update(vector, vectorOp, CreationMode::Splat); + } else { + auto vectorOp = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); + conversionState.update(vector, vectorOp, CreationMode::BroadcastInsert); + } } else { auto* vectorOp = vector->begin()->getDefiningOp(); if (failed(applicator.matchAndRewrite(vectorOp, rewriter))) { - vectorOp->emitOpError("SLP pattern application failed (did you forget to specify the pattern?)"); + vectorOp->emitOpError("SLP pattern application failed"); } } } @@ -161,30 +159,26 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, // Create vector extractions for escaping uses & erase superfluous operations. for (size_t vectorIndex = node->numVectors(); vectorIndex-- > 0;) { vector = node->getVector(vectorIndex); - // See note above. - if (!conversionState.isConverted(vector)) { - continue; - } auto const& creationMode = conversionState.getCreationMode(vector); for (size_t lane = 0; lane < vector->numLanes(); ++lane) { - auto const& vectorValue = vector->getElement(lane); - if (finishedValues.contains(vectorValue)) { + auto const& element = vector->getElement(lane); + if (finishedValues.contains(element)) { continue; } if (creationMode == CreationMode::BroadcastInsert || (creationMode == CreationMode::Splat && lane == 0)) { - finishedValues.insert(vectorValue); + finishedValues.insert(element); continue; } if (auto const& firstEscapingUse = conversionState.getFirstEscapingUse(vector, lane)) { if (creationMode != CreationMode::Constant) { rewriter.setInsertionPoint(firstEscapingUse->getDefiningOp()); auto const& source = conversionState.getValue(vector); - auto extractOp = rewriter.create(vectorValue.getLoc(), source, lane); - vectorValue.replaceAllUsesWith(extractOp.result()); + auto extractOp = rewriter.create(element.getLoc(), source, lane); + element.replaceAllUsesWith(extractOp.result()); } } - finishedValues.insert(vectorValue); - rewriter.eraseOp(vectorValue.getDefiningOp()); + finishedValues.insert(element); + rewriter.eraseOp(element.getDefiningOp()); } } From fe78817f35c030f794822228416995196fc23ee7 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Mon, 26 Apr 2021 03:36:11 +0200 Subject: [PATCH 123/385] Fix: constant operations in use being marked as erased. --- .../Vectorization/VectorizeStructurePatterns.cpp | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp index 337daf98..7ceeeca2 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp @@ -136,7 +136,7 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, // Also traverse nodes in postorder to properly handle multinodes. for (size_t vectorIndex = node->numVectors(); vectorIndex-- > 0;) { vector = node->getVector(vectorIndex); - dumpSLPNodeVector(*vector); + // dumpSLPNodeVector(*vector); if (vector->containsBlockArgs()) { auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), computationType); rewriter.setInsertionPointAfterValue(conversionState.getInsertionPoint(vector)); @@ -165,8 +165,8 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, if (finishedValues.contains(element)) { continue; } + finishedValues.insert(element); if (creationMode == CreationMode::BroadcastInsert || (creationMode == CreationMode::Splat && lane == 0)) { - finishedValues.insert(element); continue; } if (auto const& firstEscapingUse = conversionState.getFirstEscapingUse(vector, lane)) { @@ -175,11 +175,11 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, auto const& source = conversionState.getValue(vector); auto extractOp = rewriter.create(element.getLoc(), source, lane); element.replaceAllUsesWith(extractOp.result()); + } else { + continue; } } - finishedValues.insert(element); rewriter.eraseOp(element.getDefiningOp()); - } } } From c2ef4d53bb00c0bc292172774f5015043472342a Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Tue, 27 Apr 2021 02:14:03 +0200 Subject: [PATCH 124/385] Better illegal vector handling, fixed seed analysis returning broken seeds. --- .../Vectorization/SLP/SLPConversion.h | 13 +++-- .../LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h | 5 +- .../Vectorization/SLP/SLPConversion.cpp | 24 +++++---- .../Vectorization/SLP/SLPSeeding.cpp | 25 +++++---- .../SLP/SLPVectorizationPatterns.cpp | 4 -- .../VectorizeStructurePatterns.cpp | 52 +++++++++++++------ 6 files changed, 73 insertions(+), 50 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h index f6e8caa1..3a6d3fbb 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h @@ -14,11 +14,12 @@ namespace mlir { namespace slp { enum CreationMode { + Default, Constant, Splat, BroadcastInsert, ConsecutiveLoad, - Default + Skip }; class ConversionState { @@ -28,12 +29,14 @@ namespace mlir { explicit ConversionState(SLPNode* root); Value getInsertionPoint(NodeVector* vector) const; - void update(NodeVector* vector, Value const& operation, CreationMode const& mode); bool isConverted(NodeVector* vector) const; + void update(NodeVector* vector, Value const& operation, CreationMode const& mode); + void markSkipped(NodeVector* vector); + Value getValue(NodeVector* vector) const; CreationMode getCreationMode(NodeVector* vector) const; - Optional getFirstEscapingUse(NodeVector* vector, size_t lane) const; + Optional getEarliestEscapingUse(NodeVector* vector, size_t lane) const; private: @@ -43,8 +46,8 @@ namespace mlir { Optional operation{None}; /// The way it was created. Optional mode{None}; - /// The first (i.e. smaller Loc) escaping use for each lane. - DenseMap firstEscapingUses; + /// The earliest (i.e. smallest Loc) escaping use for each lane. + DenseMap earliestEscapingUses; }; DenseMap vectorData; diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h index 299aafc0..8ec14d09 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h @@ -27,12 +27,13 @@ namespace mlir { public: - explicit SeedAnalysis(Operation* rootOp); + SeedAnalysis(Operation* rootOp, unsigned width); - ArrayRef getSeed(unsigned width, SearchMode const& mode) const; + void fillSeed(SmallVectorImpl& seed, SearchMode const& mode) const; private: Operation* rootOp; + unsigned const width; }; } } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp index d2f0d6f5..d84c92f0 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp @@ -62,7 +62,7 @@ ConversionState::ConversionState(SLPNode* root) { for (size_t lane = 0; lane < vector->numLanes(); ++lane) { auto const& element = vector->getElement(lane); if (outsideUses[element] > 0) { - vectorData[vector].firstEscapingUses.insert(std::make_pair(lane, firstUser(element))); + vectorData[vector].earliestEscapingUses.insert(std::make_pair(lane, firstUser(element))); } } } @@ -73,7 +73,7 @@ Value ConversionState::getInsertionPoint(NodeVector* vector) const { Value insertionPoint = earliestInsertionPoint; for (size_t i = 0; i < vector->numOperands(); ++i) { auto* operand = vector->getOperand(i); - if (!vectorData.count(operand)) { + if (vectorData.lookup(operand).mode == CreationMode::Skip) { continue; } assert(vectorData.lookup(operand).operation.hasValue() && "operand has not yet been converted"); @@ -86,10 +86,10 @@ Value ConversionState::getInsertionPoint(NodeVector* vector) const { } void ConversionState::update(NodeVector* vector, Value const& operation, CreationMode const& mode) { - auto& data = vectorData[vector]; - assert(!data.operation.hasValue() && !data.mode.hasValue() && "vector has been converted already"); - data.operation = operation; - data.mode = mode; + assert(!vectorData[vector].operation.hasValue() && !vectorData[vector].mode.hasValue() + && "vector has been converted already"); + vectorData[vector].operation = operation; + vectorData[vector].mode = mode; if (vector->isLeaf()) { if (isBeforeInBlock(earliestInsertionPoint, operation)) { earliestInsertionPoint = operation; @@ -97,6 +97,12 @@ void ConversionState::update(NodeVector* vector, Value const& operation, Creatio } } +void ConversionState::markSkipped(NodeVector* vector) { + auto& data = vectorData[vector]; + assert(!data.operation.hasValue() && !data.mode.hasValue() && "vector has been converted already"); + data.mode = CreationMode::Skip; +} + bool ConversionState::isConverted(NodeVector* vector) const { return vectorData.lookup(vector).operation.hasValue(); } @@ -111,9 +117,9 @@ CreationMode ConversionState::getCreationMode(NodeVector* vector) const { return vectorData.lookup(vector).mode.getValue(); } -Optional ConversionState::getFirstEscapingUse(NodeVector* vector, size_t lane) const { - if (!vectorData.lookup(vector).firstEscapingUses.count(lane)) { +Optional ConversionState::getEarliestEscapingUse(NodeVector* vector, size_t lane) const { + if (!vectorData.lookup(vector).earliestEscapingUses.count(lane)) { return {None}; } - return vectorData.lookup(vector).firstEscapingUses.lookup(lane); + return vectorData.lookup(vector).earliestEscapingUses.lookup(lane); } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp index a2486431..5463f730 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp @@ -9,7 +9,7 @@ using namespace mlir; using namespace mlir::spn::low::slp; -SeedAnalysis::SeedAnalysis(Operation* rootOp) : rootOp{rootOp} {} +SeedAnalysis::SeedAnalysis(Operation* rootOp, unsigned width) : rootOp{rootOp}, width{width} {} // Helper functions in anonymous namespace. namespace { @@ -31,8 +31,7 @@ namespace { } } -llvm::ArrayRef SeedAnalysis::getSeed(unsigned width, SearchMode const& mode) const { - +void SeedAnalysis::fillSeed(SmallVectorImpl& seed, SearchMode const& mode) const { llvm::StringMap>> seedsByOpName; auto const& opDepths = getOpDepths(rootOp); @@ -46,13 +45,13 @@ llvm::ArrayRef SeedAnalysis::getSeed(unsigned width, SearchMode const& mo return WalkResult::advance(); } bool needsNewSeed = true; - for (auto& seed : seedsByOpName[op->getName().getStringRef()]) { - if (seed.size() < width && opDepths.lookup(seed.front()) == depth) { + for (auto& potentialSeed : seedsByOpName[op->getName().getStringRef()]) { + if (potentialSeed.size() < width && opDepths.lookup(potentialSeed.front()) == depth) { // Cannot use values for seeds that are defined in different scopes. - if (seed.front().getParentRegion() != value.getParentRegion()) { + if (potentialSeed.front().getParentRegion() != value.getParentRegion()) { continue; } - seed.emplace_back(value); + potentialSeed.emplace_back(value); needsNewSeed = false; break; } @@ -65,18 +64,18 @@ llvm::ArrayRef SeedAnalysis::getSeed(unsigned width, SearchMode const& mo }); SmallVector> seeds; - // Flatten the map that maps opcode to seeds. + // Flatten the map that maps opcodes to seeds. for (auto const& entry : seedsByOpName) { - for (auto const& seed : entry.second) { - if (seed.size() != width) { + for (auto const& potentialSeed : entry.second) { + if (potentialSeed.size() != width) { continue; } - seeds.emplace_back(seed); + seeds.emplace_back(potentialSeed); } } if (seeds.empty()) { - return {}; + return; } // Sort the seeds such that either the seeds closest to the beginning of the function come first (DefBeforeUse), @@ -91,6 +90,6 @@ llvm::ArrayRef SeedAnalysis::getSeed(unsigned width, SearchMode const& mo assert(false); } }); - return seeds.front(); + seed.swap(seeds.front()); } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp index ebcd1ec3..78ed39dc 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp @@ -102,10 +102,6 @@ LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead batchReadOp, Patt conversionState.update(vector, vectorLoad, CreationMode::ConsecutiveLoad); } - // Batch reads don't need their input vector operands. They're not vectorizable anyways (MemRefs and indices). - rewriter.eraseOp(conversionState.getValue(vector->getOperand(0)).getDefiningOp()); - rewriter.eraseOp(conversionState.getValue(vector->getOperand(1)).getDefiningOp()); - assert(conversionState.isConverted(vector)); return success(); diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp index 7ceeeca2..ecf47a5a 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp @@ -66,6 +66,17 @@ LogicalResult VectorizeTask::createFunctionIfVectorizable(SPNTask& task, return success(); } +// Helper functions in anonymous namespace. +namespace { + + /// MLIR does not permit vectors of MemRefs or indices. + bool wouldBeIllegal(NodeVector* vector) { + return std::any_of(std::cbegin(*vector), std::cend(*vector), [&](Value const& element) { + return element.getType().isa() || element.getType().isa(); + }); + } +} + LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, llvm::ArrayRef operands, ConversionPatternRewriter& rewriter) const { @@ -98,16 +109,17 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, // Apply SLP vectorization. task->emitRemark() << "Computing new seed for SLP vectorization..."; + //task->getParentOfType()->dump(); auto computationType = operands.back().getType().dyn_cast().getElementType(); auto hwVectorWidth = TargetInformation::nativeCPUTarget().getHWVectorEntries(computationType); - SeedAnalysis seedAnalysis{taskFunc}; - auto const& seed = seedAnalysis.getSeed(hwVectorWidth, SearchMode::UseBeforeDef); + SeedAnalysis seedAnalysis{taskFunc, hwVectorWidth}; + SmallVector seed; + seedAnalysis.fillSeed(seed, SearchMode::UseBeforeDef); assert(!seed.empty() && "couldn't find a seed!"); - // low::slp::dumpOpTree(seed); - // seed.front().getDefiningOp()->getBlock()->dump(); + //low::slp::dumpOpTree(seed); SLPGraphBuilder builder{3}; auto graph = builder.build(seed); - // low::slp::dumpSLPGraph(*graph); + //low::slp::dumpSLPGraph(*graph); task->emitRemark() << "Transforming SLP graph back into vectorized ops..."; // The current vector being transformed. @@ -137,21 +149,24 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, for (size_t vectorIndex = node->numVectors(); vectorIndex-- > 0;) { vector = node->getVector(vectorIndex); // dumpSLPNodeVector(*vector); - if (vector->containsBlockArgs()) { + auto* vectorOp = vector->begin()->getDefiningOp(); + if (vector->containsBlockArgs() || failed(applicator.matchAndRewrite(vectorOp, rewriter))) { + // This assumes that the node vectors using these illegal vectors know what they're doing and don't need them. + // For example, SPNBatchReads being replaced with vector::LoadOps or broadcastInserts instead of using their + // vectorized operands. + if (wouldBeIllegal(vector)) { + conversionState.markSkipped(vector); + continue; + } auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), computationType); rewriter.setInsertionPointAfterValue(conversionState.getInsertionPoint(vector)); if (vector->splattable()) { auto const& element = vector->getElement(0); - auto vectorOp = rewriter.create(element.getLoc(), vectorType, element); - conversionState.update(vector, vectorOp, CreationMode::Splat); + auto vectorizedOp = rewriter.create(element.getLoc(), vectorType, element); + conversionState.update(vector, vectorizedOp, CreationMode::Splat); } else { - auto vectorOp = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); - conversionState.update(vector, vectorOp, CreationMode::BroadcastInsert); - } - } else { - auto* vectorOp = vector->begin()->getDefiningOp(); - if (failed(applicator.matchAndRewrite(vectorOp, rewriter))) { - vectorOp->emitOpError("SLP pattern application failed"); + auto vectorizedOp = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); + conversionState.update(vector, vectorizedOp, CreationMode::BroadcastInsert); } } } @@ -160,6 +175,9 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, for (size_t vectorIndex = node->numVectors(); vectorIndex-- > 0;) { vector = node->getVector(vectorIndex); auto const& creationMode = conversionState.getCreationMode(vector); + if (creationMode == CreationMode::Skip) { + continue; + } for (size_t lane = 0; lane < vector->numLanes(); ++lane) { auto const& element = vector->getElement(lane); if (finishedValues.contains(element)) { @@ -169,9 +187,9 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, if (creationMode == CreationMode::BroadcastInsert || (creationMode == CreationMode::Splat && lane == 0)) { continue; } - if (auto const& firstEscapingUse = conversionState.getFirstEscapingUse(vector, lane)) { + if (auto const& earliestEscapingUse = conversionState.getEarliestEscapingUse(vector, lane)) { if (creationMode != CreationMode::Constant) { - rewriter.setInsertionPoint(firstEscapingUse->getDefiningOp()); + rewriter.setInsertionPoint(earliestEscapingUse->getDefiningOp()); auto const& source = conversionState.getValue(vector); auto extractOp = rewriter.create(element.getLoc(), source, lane); element.replaceAllUsesWith(extractOp.result()); From 542c2e3ee7b4a116685a29e3d33eb457ea63671e Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Tue, 27 Apr 2021 02:15:18 +0200 Subject: [PATCH 125/385] Added python tests. --- .../test/test-slp-vectorized-consecutive.py | 104 ++++++++++++ .../test/test-slp-vectorized-escaping.py | 149 ++++++++++++++++++ .../test/test-slp-vectorized-mini.py | 73 +++++++++ .../test/test-slp-vectorized-tree.py | 100 ++++++++++++ 4 files changed, 426 insertions(+) create mode 100644 python-interface/test/test-slp-vectorized-consecutive.py create mode 100644 python-interface/test/test-slp-vectorized-escaping.py create mode 100644 python-interface/test/test-slp-vectorized-mini.py create mode 100644 python-interface/test/test-slp-vectorized-tree.py diff --git a/python-interface/test/test-slp-vectorized-consecutive.py b/python-interface/test/test-slp-vectorized-consecutive.py new file mode 100644 index 00000000..722dcd1f --- /dev/null +++ b/python-interface/test/test-slp-vectorized-consecutive.py @@ -0,0 +1,104 @@ +import numpy as np +import tempfile +from datetime import datetime +import os +from spn.structure.Base import Product, Sum +from spn.structure.leaves.parametric.Parametric import Gaussian +from spn.algorithms.Inference import log_likelihood +from xspn.serialization.binary.BinarySerialization import BinarySerializer +from xspn.structure.Model import SPNModel +from xspn.structure.Query import JointProbability +import spncpy as spnc + +g0 = Gaussian(mean=0.11, stdev=1, scope=0) +g1 = Gaussian(mean=0.12, stdev=0.75, scope=1) +g2 = Gaussian(mean=0.13, stdev=0.5, scope=2) +g3 = Gaussian(mean=0.14, stdev=0.25, scope=3) + +g4 = Gaussian(mean=0.21, stdev=1, scope=4) +g5 = Gaussian(mean=0.22, stdev=0.75, scope=5) +g6 = Gaussian(mean=0.23, stdev=0.5, scope=6) +g7 = Gaussian(mean=0.24, stdev=0.25, scope=7) + +g8 = Gaussian(mean=0.31, stdev=1, scope=8) +g9 = Gaussian(mean=0.32, stdev=0.75, scope=9) +g10 = Gaussian(mean=0.33, stdev=0.5, scope=10) +g11 = Gaussian(mean=0.34, stdev=0.25, scope=11) + +g12 = Gaussian(mean=0.41, stdev=1, scope=12) +g13 = Gaussian(mean=0.42, stdev=0.75, scope=13) +g14 = Gaussian(mean=0.43, stdev=0.5, scope=14) +g15 = Gaussian(mean=0.44, stdev=0.25, scope=15) + +p1 = Product(children=[g0 , g4 , g8 , g12]) +p2 = Product(children=[g1 , g5 , g6 , g7 ]) +p3 = Product(children=[g10, g9 , g2 , g11]) +p4 = Product(children=[g15, g3 , g14, g13]) +s = Sum(children=[p1, p2, p3, p4], weights=[0.25, 0.25, 0.25, 0.25]) + +# Wrap the SPN in a model and query. +model = SPNModel(s, "float64", "spn_vector") +query = JointProbability(model, batchSize=1) + +# Construct a temporary file that we will use to serialize the SPN to. +timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") +tempfile = os.path.join(tempfile.gettempdir(), f"spn_{timestamp}.bin") + +# Serialize the SPN to binary format as input to the compiler. +print("Serializing SPN to file...") +BinarySerializer(tempfile).serialize_to_file(query) +# Check that the serialization worked. +if not os.path.isfile(tempfile): + raise RuntimeError("Serialization of the SPN failed") + +# Invoke the compiler. +print("Invoking compiler...") +compiler = spnc.SPNCompiler() + +# Compile the query into a Kernel. +options = dict({"target": "CPU", "cpu-vectorize": "true"}) +k = compiler.compileQuery(tempfile, options) +# Check that the comiled Kernel actually exists. +if not os.path.isfile(k.fileName()): + raise RuntimeError("Compilation failed, not Kernel produced") + +samples = 30 + +for i in range(samples): + + # Randomly sample input values from Gaussian (normal) distributions. + inputs = np.column_stack((np.random.normal(0.5, 1), + np.random.normal(0.125, 0.25), + np.random.normal(0.345, 0.24), + np.random.normal(0.456, 0.1), + np.random.normal(0.94, 0.48), + np.random.normal(0.56, 0.42), + np.random.normal(0.76, 0.14), + np.random.normal(0.32, 0.58), + np.random.normal(0.58, 0.219), + np.random.normal(0.14, 0.52), + np.random.normal(0.24, 0.42), + np.random.normal(0.34, 0.1), + np.random.normal(0.44, 0.9), + np.random.normal(0.54, 0.7), + np.random.normal(0.64, 0.5), + np.random.normal(0.74, 0.4))) + + # Execute the compiled Kernel. + results = k.execute(1, inputs) + + # Compute the reference results using the inference from SPFlow. + reference = log_likelihood(s, inputs) + reference = reference.reshape(1) + + print(f"evaluation #{i}: result: {results}, reference: {reference}") + + # Compare computed results and reference to make sure the computation by the compiled Kernel is correct. + if not np.all(np.isclose(results, reference)): + raise RuntimeError("COMPUTATION FAILED: Results did not match reference!") + +print("COMPUTATION OK") + +# Remove the serialized SPN file and the compiled Kernel. +os.remove(tempfile) +os.remove(k.fileName()) diff --git a/python-interface/test/test-slp-vectorized-escaping.py b/python-interface/test/test-slp-vectorized-escaping.py new file mode 100644 index 00000000..5be5599e --- /dev/null +++ b/python-interface/test/test-slp-vectorized-escaping.py @@ -0,0 +1,149 @@ +import numpy as np +import tempfile +from datetime import datetime +import os +from spn.structure.Base import Product, Sum +from spn.structure.leaves.parametric.Parametric import Gaussian +from spn.structure.leaves.histogram.Histograms import Histogram +from spn.structure.leaves.parametric.Parametric import Categorical +from spn.algorithms.Inference import log_likelihood +from xspn.serialization.binary.BinarySerialization import BinarySerializer +from xspn.structure.Model import SPNModel +from xspn.structure.Query import JointProbability +import spncpy as spnc + +g0 = Gaussian(mean=0.00, stdev=1, scope=0) +g1 = Gaussian(mean=0.01, stdev=0.75, scope=1) +g2 = Gaussian(mean=0.02, stdev=0.5, scope=2) +g3 = Gaussian(mean=0.03, stdev=0.25, scope=3) +g4 = Gaussian(mean=0.04, stdev=1, scope=4) +g5 = Gaussian(mean=0.05, stdev=0.25, scope=5) +g6 = Gaussian(mean=0.06, stdev=0.5, scope=6) +g7 = Gaussian(mean=0.07, stdev=0.75, scope=7) +g8 = Gaussian(mean=0.08, stdev=1, scope=8) +g9 = Gaussian(mean=0.09, stdev=0.75, scope=9) +g10 = Gaussian(mean=0.10, stdev=1, scope=10) +g11 = Gaussian(mean=0.11, stdev=1, scope=11) + +h0 = Histogram([0., 1., 2.], [0.1, 0.9], [1, 1], scope=0) +h1 = Histogram([0., 1., 2.], [0.2, 0.8], [1, 1], scope=1) +h2 = Histogram([0., 1., 2.], [0.3, 0.7], [1, 1], scope=2) +h3 = Histogram([0., 1., 2.], [0.4, 0.6], [1, 1], scope=3) +h4 = Histogram([0., 1., 2.], [0.5, 0.5], [1, 1], scope=4) +h5 = Histogram([0., 1., 2.], [0.6, 0.4], [1, 1], scope=5) +h6 = Histogram([0., 1., 2.], [0.7, 0.3], [1, 1], scope=6) +h7 = Histogram([0., 1., 2.], [0.8, 0.2], [1, 1], scope=7) + +c0 = Categorical(p=[0.1, 0.1, 0.8], scope=0) +c1 = Categorical(p=[0.2, 0.2, 0.6], scope=1) +c2 = Categorical(p=[0.3, 0.3, 0.4], scope=2) +c3 = Categorical(p=[0.4, 0.4, 0.2], scope=3) +c4 = Categorical(p=[0.5, 0.4, 0.1], scope=4) +c5 = Categorical(p=[0.6, 0.3, 0.1], scope=5) +c6 = Categorical(p=[0.7, 0.2, 0.1], scope=6) +c7 = Categorical(p=[0.8, 0.1, 0.1], scope=7) + +s0 = Sum(children=[g8, h4], weights=[0.5, 0.5]) +s1 = Sum(children=[g9, h5], weights=[0.5, 0.5]) +s2 = Sum(children=[g10, c6], weights=[0.5, 0.5]) +s3 = Sum(children=[g11, h7], weights=[0.5, 0.5]) + +s4 = Sum(children=[s0, c4], weights=[0.5, 0.5]) +s5 = Sum(children=[s1, c5], weights=[0.5, 0.5]) +s6 = Sum(children=[s2, g6], weights=[0.5, 0.5]) +s7 = Sum(children=[s3, c7], weights=[0.5, 0.5]) + +s8 = Sum(children=[s4, g4], weights=[0.5, 0.5]) +s9 = Sum(children=[s5, g5], weights=[0.5, 0.5]) +s10 = Sum(children=[s6, h6], weights=[0.5, 0.5]) +s11 = Sum(children=[s7, g7], weights=[0.5, 0.5]) + +p0 = Product(children=[h0, s8]) +p1 = Product(children=[c1, s9]) +p2 = Product(children=[c2, s10]) +p3 = Product(children=[g3, s11]) + +p4 = Product(children=[p0, g0]) +p5 = Product(children=[p1, g1]) +p6 = Product(children=[p2, h2]) +p7 = Product(children=[p3, c3]) + +p8 = Product(children=[p4, c0]) +p9 = Product(children=[p5, h1]) +p10 = Product(children=[p6, g2]) +p11 = Product(children=[p7, h3]) + +s12 = Sum(children=[p8, p9], weights=[0.5, 0.5]) +s13 = Sum(children=[p10, p11], weights=[0.5, 0.5]) + +s14 = Sum(children=[s12, p2], weights=[0.5, 0.5]) +s15 = Sum(children=[s13, s2], weights=[0.5, 0.5]) + +spn = Product(children=[s14, s15]) + +# Wrap the SPN in a model and query. +model = SPNModel(spn, "float64", "spn_vector") +query = JointProbability(model, batchSize=1) + +# Construct a temporary file that we will use to serialize the SPN to. +timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") +tempfile = os.path.join(tempfile.gettempdir(), f"spn_{timestamp}.bin") + +# Serialize the SPN to binary format as input to the compiler. +print("Serializing SPN to file...") +BinarySerializer(tempfile).serialize_to_file(query) +# Check that the serialization worked. +if not os.path.isfile(tempfile): + raise RuntimeError("Serialization of the SPN failed") + +# Invoke the compiler. +print("Invoking compiler...") +compiler = spnc.SPNCompiler() + +# Compile the query into a Kernel. +options = dict({"target": "CPU", "cpu-vectorize": "true"}) +k = compiler.compileQuery(tempfile, options) +# Check that the comiled Kernel actually exists. +if not os.path.isfile(k.fileName()): + raise RuntimeError("Compilation failed, not Kernel produced") + +samples = 30 + +for i in range(samples): + + # Randomly sample input values from Gaussian (normal) distributions. + inputs = np.column_stack((np.random.normal(0.5, 1), + np.random.normal(0.125, 0.25), + np.random.normal(0.345, 0.24), + np.random.normal(0.456, 0.1), + np.random.normal(0.94, 0.48), + np.random.normal(0.56, 0.42), + np.random.normal(0.76, 0.14), + np.random.normal(0.32, 0.58), + np.random.normal(0.58, 0.219), + np.random.normal(0.14, 0.52), + np.random.normal(0.24, 0.42), + np.random.normal(0.34, 0.1), + np.random.normal(0.44, 0.9), + np.random.normal(0.54, 0.7), + np.random.normal(0.64, 0.5), + np.random.normal(0.74, 0.4))) + + # Execute the compiled Kernel. + results = k.execute(1, inputs) + + # Compute the reference results using the inference from SPFlow. + reference = log_likelihood(spn, inputs) + reference = reference.reshape(1) + + print(f"evaluation #{i}: result: {results}, reference: {reference}") + + # Compare computed results and reference to make sure the computation by the compiled Kernel is correct. + if not np.all(np.isclose(results, reference)): + raise RuntimeError("COMPUTATION FAILED: Results did not match reference!") + +print("COMPUTATION OK") + +# Remove the serialized SPN file and the compiled Kernel. +os.remove(tempfile) +os.remove(k.fileName()) diff --git a/python-interface/test/test-slp-vectorized-mini.py b/python-interface/test/test-slp-vectorized-mini.py new file mode 100644 index 00000000..d38d67dc --- /dev/null +++ b/python-interface/test/test-slp-vectorized-mini.py @@ -0,0 +1,73 @@ +import numpy as np +import tempfile +from datetime import datetime +import os +from spn.structure.Base import Product, Sum +from spn.structure.leaves.parametric.Parametric import Gaussian +from spn.algorithms.Inference import log_likelihood +from xspn.serialization.binary.BinarySerialization import BinarySerializer +from xspn.structure.Model import SPNModel +from xspn.structure.Query import JointProbability +import spncpy as spnc + +g0 = Gaussian(mean=0.11, stdev=1, scope=0) +g1 = Gaussian(mean=0.12, stdev=0.75, scope=1) +g2 = Gaussian(mean=0.13, stdev=0.5, scope=2) +g3 = Gaussian(mean=0.14, stdev=0.25, scope=3) + +s = Sum(children=[g0, g1, g2, g3], weights=[0.25, 0.25, 0.25, 0.25]) + +# Wrap the SPN in a model and query. +model = SPNModel(s, "float64", "spn_vector") +query = JointProbability(model, batchSize=1) + +# Construct a temporary file that we will use to serialize the SPN to. +timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") +tempfile = os.path.join(tempfile.gettempdir(), f"spn_{timestamp}.bin") + +# Serialize the SPN to binary format as input to the compiler. +print("Serializing SPN to file...") +BinarySerializer(tempfile).serialize_to_file(query) +# Check that the serialization worked. +if not os.path.isfile(tempfile): + raise RuntimeError("Serialization of the SPN failed") + +# Invoke the compiler. +print("Invoking compiler...") +compiler = spnc.SPNCompiler() + +# Compile the query into a Kernel. +options = dict({"target": "CPU", "cpu-vectorize": "true"}) +k = compiler.compileQuery(tempfile, options) +# Check that the comiled Kernel actually exists. +if not os.path.isfile(k.fileName()): + raise RuntimeError("Compilation failed, not Kernel produced") + +samples = 30 + +for i in range(samples): + + # Randomly sample input values from Gaussian (normal) distributions. + inputs = np.column_stack((np.random.normal(0.5, 1), + np.random.normal(0.125, 0.25), + np.random.normal(0.345, 0.24), + np.random.normal(0.456, 0.1))) + + # Execute the compiled Kernel. + results = k.execute(1, inputs) + + # Compute the reference results using the inference from SPFlow. + reference = log_likelihood(s, inputs) + reference = reference.reshape(1) + + print(f"evaluation #{i}: result: {results}, reference: {reference}") + + # Compare computed results and reference to make sure the computation by the compiled Kernel is correct. + if not np.all(np.isclose(results, reference)): + raise RuntimeError("COMPUTATION FAILED: Results did not match reference!") + +print("COMPUTATION OK") + +# Remove the serialized SPN file and the compiled Kernel. +os.remove(tempfile) +os.remove(k.fileName()) diff --git a/python-interface/test/test-slp-vectorized-tree.py b/python-interface/test/test-slp-vectorized-tree.py new file mode 100644 index 00000000..5fe486a4 --- /dev/null +++ b/python-interface/test/test-slp-vectorized-tree.py @@ -0,0 +1,100 @@ +import numpy as np +import tempfile +from datetime import datetime +import os +from spn.structure.Base import Product, Sum +from spn.structure.leaves.parametric.Parametric import Gaussian +from spn.algorithms.Inference import log_likelihood +from xspn.serialization.binary.BinarySerialization import BinarySerializer +from xspn.structure.Model import SPNModel +from xspn.structure.Query import JointProbability +import spncpy as spnc + +g0 = Gaussian(mean=0.11, stdev=1, scope=0) +g1 = Gaussian(mean=0.12, stdev=0.75, scope=1) +g2 = Gaussian(mean=0.13, stdev=0.5, scope=2) +g3 = Gaussian(mean=0.14, stdev=0.25, scope=3) +g4 = Gaussian(mean=0.15, stdev=1, scope=4) +g5 = Gaussian(mean=0.16, stdev=0.25, scope=5) +g6 = Gaussian(mean=0.17, stdev=0.5, scope=6) +g7 = Gaussian(mean=0.18, stdev=0.75, scope=7) +g8 = Gaussian(mean=0.19, stdev=1, scope=8) + +p0 = Product(children=[g0, g1, g2, g4]) +p1 = Product(children=[g3, g4, g4, g5]) +p2 = Product(children=[g6, g4, g7, g8]) +p3 = Product(children=[g8, g6, g4, g2]) + +s0 = Sum(children=[g0, g1, g2, p0], weights=[0.25, 0.25, 0.25, 0.25]) +s1 = Sum(children=[g3, g4, g5, p1], weights=[0.25, 0.25, 0.25, 0.25]) +s2 = Sum(children=[g6, g7, g8, p2], weights=[0.25, 0.25, 0.25, 0.25]) +s3 = Sum(children=[g0, g4, g8, p3], weights=[0.25, 0.25, 0.25, 0.25]) + +spn = Product(children=[s0, s1, s2, s3]) + +# Wrap the SPN in a model and query. +model = SPNModel(spn, "float64", "spn_vector") +query = JointProbability(model, batchSize=1) + +# Construct a temporary file that we will use to serialize the SPN to. +timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") +tempfile = os.path.join(tempfile.gettempdir(), f"spn_{timestamp}.bin") + +# Serialize the SPN to binary format as input to the compiler. +print("Serializing SPN to file...") +BinarySerializer(tempfile).serialize_to_file(query) +# Check that the serialization worked. +if not os.path.isfile(tempfile): + raise RuntimeError("Serialization of the SPN failed") + +# Invoke the compiler. +print("Invoking compiler...") +compiler = spnc.SPNCompiler() + +# Compile the query into a Kernel. +options = dict({"target": "CPU", "cpu-vectorize": "true"}) +k = compiler.compileQuery(tempfile, options) +# Check that the comiled Kernel actually exists. +if not os.path.isfile(k.fileName()): + raise RuntimeError("Compilation failed, not Kernel produced") + +samples = 30 + +for i in range(samples): + + # Randomly sample input values from Gaussian (normal) distributions. + inputs = np.column_stack((np.random.normal(0.5, 1), + np.random.normal(0.125, 0.25), + np.random.normal(0.345, 0.24), + np.random.normal(0.456, 0.1), + np.random.normal(0.94, 0.48), + np.random.normal(0.56, 0.42), + np.random.normal(0.76, 0.14), + np.random.normal(0.32, 0.58), + np.random.normal(0.58, 0.219), + np.random.normal(0.14, 0.52), + np.random.normal(0.24, 0.42), + np.random.normal(0.34, 0.1), + np.random.normal(0.44, 0.9), + np.random.normal(0.54, 0.7), + np.random.normal(0.64, 0.5), + np.random.normal(0.74, 0.4))) + + # Execute the compiled Kernel. + results = k.execute(1, inputs) + + # Compute the reference results using the inference from SPFlow. + reference = log_likelihood(spn, inputs) + reference = reference.reshape(1) + + print(f"evaluation #{i}: result: {results}, reference: {reference}") + + # Compare computed results and reference to make sure the computation by the compiled Kernel is correct. + if not np.all(np.isclose(results, reference)): + raise RuntimeError("COMPUTATION FAILED: Results did not match reference!") + +print("COMPUTATION OK") + +# Remove the serialized SPN file and the compiled Kernel. +os.remove(tempfile) +os.remove(k.fileName()) From 8edff891cb95a7acbbfc18d84f02ca4a3f7f5682 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Tue, 27 Apr 2021 02:42:51 +0200 Subject: [PATCH 126/385] Replaced error emission with hard failures. --- .../SLP/SLPVectorizationPatterns.cpp | 43 ++++--------------- 1 file changed, 9 insertions(+), 34 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp index 78ed39dc..2c39dd8e 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp @@ -55,18 +55,15 @@ namespace { LogicalResult VectorizeConstant::matchAndRewrite(ConstantOp constantOp, PatternRewriter& rewriter) const { - if (conversionState.isConverted(vector)) { - return constantOp.emitError("operation's vector has already been created"); - } - + assert(!conversionState.isConverted(vector) && "vector has already been created"); auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), constantOp.getType()); + rewriter.setInsertionPointAfterValue(conversionState.getInsertionPoint(vector)); SmallVector constants; for (auto const& value : *vector) { constants.emplace_back(value.getDefiningOp().value()); } - rewriter.setInsertionPointAfterValue(conversionState.getInsertionPoint(vector)); auto const& elements = denseFloatingPoints(std::begin(constants), std::end(constants), vectorType); auto constVector = rewriter.create(constantOp.getLoc(), elements); @@ -77,12 +74,8 @@ LogicalResult VectorizeConstant::matchAndRewrite(ConstantOp constantOp, PatternR LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead batchReadOp, PatternRewriter& rewriter) const { - if (conversionState.isConverted(vector)) { - return batchReadOp.emitError("operation's vector has already been created"); - } - + assert(!conversionState.isConverted(vector) && "vector has already been created"); auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), batchReadOp.getType()); - rewriter.setInsertionPointAfterValue(conversionState.getInsertionPoint(vector)); if (!consecutiveLoads(vector->begin(), vector->end())) { @@ -109,12 +102,8 @@ LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead batchReadOp, Patt LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd addOp, PatternRewriter& rewriter) const { - if (conversionState.isConverted(vector)) { - return addOp.emitError("operation's vector has already been created"); - } - + assert(!conversionState.isConverted(vector) && "vector has already been created"); auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), addOp.getType()); - rewriter.setInsertionPointAfterValue(conversionState.getInsertionPoint(vector)); if (vector->isLeaf()) { @@ -127,9 +116,7 @@ LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd addOp, PatternRewriter& rewri SmallVector operands; for (unsigned i = 0; i < addOp.getNumOperands(); ++i) { auto* operand = vector->getOperand(i); - if (!conversionState.isConverted(operand)) { - return addOp.emitError("operand #" + std::to_string(i) + " has not yet been vectorized"); - } + assert(conversionState.isConverted(operand) && "operand has not yet been vectorized"); operands.emplace_back(conversionState.getValue(operand)); } @@ -141,12 +128,8 @@ LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd addOp, PatternRewriter& rewri LogicalResult VectorizeMul::matchAndRewrite(SPNMul mulOp, PatternRewriter& rewriter) const { - if (conversionState.isConverted(vector)) { - return mulOp.emitError("operation's vector has already been created"); - } - + assert(!conversionState.isConverted(vector) && "vector has already been created"); auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), mulOp.getType()); - rewriter.setInsertionPointAfterValue(conversionState.getInsertionPoint(vector)); if (vector->isLeaf()) { @@ -159,9 +142,7 @@ LogicalResult VectorizeMul::matchAndRewrite(SPNMul mulOp, PatternRewriter& rewri SmallVector operands; for (unsigned i = 0; i < mulOp.getNumOperands(); ++i) { auto* operand = vector->getOperand(i); - if (!conversionState.isConverted(operand)) { - return mulOp.emitError("operand #" + std::to_string(i) + " has not yet been vectorized"); - } + assert(conversionState.isConverted(operand) && "operand has not yet been vectorized"); operands.emplace_back(conversionState.getValue(operand)); } @@ -173,12 +154,8 @@ LogicalResult VectorizeMul::matchAndRewrite(SPNMul mulOp, PatternRewriter& rewri LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, PatternRewriter& rewriter) const { - if (conversionState.isConverted(vector)) { - return gaussianOp.emitError("operation's vector has already been created"); - } - + assert(!conversionState.isConverted(vector) && "vector has already been created"); auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), gaussianOp.getType()); - rewriter.setInsertionPointAfterValue(conversionState.getInsertionPoint(vector)); if (vector->isLeaf()) { @@ -220,9 +197,7 @@ LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, Pat auto const& stddevs = denseFloatingPoints(std::begin(stddevAttributes), std::end(stddevAttributes), vectorType); // Grab the input vector. - if (!conversionState.isConverted(vector->getOperand(0))) { - return gaussianOp.emitError("input vector has not yet been vectorized"); - } + assert(conversionState.isConverted(vector->getOperand(0)) && "input vector has not yet been vectorized"); Value const& inputVector = conversionState.getValue(vector->getOperand(0)); // Calculate Gaussian distribution using e^(-0.5 * ((x - mean) / stddev)^2)) / (stddev * sqrt(2 * PI)) From 0fdb388dad00030e0a6103c97404d65095129238 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Wed, 28 Apr 2021 02:46:54 +0200 Subject: [PATCH 127/385] Fix: single task lowering not being applied when SLP vectorization fails. --- mlir/include/Conversion/LoSPNtoCPU/StructurePatterns.h | 2 +- mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/StructurePatterns.h b/mlir/include/Conversion/LoSPNtoCPU/StructurePatterns.h index f28d54d2..899aa6ea 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/StructurePatterns.h +++ b/mlir/include/Conversion/LoSPNtoCPU/StructurePatterns.h @@ -61,7 +61,7 @@ namespace mlir { static void populateLoSPNtoCPUTaskPatterns(OwningRewritePatternList& patterns, MLIRContext* context, TypeConverter& typeConverter) { - patterns.insert(typeConverter, context); + patterns.insert(typeConverter, context, 1); } } } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp b/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp index b7600d70..cf352342 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/LoSPNtoCPUConversionPasses.cpp @@ -46,9 +46,9 @@ void mlir::spn::LoSPNtoCPUStructureConversionPass::runOnOperation() { patterns = OwningRewritePatternList(); if (vectorize) { spn::populateLoSPNtoCPUVectorizationTaskPatterns(patterns, &getContext(), typeConverter); - } else { - spn::populateLoSPNtoCPUTaskPatterns(patterns, &getContext(), typeConverter); } + spn::populateLoSPNtoCPUTaskPatterns(patterns, &getContext(), typeConverter); + frozenPatterns = FrozenRewritePatternList(std::move(patterns)); if (failed(applyPartialConversion(getOperation(), target, frozenPatterns))) { From baf6331ed56ee3e1496070ecd6b13295f2771271 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Wed, 28 Apr 2021 02:48:39 +0200 Subject: [PATCH 128/385] Fix: users appearing in front of vectorized ops. --- .../Vectorization/SLP/SLPConversion.h | 13 +-- .../Vectorization/SLP/SLPConversion.cpp | 81 +++++++++---------- .../VectorizeStructurePatterns.cpp | 53 +++++++----- 3 files changed, 76 insertions(+), 71 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h index 3a6d3fbb..e895c78a 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h @@ -36,22 +36,23 @@ namespace mlir { Value getValue(NodeVector* vector) const; CreationMode getCreationMode(NodeVector* vector) const; - Optional getEarliestEscapingUse(NodeVector* vector, size_t lane) const; + bool hasEscapingUsers(Value const& value, SmallVectorImpl& users) const; private: struct NodeVectorData { - /// The operation that was created for this node vector. Optional operation{None}; /// The way it was created. Optional mode{None}; - /// The earliest (i.e. smallest Loc) escaping use for each lane. - DenseMap earliestEscapingUses; }; - DenseMap vectorData; - Value earliestInsertionPoint = nullptr; + + /// Stores escaping users for each value. + DenseMap> escapingUsers; + + Value latestInsertion; + }; } } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp index d84c92f0..59fbc554 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp @@ -4,6 +4,7 @@ // #include "LoSPNtoCPU/Vectorization/SLP/SLPConversion.h" +#include "llvm/Support/Debug.h" using namespace mlir; using namespace mlir::spn::low::slp; @@ -19,58 +20,53 @@ namespace { return lhs.getDefiningOp()->isBeforeInBlock(rhs.getDefiningOp()); } - Value firstUser(Value const& element) { - Operation* first = nullptr; - for (auto* user : element.getUsers()) { - if (!first || user->isBeforeInBlock(first)) { - first = user; + Value latestElement(NodeVector* vector) { + Value latestVal = vector->getElement(0); + for (size_t lane = 1; lane < vector->numLanes(); ++lane) { + if (isBeforeInBlock(latestVal, vector->getElement(lane))) { + latestVal = vector->getElement(lane); } } - return first->getResult(0); + return latestVal; } } ConversionState::ConversionState(SLPNode* root) { - DenseMap outsideUses; for (auto* node : SLPNode::postOrder(root)) { for (size_t i = node->numVectors(); i-- > 0;) { auto* vector = node->getVector(i); for (size_t lane = 0; lane < vector->numLanes(); ++lane) { auto const& element = vector->getElement(lane); - // Store the graph's last input to determine the earliest possible insertion point. - if (!earliestInsertionPoint || (vector->isLeaf() && isBeforeInBlock(earliestInsertionPoint, element))) { - earliestInsertionPoint = element; - } - // Skip duplicate (splat) values. - if (outsideUses.count(element)) { + if (element.isa()) { continue; } - outsideUses[element] = std::distance(std::begin(element.getUsers()), std::end(element.getUsers())); - for (size_t j = 0; j < vector->numOperands(); ++j) { - auto* operand = vector->getOperand(j); - assert(outsideUses[operand->getElement(lane)] > 0); - outsideUses[operand->getElement(lane)]--; + if (!escapingUsers.count(element)) { + escapingUsers[element].assign(std::begin(element.getUsers()), std::end(element.getUsers())); } - } - } - } - - for (auto* node : SLPNode::postOrder(root)) { - for (size_t i = 0; i < node->numVectors(); ++i) { - auto* vector = node->getVector(i); - for (size_t lane = 0; lane < vector->numLanes(); ++lane) { - auto const& element = vector->getElement(lane); - if (outsideUses[element] > 0) { - vectorData[vector].earliestEscapingUses.insert(std::make_pair(lane, firstUser(element))); + llvm::dbgs() << "element: " << element << "\n"; + for (auto const& operand : element.getDefiningOp()->getOperands()) { + if (!operand.isa()) { + llvm::dbgs() << "\toperand: " << operand << "\n"; + auto& users = escapingUsers[operand]; + for (auto* user : users) { + llvm::dbgs() << "\t\toperand user: " << *user << "\n"; + } + users.erase(std::remove(std::begin(users), std::end(users), element.getDefiningOp())); + } } } } } + for (auto& entry : escapingUsers) { + std::sort(std::begin(entry.second), std::end(entry.second), [&](Operation* lhs, Operation* rhs) { + return lhs->isBeforeInBlock(rhs); + }); + } } Value ConversionState::getInsertionPoint(NodeVector* vector) const { - Value insertionPoint = earliestInsertionPoint; + Value insertionPoint = latestInsertion; for (size_t i = 0; i < vector->numOperands(); ++i) { auto* operand = vector->getOperand(i); if (vectorData.lookup(operand).mode == CreationMode::Skip) { @@ -78,11 +74,11 @@ Value ConversionState::getInsertionPoint(NodeVector* vector) const { } assert(vectorData.lookup(operand).operation.hasValue() && "operand has not yet been converted"); auto const& operandValue = vectorData.lookup(operand).operation.getValue(); - if (isBeforeInBlock(insertionPoint, operandValue)) { + if (!insertionPoint || isBeforeInBlock(insertionPoint, operandValue)) { insertionPoint = operandValue; } } - return insertionPoint; + return insertionPoint ? insertionPoint : latestElement(vector); } void ConversionState::update(NodeVector* vector, Value const& operation, CreationMode const& mode) { @@ -90,17 +86,15 @@ void ConversionState::update(NodeVector* vector, Value const& operation, Creatio && "vector has been converted already"); vectorData[vector].operation = operation; vectorData[vector].mode = mode; - if (vector->isLeaf()) { - if (isBeforeInBlock(earliestInsertionPoint, operation)) { - earliestInsertionPoint = operation; - } + if (!latestInsertion || isBeforeInBlock(latestInsertion, operation)) { + latestInsertion = operation; } } void ConversionState::markSkipped(NodeVector* vector) { - auto& data = vectorData[vector]; - assert(!data.operation.hasValue() && !data.mode.hasValue() && "vector has been converted already"); - data.mode = CreationMode::Skip; + assert(!vectorData[vector].operation.hasValue() && !vectorData[vector].mode.hasValue() + && "vector has been converted already"); + vectorData[vector].mode = CreationMode::Skip; } bool ConversionState::isConverted(NodeVector* vector) const { @@ -117,9 +111,10 @@ CreationMode ConversionState::getCreationMode(NodeVector* vector) const { return vectorData.lookup(vector).mode.getValue(); } -Optional ConversionState::getEarliestEscapingUse(NodeVector* vector, size_t lane) const { - if (!vectorData.lookup(vector).earliestEscapingUses.count(lane)) { - return {None}; +bool ConversionState::hasEscapingUsers(Value const& value, SmallVectorImpl& users) const { + if (!escapingUsers.count(value) || escapingUsers.lookup(value).empty()) { + return false; } - return vectorData.lookup(vector).earliestEscapingUses.lookup(lane); + users.assign(escapingUsers.lookup(value)); + return true; } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp index ecf47a5a..300c2b36 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp @@ -82,7 +82,11 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, ConversionPatternRewriter& rewriter) const { if (task.batchSize() > 1) { - return rewriter.notifyMatchFailure(task, "Single batch vectorization does not match for batchSize > 1"); + return rewriter.notifyMatchFailure(task, "SLP vectorization does not match for batchSize > 1"); + } + + if (task.body().getBlocks().size() > 1) { + return rewriter.notifyMatchFailure(task, "SLP vectorization only applicable to single basic blocks (yet)"); } auto const& callPoint = rewriter.saveInsertionPoint(); @@ -101,14 +105,13 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, // The other arguments are the arguments of the entry block of this function. SmallVector blockReplacementArgs; blockReplacementArgs.push_back(rewriter.create(task.getLoc(), rewriter.getIndexAttr(0))); - for (auto bArg : taskBlock->getArguments()) { + for (auto const& bArg : taskBlock->getArguments()) { blockReplacementArgs.push_back(bArg); } - // Inline the content of the Task into the function. + // Inline the content of the task into the function. rewriter.mergeBlocks(&task.body().front(), taskBlock, blockReplacementArgs); // Apply SLP vectorization. - task->emitRemark() << "Computing new seed for SLP vectorization..."; //task->getParentOfType()->dump(); auto computationType = operands.back().getType().dyn_cast().getElementType(); auto hwVectorWidth = TargetInformation::nativeCPUTarget().getHWVectorEntries(computationType); @@ -121,30 +124,24 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, auto graph = builder.build(seed); //low::slp::dumpSLPGraph(*graph); - task->emitRemark() << "Transforming SLP graph back into vectorized ops..."; // The current vector being transformed. NodeVector* vector = nullptr; - ConversionState conversionState{graph.get()}; - // Prevent extracting/removing values more than once (happens in splat mode, if they appear in multiple vectors, ...). SmallPtrSet finishedValues; - - OwningRewritePatternList patterns; - populateSLPVectorizationPatterns(patterns, rewriter.getContext(), vector, conversionState); - FrozenRewritePatternList frozenPatterns(std::move(patterns)); - // Use a custom pattern driver that does *not* perform folding automatically (which the other rewrite drivers // such as applyOpPatternsAndFold() would do). Folding would mess up identified SLP-vectorizable constants, which // aren't necessarily located at the front of the function's body yet. The drivers delete those that aren't at the // front (including those we identified as SLP-vectorizable!) and create new ones there, resulting in our constant // SLP-vectorization patterns not being applied to them and messing up operand handling further down the SLP graph. + OwningRewritePatternList patterns; + populateSLPVectorizationPatterns(patterns, rewriter.getContext(), vector, conversionState); + FrozenRewritePatternList frozenPatterns(std::move(patterns)); PatternApplicator applicator(frozenPatterns); applicator.applyDefaultCostModel(); // Traverse the SLP graph in postorder and apply the vectorization patterns. for (auto* node : SLPNode::postOrder(graph.get())) { - // Also traverse nodes in postorder to properly handle multinodes. for (size_t vectorIndex = node->numVectors(); vectorIndex-- > 0;) { vector = node->getVector(vectorIndex); @@ -172,12 +169,15 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, } // Create vector extractions for escaping uses & erase superfluous operations. + SmallVector escapingUsers; for (size_t vectorIndex = node->numVectors(); vectorIndex-- > 0;) { vector = node->getVector(vectorIndex); auto const& creationMode = conversionState.getCreationMode(vector); if (creationMode == CreationMode::Skip) { continue; } + // Users that have already been moved to the correct place (no need to move them more than once for each vector). + SmallPtrSet movedUsers; for (size_t lane = 0; lane < vector->numLanes(); ++lane) { auto const& element = vector->getElement(lane); if (finishedValues.contains(element)) { @@ -187,25 +187,34 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, if (creationMode == CreationMode::BroadcastInsert || (creationMode == CreationMode::Splat && lane == 0)) { continue; } - if (auto const& earliestEscapingUse = conversionState.getEarliestEscapingUse(vector, lane)) { - if (creationMode != CreationMode::Constant) { - rewriter.setInsertionPoint(earliestEscapingUse->getDefiningOp()); - auto const& source = conversionState.getValue(vector); - auto extractOp = rewriter.create(element.getLoc(), source, lane); - element.replaceAllUsesWith(extractOp.result()); - } else { + if (conversionState.hasEscapingUsers(element, escapingUsers)) { + if (creationMode == CreationMode::Constant) { continue; } + // Move all escaping users behind the vector operation. + auto const& source = conversionState.getValue(vector); + Operation* latestUser = source.getDefiningOp(); + for (auto* escapingUser : escapingUsers) { + if (movedUsers.contains(escapingUser)) { + continue; + } + movedUsers.insert(escapingUser); + if (escapingUser->isBeforeInBlock(source.getDefiningOp())) { + escapingUser->moveAfter(latestUser); + latestUser = escapingUser; + } + } + rewriter.setInsertionPoint(escapingUsers.front()); + auto extractOp = rewriter.create(element.getLoc(), source, lane); + element.replaceAllUsesWith(extractOp.result()); } rewriter.eraseOp(element.getDefiningOp()); } } } - rewriter.restoreInsertionPoint(callPoint); rewriter.replaceOpWithNewOp(task, taskFunc, operands); return success(); - } LogicalResult VectorizeBatchTask::matchAndRewrite(SPNTask op, From 9b3febbf664ffeb560975121b09343c0e88bfe27 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Wed, 28 Apr 2021 03:25:06 +0200 Subject: [PATCH 129/385] Fix: escaping use computation failing for SLP graph leaf nodes. --- .../LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp index 59fbc554..06b74fb4 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp @@ -4,7 +4,6 @@ // #include "LoSPNtoCPU/Vectorization/SLP/SLPConversion.h" -#include "llvm/Support/Debug.h" using namespace mlir; using namespace mlir::spn::low::slp; @@ -44,14 +43,9 @@ ConversionState::ConversionState(SLPNode* root) { if (!escapingUsers.count(element)) { escapingUsers[element].assign(std::begin(element.getUsers()), std::end(element.getUsers())); } - llvm::dbgs() << "element: " << element << "\n"; for (auto const& operand : element.getDefiningOp()->getOperands()) { - if (!operand.isa()) { - llvm::dbgs() << "\toperand: " << operand << "\n"; + if (!operand.isa() && escapingUsers.count(operand)) { auto& users = escapingUsers[operand]; - for (auto* user : users) { - llvm::dbgs() << "\t\toperand user: " << *user << "\n"; - } users.erase(std::remove(std::begin(users), std::end(users), element.getDefiningOp())); } } From 83781f7d5304eeb5657583d03c4aa8e90db59234 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Wed, 28 Apr 2021 03:25:31 +0200 Subject: [PATCH 130/385] Better dumpSLPGraph() output. --- .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp index 93459108..c4643930 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp @@ -182,6 +182,19 @@ void slp::dumpSLPGraph(SLPNode const& root) { llvm::dbgs() << "
batch: "; dumpBlockArgOrDefiningAddress(readOp.batchIndex()); llvm::dbgs() << "
sample: " << readOp.sampleIndex(); + } else if (auto gaussianOp = dyn_cast(definingOp)) { + llvm::dbgs() << "
index: "; + dumpBlockArgOrDefiningAddress(gaussianOp.index()); + llvm::dbgs() << "
mean: " << gaussianOp.mean().convertToDouble(); + llvm::dbgs() << "
stddev: " << gaussianOp.stddev().convertToDouble(); + } else if (auto categoricalOp = dyn_cast(definingOp)) { + llvm::dbgs() << "
index: "; + dumpBlockArgOrDefiningAddress(categoricalOp.index()); + llvm::dbgs() << "
probabilities: [ "; + for (auto const& probability : categoricalOp.probabilities()) { + llvm::dbgs() << probability << " "; + } + llvm::dbgs() << "]"; } llvm::dbgs() << ""; } From afe62612c84f0c89bd01f2878e3168ad1e05c8d2 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Wed, 28 Apr 2021 04:53:36 +0200 Subject: [PATCH 131/385] Fix: escaping uses not moving their uses recursively behind a created vector operation. --- .../Vectorization/SLP/SLPConversion.h | 8 +- .../SLP/SLPVectorizationPatterns.h | 16 ++-- .../Vectorization/SLP/SLPConversion.cpp | 78 +++++++++++++------ .../SLP/SLPVectorizationPatterns.cpp | 54 ++++++------- .../VectorizeStructurePatterns.cpp | 34 +++----- 5 files changed, 106 insertions(+), 84 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h index e895c78a..c38730c9 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h @@ -22,11 +22,11 @@ namespace mlir { Skip }; - class ConversionState { + class ConversionManager { public: - explicit ConversionState(SLPNode* root); + explicit ConversionManager(SLPNode* root); Value getInsertionPoint(NodeVector* vector) const; bool isConverted(NodeVector* vector) const; @@ -36,7 +36,9 @@ namespace mlir { Value getValue(NodeVector* vector) const; CreationMode getCreationMode(NodeVector* vector) const; - bool hasEscapingUsers(Value const& value, SmallVectorImpl& users) const; + + bool hasEscapingUsers(Value const& value) const; + Operation* moveEscapingUsersBehind(NodeVector* vector, Value const& operation) const; private: diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h index 41861ff9..1022a3ba 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h @@ -28,13 +28,13 @@ namespace mlir { class SLPVectorizationPattern : public OpRewritePattern { public: - SLPVectorizationPattern(MLIRContext* context, NodeVector* const& vector, ConversionState& conversionState) - : OpRewritePattern{context}, vector{vector}, conversionState{conversionState} {} + SLPVectorizationPattern(MLIRContext* context, NodeVector* const& vector, ConversionManager& conversionManager) + : OpRewritePattern{context}, vector{vector}, conversionManager{conversionManager} {} protected: /// The current vector being transformed. NodeVector* const& vector; - ConversionState& conversionState; + ConversionManager& conversionManager; }; struct VectorizeConstant : public SLPVectorizationPattern { @@ -65,11 +65,11 @@ namespace mlir { static void populateSLPVectorizationPatterns(OwningRewritePatternList& patterns, MLIRContext* context, NodeVector* const& vector, - ConversionState& conversionState) { - patterns.insert(context, vector, conversionState); - patterns.insert(context, vector, conversionState); - patterns.insert(context, vector, conversionState); - patterns.insert(context, vector, conversionState); + ConversionManager& conversionManager) { + patterns.insert(context, vector, conversionManager); + patterns.insert(context, vector, conversionManager); + patterns.insert(context, vector, conversionManager); + patterns.insert(context, vector, conversionManager); } } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp index 06b74fb4..126656a0 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp @@ -19,19 +19,24 @@ namespace { return lhs.getDefiningOp()->isBeforeInBlock(rhs.getDefiningOp()); } + Value latest(Value const& lhs, Value const& rhs) { + if (isBeforeInBlock(lhs, rhs)) { + return rhs; + } + return lhs; + } + Value latestElement(NodeVector* vector) { - Value latestVal = vector->getElement(0); + Value latestElement = vector->getElement(0); for (size_t lane = 1; lane < vector->numLanes(); ++lane) { - if (isBeforeInBlock(latestVal, vector->getElement(lane))) { - latestVal = vector->getElement(lane); - } + latestElement = latest(latestElement, vector->getElement(lane)); } - return latestVal; + return latestElement; } } -ConversionState::ConversionState(SLPNode* root) { +ConversionManager::ConversionManager(SLPNode* root) { for (auto* node : SLPNode::postOrder(root)) { for (size_t i = node->numVectors(); i-- > 0;) { auto* vector = node->getVector(i); @@ -59,8 +64,8 @@ ConversionState::ConversionState(SLPNode* root) { } } -Value ConversionState::getInsertionPoint(NodeVector* vector) const { - Value insertionPoint = latestInsertion; +Value ConversionManager::getInsertionPoint(NodeVector* vector) const { + Value latestVal = latestInsertion; for (size_t i = 0; i < vector->numOperands(); ++i) { auto* operand = vector->getOperand(i); if (vectorData.lookup(operand).mode == CreationMode::Skip) { @@ -68,14 +73,17 @@ Value ConversionState::getInsertionPoint(NodeVector* vector) const { } assert(vectorData.lookup(operand).operation.hasValue() && "operand has not yet been converted"); auto const& operandValue = vectorData.lookup(operand).operation.getValue(); - if (!insertionPoint || isBeforeInBlock(insertionPoint, operandValue)) { - insertionPoint = operandValue; + if (!latestVal || isBeforeInBlock(latestVal, operandValue)) { + latestVal = operandValue; } } - return insertionPoint ? insertionPoint : latestElement(vector); + if (latestVal) { + return latest(latestVal, latestElement(vector)); + } + return latestElement(vector); } -void ConversionState::update(NodeVector* vector, Value const& operation, CreationMode const& mode) { +void ConversionManager::update(NodeVector* vector, Value const& operation, CreationMode const& mode) { assert(!vectorData[vector].operation.hasValue() && !vectorData[vector].mode.hasValue() && "vector has been converted already"); vectorData[vector].operation = operation; @@ -85,30 +93,56 @@ void ConversionState::update(NodeVector* vector, Value const& operation, Creatio } } -void ConversionState::markSkipped(NodeVector* vector) { +void ConversionManager::markSkipped(NodeVector* vector) { assert(!vectorData[vector].operation.hasValue() && !vectorData[vector].mode.hasValue() && "vector has been converted already"); vectorData[vector].mode = CreationMode::Skip; } -bool ConversionState::isConverted(NodeVector* vector) const { +bool ConversionManager::isConverted(NodeVector* vector) const { return vectorData.lookup(vector).operation.hasValue(); } -Value ConversionState::getValue(NodeVector* vector) const { +Value ConversionManager::getValue(NodeVector* vector) const { assert(isConverted(vector) && "vector has not yet been converted"); return vectorData.lookup(vector).operation.getValue(); } -CreationMode ConversionState::getCreationMode(NodeVector* vector) const { +CreationMode ConversionManager::getCreationMode(NodeVector* vector) const { assert(vectorData.lookup(vector).mode.hasValue() && "vector has not yet been converted"); return vectorData.lookup(vector).mode.getValue(); } -bool ConversionState::hasEscapingUsers(Value const& value, SmallVectorImpl& users) const { - if (!escapingUsers.count(value) || escapingUsers.lookup(value).empty()) { - return false; - } - users.assign(escapingUsers.lookup(value)); - return true; +bool ConversionManager::hasEscapingUsers(Value const& value) const { + return escapingUsers.count(value) && !escapingUsers.lookup(value).empty(); } + +Operation* ConversionManager::moveEscapingUsersBehind(NodeVector* vector, Value const& value) const { + SmallVector users; + Operation* earliestEscapingUser = nullptr; + for (size_t lane = 0; lane < vector->numLanes(); ++lane) { + auto const& element = vector->getElement(lane); + for (auto* user : escapingUsers.lookup(element)) { + if (!earliestEscapingUser) { + earliestEscapingUser = user; + } + users.emplace_back(user); + } + } + for (size_t i = 0; i < users.size(); ++i) { + users.append(std::begin(users[i]->getUsers()), std::end(users[i]->getUsers())); + } + std::sort(std::begin(users), std::end(users), [&](Operation* lhs, Operation* rhs) { + return lhs->isBeforeInBlock(rhs); + }); + users.erase(std::unique(std::begin(users), std::end(users)), std::end(users)); + + Operation* latestUser = value.getDefiningOp(); + for (size_t i = 0; i < users.size(); ++i) { + if (users[i]->isBeforeInBlock(latestUser)) { + users[i]->moveAfter(latestUser); + } + latestUser = users[i]; + } + return earliestEscapingUser; +} \ No newline at end of file diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp index 2c39dd8e..8c44cc1d 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp @@ -55,9 +55,9 @@ namespace { LogicalResult VectorizeConstant::matchAndRewrite(ConstantOp constantOp, PatternRewriter& rewriter) const { - assert(!conversionState.isConverted(vector) && "vector has already been created"); + assert(!conversionManager.isConverted(vector) && "vector has already been created"); auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), constantOp.getType()); - rewriter.setInsertionPointAfterValue(conversionState.getInsertionPoint(vector)); + rewriter.setInsertionPointAfterValue(conversionManager.getInsertionPoint(vector)); SmallVector constants; for (auto const& value : *vector) { @@ -67,101 +67,101 @@ LogicalResult VectorizeConstant::matchAndRewrite(ConstantOp constantOp, PatternR auto const& elements = denseFloatingPoints(std::begin(constants), std::end(constants), vectorType); auto constVector = rewriter.create(constantOp.getLoc(), elements); - conversionState.update(vector, constVector, CreationMode::Constant); + conversionManager.update(vector, constVector, CreationMode::Constant); return success(); } LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead batchReadOp, PatternRewriter& rewriter) const { - assert(!conversionState.isConverted(vector) && "vector has already been created"); + assert(!conversionManager.isConverted(vector) && "vector has already been created"); auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), batchReadOp.getType()); - rewriter.setInsertionPointAfterValue(conversionState.getInsertionPoint(vector)); + rewriter.setInsertionPointAfterValue(conversionManager.getInsertionPoint(vector)); if (!consecutiveLoads(vector->begin(), vector->end())) { if (vector->splattable()) { auto const& element = vector->getElement(0); auto vectorOperation = rewriter.create(batchReadOp.getLoc(), vectorType, element); - conversionState.update(vector, vectorOperation, CreationMode::Splat); + conversionManager.update(vector, vectorOperation, CreationMode::Splat); } else { auto vectorOperation = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); - conversionState.update(vector, vectorOperation, CreationMode::BroadcastInsert); + conversionManager.update(vector, vectorOperation, CreationMode::BroadcastInsert); } } else { auto batchReadLoc = batchReadOp.getLoc(); auto memIndex = rewriter.create(batchReadLoc, rewriter.getIndexAttr(batchReadOp.sampleIndex())); ValueRange indices{batchReadOp.batchIndex(), memIndex}; auto vectorLoad = rewriter.create(batchReadLoc, vectorType, batchReadOp.batchMem(), indices); - conversionState.update(vector, vectorLoad, CreationMode::ConsecutiveLoad); + conversionManager.update(vector, vectorLoad, CreationMode::ConsecutiveLoad); } - assert(conversionState.isConverted(vector)); + assert(conversionManager.isConverted(vector)); return success(); } LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd addOp, PatternRewriter& rewriter) const { - assert(!conversionState.isConverted(vector) && "vector has already been created"); + assert(!conversionManager.isConverted(vector) && "vector has already been created"); auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), addOp.getType()); - rewriter.setInsertionPointAfterValue(conversionState.getInsertionPoint(vector)); + rewriter.setInsertionPointAfterValue(conversionManager.getInsertionPoint(vector)); if (vector->isLeaf()) { assert(!vector->splattable() && "addition vector should not be a leaf vector if it is splattable"); auto vectorOperation = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); - conversionState.update(vector, vectorOperation, CreationMode::BroadcastInsert); + conversionManager.update(vector, vectorOperation, CreationMode::BroadcastInsert); return success(); } SmallVector operands; for (unsigned i = 0; i < addOp.getNumOperands(); ++i) { auto* operand = vector->getOperand(i); - assert(conversionState.isConverted(operand) && "operand has not yet been vectorized"); - operands.emplace_back(conversionState.getValue(operand)); + assert(conversionManager.isConverted(operand) && "operand has not yet been vectorized"); + operands.emplace_back(conversionManager.getValue(operand)); } auto vectorAdd = rewriter.create(addOp.getLoc(), vectorType, operands); - conversionState.update(vector, vectorAdd, CreationMode::Default); + conversionManager.update(vector, vectorAdd, CreationMode::Default); return success(); } LogicalResult VectorizeMul::matchAndRewrite(SPNMul mulOp, PatternRewriter& rewriter) const { - assert(!conversionState.isConverted(vector) && "vector has already been created"); + assert(!conversionManager.isConverted(vector) && "vector has already been created"); auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), mulOp.getType()); - rewriter.setInsertionPointAfterValue(conversionState.getInsertionPoint(vector)); + rewriter.setInsertionPointAfterValue(conversionManager.getInsertionPoint(vector)); if (vector->isLeaf()) { assert(!vector->splattable() && "multiplication vector should not be a leaf vector if it is splattable"); auto vectorOperation = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); - conversionState.update(vector, vectorOperation, CreationMode::BroadcastInsert); + conversionManager.update(vector, vectorOperation, CreationMode::BroadcastInsert); return success(); } SmallVector operands; for (unsigned i = 0; i < mulOp.getNumOperands(); ++i) { auto* operand = vector->getOperand(i); - assert(conversionState.isConverted(operand) && "operand has not yet been vectorized"); - operands.emplace_back(conversionState.getValue(operand)); + assert(conversionManager.isConverted(operand) && "operand has not yet been vectorized"); + operands.emplace_back(conversionManager.getValue(operand)); } auto vectorAdd = rewriter.create(mulOp.getLoc(), vectorType, operands); - conversionState.update(vector, vectorAdd, CreationMode::Default); + conversionManager.update(vector, vectorAdd, CreationMode::Default); return success(); } LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, PatternRewriter& rewriter) const { - assert(!conversionState.isConverted(vector) && "vector has already been created"); + assert(!conversionManager.isConverted(vector) && "vector has already been created"); auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), gaussianOp.getType()); - rewriter.setInsertionPointAfterValue(conversionState.getInsertionPoint(vector)); + rewriter.setInsertionPointAfterValue(conversionManager.getInsertionPoint(vector)); if (vector->isLeaf()) { assert(!vector->splattable() && "gaussian vector should not be a leaf vector if it is splattable"); auto vectorOperation = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); - conversionState.update(vector, vectorOperation, CreationMode::BroadcastInsert); + conversionManager.update(vector, vectorOperation, CreationMode::BroadcastInsert); return success(); } @@ -197,8 +197,8 @@ LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, Pat auto const& stddevs = denseFloatingPoints(std::begin(stddevAttributes), std::end(stddevAttributes), vectorType); // Grab the input vector. - assert(conversionState.isConverted(vector->getOperand(0)) && "input vector has not yet been vectorized"); - Value const& inputVector = conversionState.getValue(vector->getOperand(0)); + assert(conversionManager.isConverted(vector->getOperand(0)) && "input vector has not yet been vectorized"); + Value const& inputVector = conversionManager.getValue(vector->getOperand(0)); // Calculate Gaussian distribution using e^(-0.5 * ((x - mean) / stddev)^2)) / (stddev * sqrt(2 * PI)) auto const& gaussianLoc = gaussianOp.getLoc(); @@ -221,7 +221,7 @@ LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, Pat auto coefficientVector = rewriter.create(gaussianLoc, coefficients); gaussianVector = rewriter.create(gaussianLoc, coefficientVector, gaussianVector); - conversionState.update(vector, gaussianVector, CreationMode::Default); + conversionManager.update(vector, gaussianVector, CreationMode::Default); return success(); } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp index 300c2b36..e68abff4 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp @@ -126,7 +126,7 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, // The current vector being transformed. NodeVector* vector = nullptr; - ConversionState conversionState{graph.get()}; + ConversionManager conversionManager{graph.get()}; // Prevent extracting/removing values more than once (happens in splat mode, if they appear in multiple vectors, ...). SmallPtrSet finishedValues; // Use a custom pattern driver that does *not* perform folding automatically (which the other rewrite drivers @@ -135,7 +135,7 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, // front (including those we identified as SLP-vectorizable!) and create new ones there, resulting in our constant // SLP-vectorization patterns not being applied to them and messing up operand handling further down the SLP graph. OwningRewritePatternList patterns; - populateSLPVectorizationPatterns(patterns, rewriter.getContext(), vector, conversionState); + populateSLPVectorizationPatterns(patterns, rewriter.getContext(), vector, conversionManager); FrozenRewritePatternList frozenPatterns(std::move(patterns)); PatternApplicator applicator(frozenPatterns); applicator.applyDefaultCostModel(); @@ -152,32 +152,30 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, // For example, SPNBatchReads being replaced with vector::LoadOps or broadcastInserts instead of using their // vectorized operands. if (wouldBeIllegal(vector)) { - conversionState.markSkipped(vector); + conversionManager.markSkipped(vector); continue; } auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), computationType); - rewriter.setInsertionPointAfterValue(conversionState.getInsertionPoint(vector)); + rewriter.setInsertionPointAfterValue(conversionManager.getInsertionPoint(vector)); if (vector->splattable()) { auto const& element = vector->getElement(0); auto vectorizedOp = rewriter.create(element.getLoc(), vectorType, element); - conversionState.update(vector, vectorizedOp, CreationMode::Splat); + conversionManager.update(vector, vectorizedOp, CreationMode::Splat); } else { auto vectorizedOp = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); - conversionState.update(vector, vectorizedOp, CreationMode::BroadcastInsert); + conversionManager.update(vector, vectorizedOp, CreationMode::BroadcastInsert); } } } // Create vector extractions for escaping uses & erase superfluous operations. - SmallVector escapingUsers; for (size_t vectorIndex = node->numVectors(); vectorIndex-- > 0;) { vector = node->getVector(vectorIndex); - auto const& creationMode = conversionState.getCreationMode(vector); + auto const& creationMode = conversionManager.getCreationMode(vector); if (creationMode == CreationMode::Skip) { continue; } // Users that have already been moved to the correct place (no need to move them more than once for each vector). - SmallPtrSet movedUsers; for (size_t lane = 0; lane < vector->numLanes(); ++lane) { auto const& element = vector->getElement(lane); if (finishedValues.contains(element)) { @@ -187,24 +185,12 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, if (creationMode == CreationMode::BroadcastInsert || (creationMode == CreationMode::Splat && lane == 0)) { continue; } - if (conversionState.hasEscapingUsers(element, escapingUsers)) { + if (conversionManager.hasEscapingUsers(element)) { if (creationMode == CreationMode::Constant) { continue; } - // Move all escaping users behind the vector operation. - auto const& source = conversionState.getValue(vector); - Operation* latestUser = source.getDefiningOp(); - for (auto* escapingUser : escapingUsers) { - if (movedUsers.contains(escapingUser)) { - continue; - } - movedUsers.insert(escapingUser); - if (escapingUser->isBeforeInBlock(source.getDefiningOp())) { - escapingUser->moveAfter(latestUser); - latestUser = escapingUser; - } - } - rewriter.setInsertionPoint(escapingUsers.front()); + auto const& source = conversionManager.getValue(vector); + rewriter.setInsertionPoint(conversionManager.moveEscapingUsersBehind(vector, source)); auto extractOp = rewriter.create(element.getLoc(), source, lane); element.replaceAllUsesWith(extractOp.result()); } From e3c3257e3d820c82fe1e12dcad9d35f25877817f Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Wed, 28 Apr 2021 05:06:54 +0200 Subject: [PATCH 132/385] Make use of conversion manager's managed data. --- .../LoSPNtoCPU/Vectorization/SLP/SLPConversion.h | 2 +- .../LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp | 7 ++++--- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h index c38730c9..32914a2e 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h @@ -38,7 +38,7 @@ namespace mlir { CreationMode getCreationMode(NodeVector* vector) const; bool hasEscapingUsers(Value const& value) const; - Operation* moveEscapingUsersBehind(NodeVector* vector, Value const& operation) const; + Operation* moveEscapingUsersBehind(NodeVector* vector) const; private: diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp index 126656a0..8f155899 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp @@ -117,7 +117,8 @@ bool ConversionManager::hasEscapingUsers(Value const& value) const { return escapingUsers.count(value) && !escapingUsers.lookup(value).empty(); } -Operation* ConversionManager::moveEscapingUsersBehind(NodeVector* vector, Value const& value) const { +Operation* ConversionManager::moveEscapingUsersBehind(NodeVector* vector) const { + assert(isConverted(vector) && "vector has not yet been converted"); SmallVector users; Operation* earliestEscapingUser = nullptr; for (size_t lane = 0; lane < vector->numLanes(); ++lane) { @@ -137,7 +138,7 @@ Operation* ConversionManager::moveEscapingUsersBehind(NodeVector* vector, Value }); users.erase(std::unique(std::begin(users), std::end(users)), std::end(users)); - Operation* latestUser = value.getDefiningOp(); + Operation* latestUser = vectorData.lookup(vector).operation->getDefiningOp(); for (size_t i = 0; i < users.size(); ++i) { if (users[i]->isBeforeInBlock(latestUser)) { users[i]->moveAfter(latestUser); @@ -145,4 +146,4 @@ Operation* ConversionManager::moveEscapingUsersBehind(NodeVector* vector, Value latestUser = users[i]; } return earliestEscapingUser; -} \ No newline at end of file +} From df30223cb5e19e8d6139cedc39408da9950d40a9 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Wed, 28 Apr 2021 05:15:23 +0200 Subject: [PATCH 133/385] Added leftover changes. --- .../LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp index e68abff4..fd9f6c1f 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp @@ -175,7 +175,6 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, if (creationMode == CreationMode::Skip) { continue; } - // Users that have already been moved to the correct place (no need to move them more than once for each vector). for (size_t lane = 0; lane < vector->numLanes(); ++lane) { auto const& element = vector->getElement(lane); if (finishedValues.contains(element)) { @@ -190,7 +189,7 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, continue; } auto const& source = conversionManager.getValue(vector); - rewriter.setInsertionPoint(conversionManager.moveEscapingUsersBehind(vector, source)); + rewriter.setInsertionPoint(conversionManager.moveEscapingUsersBehind(vector)); auto extractOp = rewriter.create(element.getLoc(), source, lane); element.replaceAllUsesWith(extractOp.result()); } From 3c0c65550e85b384d16bda79a4d87d5b91f64460 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Wed, 28 Apr 2021 15:31:01 +0200 Subject: [PATCH 134/385] Fix: operand reordering not preferring consecutive loads. --- .../LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp index 0cc44980..7ca8d8e3 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp @@ -86,7 +86,7 @@ void SLPGraphBuilder::buildGraph(NodeVector* vector, SLPNode* currentNode) const for (auto& operands : allOperands) { sortByOpcode(operands, currentOpCode); } - for (size_t i = 0; i < arity; ++i) { + for (unsigned i = 0; i < arity; ++i) { if (std::all_of(std::begin(allOperands), std::end(allOperands), [&](SmallVector const& operands) { return operands[i].getDefiningOp()->getName() == currentOpCode && !escapesMultinode(operands[i], currentNode); })) { @@ -258,8 +258,8 @@ int SLPGraphBuilder::getLookAheadScore(Value const& last, Value const& candidate if (last == candidate) { return 1; } - if (last.getDefiningOp() && consecutiveLoads(last, candidate)) { - return 1; + if (last.getDefiningOp()) { + return consecutiveLoads(last, candidate); } if (!last.isa() && !candidate.isa()) { return last.getDefiningOp()->getName() == candidate.getDefiningOp()->getName(); From f21074a8c44154bc100358ccf4e6dfdddde06cad Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Wed, 28 Apr 2021 16:07:53 +0200 Subject: [PATCH 135/385] Fix: escaping users computation failing for empty user vectors. --- .../Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp index 8f155899..62253c1a 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp @@ -49,9 +49,9 @@ ConversionManager::ConversionManager(SLPNode* root) { escapingUsers[element].assign(std::begin(element.getUsers()), std::end(element.getUsers())); } for (auto const& operand : element.getDefiningOp()->getOperands()) { - if (!operand.isa() && escapingUsers.count(operand)) { + if (!operand.isa()) { auto& users = escapingUsers[operand]; - users.erase(std::remove(std::begin(users), std::end(users), element.getDefiningOp())); + users.erase(std::remove(std::begin(users), std::end(users), element.getDefiningOp()), std::end(users)); } } } From e8438ba69decf1f5fc3e87929d319034aa21e92d Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Thu, 29 Apr 2021 02:32:34 +0200 Subject: [PATCH 136/385] Test formatting. --- .../test/test-slp-vectorized-consecutive.py | 76 +++++++++---------- .../test/test-slp-vectorized-escaping.py | 68 ++++++++--------- .../test/test-slp-vectorized-mini.py | 36 ++++----- .../test/test-slp-vectorized-tree.py | 66 ++++++++-------- 4 files changed, 123 insertions(+), 123 deletions(-) diff --git a/python-interface/test/test-slp-vectorized-consecutive.py b/python-interface/test/test-slp-vectorized-consecutive.py index 722dcd1f..47b7ebcd 100644 --- a/python-interface/test/test-slp-vectorized-consecutive.py +++ b/python-interface/test/test-slp-vectorized-consecutive.py @@ -1,14 +1,14 @@ import numpy as np +import os +import spncpy as spnc import tempfile from datetime import datetime -import os +from spn.algorithms.Inference import log_likelihood from spn.structure.Base import Product, Sum from spn.structure.leaves.parametric.Parametric import Gaussian -from spn.algorithms.Inference import log_likelihood from xspn.serialization.binary.BinarySerialization import BinarySerializer from xspn.structure.Model import SPNModel from xspn.structure.Query import JointProbability -import spncpy as spnc g0 = Gaussian(mean=0.11, stdev=1, scope=0) g1 = Gaussian(mean=0.12, stdev=0.75, scope=1) @@ -21,7 +21,7 @@ g7 = Gaussian(mean=0.24, stdev=0.25, scope=7) g8 = Gaussian(mean=0.31, stdev=1, scope=8) -g9 = Gaussian(mean=0.32, stdev=0.75, scope=9) +g9 = Gaussian(mean=0.32, stdev=0.75, scope=9) g10 = Gaussian(mean=0.33, stdev=0.5, scope=10) g11 = Gaussian(mean=0.34, stdev=0.25, scope=11) @@ -30,10 +30,10 @@ g14 = Gaussian(mean=0.43, stdev=0.5, scope=14) g15 = Gaussian(mean=0.44, stdev=0.25, scope=15) -p1 = Product(children=[g0 , g4 , g8 , g12]) -p2 = Product(children=[g1 , g5 , g6 , g7 ]) -p3 = Product(children=[g10, g9 , g2 , g11]) -p4 = Product(children=[g15, g3 , g14, g13]) +p1 = Product(children=[g0, g4, g8, g12]) +p2 = Product(children=[g1, g5, g6, g7]) +p3 = Product(children=[g10, g9, g2, g11]) +p4 = Product(children=[g15, g3, g14, g13]) s = Sum(children=[p1, p2, p3, p4], weights=[0.25, 0.25, 0.25, 0.25]) # Wrap the SPN in a model and query. @@ -66,36 +66,36 @@ for i in range(samples): - # Randomly sample input values from Gaussian (normal) distributions. - inputs = np.column_stack((np.random.normal(0.5, 1), - np.random.normal(0.125, 0.25), - np.random.normal(0.345, 0.24), - np.random.normal(0.456, 0.1), - np.random.normal(0.94, 0.48), - np.random.normal(0.56, 0.42), - np.random.normal(0.76, 0.14), - np.random.normal(0.32, 0.58), - np.random.normal(0.58, 0.219), - np.random.normal(0.14, 0.52), - np.random.normal(0.24, 0.42), - np.random.normal(0.34, 0.1), - np.random.normal(0.44, 0.9), - np.random.normal(0.54, 0.7), - np.random.normal(0.64, 0.5), - np.random.normal(0.74, 0.4))) - - # Execute the compiled Kernel. - results = k.execute(1, inputs) - - # Compute the reference results using the inference from SPFlow. - reference = log_likelihood(s, inputs) - reference = reference.reshape(1) - - print(f"evaluation #{i}: result: {results}, reference: {reference}") - - # Compare computed results and reference to make sure the computation by the compiled Kernel is correct. - if not np.all(np.isclose(results, reference)): - raise RuntimeError("COMPUTATION FAILED: Results did not match reference!") + # Randomly sample input values from Gaussian (normal) distributions. + inputs = np.column_stack((np.random.normal(0.5, 1), + np.random.normal(0.125, 0.25), + np.random.normal(0.345, 0.24), + np.random.normal(0.456, 0.1), + np.random.normal(0.94, 0.48), + np.random.normal(0.56, 0.42), + np.random.normal(0.76, 0.14), + np.random.normal(0.32, 0.58), + np.random.normal(0.58, 0.219), + np.random.normal(0.14, 0.52), + np.random.normal(0.24, 0.42), + np.random.normal(0.34, 0.1), + np.random.normal(0.44, 0.9), + np.random.normal(0.54, 0.7), + np.random.normal(0.64, 0.5), + np.random.normal(0.74, 0.4))) + + # Execute the compiled Kernel. + results = k.execute(1, inputs) + + # Compute the reference results using the inference from SPFlow. + reference = log_likelihood(s, inputs) + reference = reference.reshape(1) + + print(f"evaluation #{i}: result: {results}, reference: {reference}") + + # Compare computed results and reference to make sure the computation by the compiled Kernel is correct. + if not np.all(np.isclose(results, reference)): + raise RuntimeError("COMPUTATION FAILED: Results did not match reference!") print("COMPUTATION OK") diff --git a/python-interface/test/test-slp-vectorized-escaping.py b/python-interface/test/test-slp-vectorized-escaping.py index 5be5599e..1c141e87 100644 --- a/python-interface/test/test-slp-vectorized-escaping.py +++ b/python-interface/test/test-slp-vectorized-escaping.py @@ -1,16 +1,16 @@ import numpy as np +import os +import spncpy as spnc import tempfile from datetime import datetime -import os +from spn.algorithms.Inference import log_likelihood from spn.structure.Base import Product, Sum -from spn.structure.leaves.parametric.Parametric import Gaussian from spn.structure.leaves.histogram.Histograms import Histogram from spn.structure.leaves.parametric.Parametric import Categorical -from spn.algorithms.Inference import log_likelihood +from spn.structure.leaves.parametric.Parametric import Gaussian from xspn.serialization.binary.BinarySerialization import BinarySerializer from xspn.structure.Model import SPNModel from xspn.structure.Query import JointProbability -import spncpy as spnc g0 = Gaussian(mean=0.00, stdev=1, scope=0) g1 = Gaussian(mean=0.01, stdev=0.75, scope=1) @@ -111,36 +111,36 @@ for i in range(samples): - # Randomly sample input values from Gaussian (normal) distributions. - inputs = np.column_stack((np.random.normal(0.5, 1), - np.random.normal(0.125, 0.25), - np.random.normal(0.345, 0.24), - np.random.normal(0.456, 0.1), - np.random.normal(0.94, 0.48), - np.random.normal(0.56, 0.42), - np.random.normal(0.76, 0.14), - np.random.normal(0.32, 0.58), - np.random.normal(0.58, 0.219), - np.random.normal(0.14, 0.52), - np.random.normal(0.24, 0.42), - np.random.normal(0.34, 0.1), - np.random.normal(0.44, 0.9), - np.random.normal(0.54, 0.7), - np.random.normal(0.64, 0.5), - np.random.normal(0.74, 0.4))) - - # Execute the compiled Kernel. - results = k.execute(1, inputs) - - # Compute the reference results using the inference from SPFlow. - reference = log_likelihood(spn, inputs) - reference = reference.reshape(1) - - print(f"evaluation #{i}: result: {results}, reference: {reference}") - - # Compare computed results and reference to make sure the computation by the compiled Kernel is correct. - if not np.all(np.isclose(results, reference)): - raise RuntimeError("COMPUTATION FAILED: Results did not match reference!") + # Randomly sample input values from Gaussian (normal) distributions. + inputs = np.column_stack((np.random.normal(0.5, 1), + np.random.normal(0.125, 0.25), + np.random.normal(0.345, 0.24), + np.random.normal(0.456, 0.1), + np.random.normal(0.94, 0.48), + np.random.normal(0.56, 0.42), + np.random.normal(0.76, 0.14), + np.random.normal(0.32, 0.58), + np.random.normal(0.58, 0.219), + np.random.normal(0.14, 0.52), + np.random.normal(0.24, 0.42), + np.random.normal(0.34, 0.1), + np.random.normal(0.44, 0.9), + np.random.normal(0.54, 0.7), + np.random.normal(0.64, 0.5), + np.random.normal(0.74, 0.4))) + + # Execute the compiled Kernel. + results = k.execute(1, inputs) + + # Compute the reference results using the inference from SPFlow. + reference = log_likelihood(spn, inputs) + reference = reference.reshape(1) + + print(f"evaluation #{i}: result: {results}, reference: {reference}") + + # Compare computed results and reference to make sure the computation by the compiled Kernel is correct. + if not np.all(np.isclose(results, reference)): + raise RuntimeError("COMPUTATION FAILED: Results did not match reference!") print("COMPUTATION OK") diff --git a/python-interface/test/test-slp-vectorized-mini.py b/python-interface/test/test-slp-vectorized-mini.py index d38d67dc..2f32e807 100644 --- a/python-interface/test/test-slp-vectorized-mini.py +++ b/python-interface/test/test-slp-vectorized-mini.py @@ -1,14 +1,14 @@ import numpy as np +import os +import spncpy as spnc import tempfile from datetime import datetime -import os -from spn.structure.Base import Product, Sum -from spn.structure.leaves.parametric.Parametric import Gaussian from spn.algorithms.Inference import log_likelihood +from spn.structure.Base import Sum +from spn.structure.leaves.parametric.Parametric import Gaussian from xspn.serialization.binary.BinarySerialization import BinarySerializer from xspn.structure.Model import SPNModel from xspn.structure.Query import JointProbability -import spncpy as spnc g0 = Gaussian(mean=0.11, stdev=1, scope=0) g1 = Gaussian(mean=0.12, stdev=0.75, scope=1) @@ -47,24 +47,24 @@ for i in range(samples): - # Randomly sample input values from Gaussian (normal) distributions. - inputs = np.column_stack((np.random.normal(0.5, 1), - np.random.normal(0.125, 0.25), - np.random.normal(0.345, 0.24), - np.random.normal(0.456, 0.1))) + # Randomly sample input values from Gaussian (normal) distributions. + inputs = np.column_stack((np.random.normal(0.5, 1), + np.random.normal(0.125, 0.25), + np.random.normal(0.345, 0.24), + np.random.normal(0.456, 0.1))) - # Execute the compiled Kernel. - results = k.execute(1, inputs) + # Execute the compiled Kernel. + results = k.execute(1, inputs) - # Compute the reference results using the inference from SPFlow. - reference = log_likelihood(s, inputs) - reference = reference.reshape(1) + # Compute the reference results using the inference from SPFlow. + reference = log_likelihood(s, inputs) + reference = reference.reshape(1) - print(f"evaluation #{i}: result: {results}, reference: {reference}") + print(f"evaluation #{i}: result: {results}, reference: {reference}") - # Compare computed results and reference to make sure the computation by the compiled Kernel is correct. - if not np.all(np.isclose(results, reference)): - raise RuntimeError("COMPUTATION FAILED: Results did not match reference!") + # Compare computed results and reference to make sure the computation by the compiled Kernel is correct. + if not np.all(np.isclose(results, reference)): + raise RuntimeError("COMPUTATION FAILED: Results did not match reference!") print("COMPUTATION OK") diff --git a/python-interface/test/test-slp-vectorized-tree.py b/python-interface/test/test-slp-vectorized-tree.py index 5fe486a4..9fbde7b1 100644 --- a/python-interface/test/test-slp-vectorized-tree.py +++ b/python-interface/test/test-slp-vectorized-tree.py @@ -1,14 +1,14 @@ import numpy as np +import os +import spncpy as spnc import tempfile from datetime import datetime -import os +from spn.algorithms.Inference import log_likelihood from spn.structure.Base import Product, Sum from spn.structure.leaves.parametric.Parametric import Gaussian -from spn.algorithms.Inference import log_likelihood from xspn.serialization.binary.BinarySerialization import BinarySerializer from xspn.structure.Model import SPNModel from xspn.structure.Query import JointProbability -import spncpy as spnc g0 = Gaussian(mean=0.11, stdev=1, scope=0) g1 = Gaussian(mean=0.12, stdev=0.75, scope=1) @@ -62,36 +62,36 @@ for i in range(samples): - # Randomly sample input values from Gaussian (normal) distributions. - inputs = np.column_stack((np.random.normal(0.5, 1), - np.random.normal(0.125, 0.25), - np.random.normal(0.345, 0.24), - np.random.normal(0.456, 0.1), - np.random.normal(0.94, 0.48), - np.random.normal(0.56, 0.42), - np.random.normal(0.76, 0.14), - np.random.normal(0.32, 0.58), - np.random.normal(0.58, 0.219), - np.random.normal(0.14, 0.52), - np.random.normal(0.24, 0.42), - np.random.normal(0.34, 0.1), - np.random.normal(0.44, 0.9), - np.random.normal(0.54, 0.7), - np.random.normal(0.64, 0.5), - np.random.normal(0.74, 0.4))) - - # Execute the compiled Kernel. - results = k.execute(1, inputs) - - # Compute the reference results using the inference from SPFlow. - reference = log_likelihood(spn, inputs) - reference = reference.reshape(1) - - print(f"evaluation #{i}: result: {results}, reference: {reference}") - - # Compare computed results and reference to make sure the computation by the compiled Kernel is correct. - if not np.all(np.isclose(results, reference)): - raise RuntimeError("COMPUTATION FAILED: Results did not match reference!") + # Randomly sample input values from Gaussian (normal) distributions. + inputs = np.column_stack((np.random.normal(0.5, 1), + np.random.normal(0.125, 0.25), + np.random.normal(0.345, 0.24), + np.random.normal(0.456, 0.1), + np.random.normal(0.94, 0.48), + np.random.normal(0.56, 0.42), + np.random.normal(0.76, 0.14), + np.random.normal(0.32, 0.58), + np.random.normal(0.58, 0.219), + np.random.normal(0.14, 0.52), + np.random.normal(0.24, 0.42), + np.random.normal(0.34, 0.1), + np.random.normal(0.44, 0.9), + np.random.normal(0.54, 0.7), + np.random.normal(0.64, 0.5), + np.random.normal(0.74, 0.4))) + + # Execute the compiled Kernel. + results = k.execute(1, inputs) + + # Compute the reference results using the inference from SPFlow. + reference = log_likelihood(spn, inputs) + reference = reference.reshape(1) + + print(f"evaluation #{i}: result: {results}, reference: {reference}") + + # Compare computed results and reference to make sure the computation by the compiled Kernel is correct. + if not np.all(np.isclose(results, reference)): + raise RuntimeError("COMPUTATION FAILED: Results did not match reference!") print("COMPUTATION OK") From 5df1a76a39b2fcc353dfc388b6865c0d6b8772a8 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Thu, 29 Apr 2021 02:44:46 +0200 Subject: [PATCH 137/385] Fix: test case not set up correctly. --- .../test/test-slp-vectorized-escaping.py | 85 +++++++++++-------- 1 file changed, 50 insertions(+), 35 deletions(-) diff --git a/python-interface/test/test-slp-vectorized-escaping.py b/python-interface/test/test-slp-vectorized-escaping.py index 1c141e87..2ed8294d 100644 --- a/python-interface/test/test-slp-vectorized-escaping.py +++ b/python-interface/test/test-slp-vectorized-escaping.py @@ -25,23 +25,23 @@ g10 = Gaussian(mean=0.10, stdev=1, scope=10) g11 = Gaussian(mean=0.11, stdev=1, scope=11) -h0 = Histogram([0., 1., 2.], [0.1, 0.9], [1, 1], scope=0) -h1 = Histogram([0., 1., 2.], [0.2, 0.8], [1, 1], scope=1) -h2 = Histogram([0., 1., 2.], [0.3, 0.7], [1, 1], scope=2) -h3 = Histogram([0., 1., 2.], [0.4, 0.6], [1, 1], scope=3) -h4 = Histogram([0., 1., 2.], [0.5, 0.5], [1, 1], scope=4) -h5 = Histogram([0., 1., 2.], [0.6, 0.4], [1, 1], scope=5) -h6 = Histogram([0., 1., 2.], [0.7, 0.3], [1, 1], scope=6) -h7 = Histogram([0., 1., 2.], [0.8, 0.2], [1, 1], scope=7) - -c0 = Categorical(p=[0.1, 0.1, 0.8], scope=0) -c1 = Categorical(p=[0.2, 0.2, 0.6], scope=1) -c2 = Categorical(p=[0.3, 0.3, 0.4], scope=2) -c3 = Categorical(p=[0.4, 0.4, 0.2], scope=3) -c4 = Categorical(p=[0.5, 0.4, 0.1], scope=4) -c5 = Categorical(p=[0.6, 0.3, 0.1], scope=5) -c6 = Categorical(p=[0.7, 0.2, 0.1], scope=6) -c7 = Categorical(p=[0.8, 0.1, 0.1], scope=7) +h0 = Histogram([0., 1., 2.], [0.1, 0.9], [1, 1], scope=12) +h1 = Histogram([0., 1., 2.], [0.2, 0.8], [1, 1], scope=13) +h2 = Histogram([0., 1., 2.], [0.3, 0.7], [1, 1], scope=14) +h3 = Histogram([0., 1., 2.], [0.4, 0.6], [1, 1], scope=15) +h4 = Histogram([0., 1., 2.], [0.5, 0.5], [1, 1], scope=16) +h5 = Histogram([0., 1., 2.], [0.6, 0.4], [1, 1], scope=17) +h6 = Histogram([0., 1., 2.], [0.7, 0.3], [1, 1], scope=18) +h7 = Histogram([0., 1., 2.], [0.8, 0.2], [1, 1], scope=19) + +c0 = Categorical(p=[0.1, 0.1, 0.8], scope=20) +c1 = Categorical(p=[0.2, 0.2, 0.6], scope=21) +c2 = Categorical(p=[0.3, 0.3, 0.4], scope=22) +c3 = Categorical(p=[0.4, 0.4, 0.2], scope=23) +c4 = Categorical(p=[0.5, 0.4, 0.1], scope=24) +c5 = Categorical(p=[0.6, 0.3, 0.1], scope=25) +c6 = Categorical(p=[0.7, 0.2, 0.1], scope=26) +c7 = Categorical(p=[0.8, 0.1, 0.1], scope=27) s0 = Sum(children=[g8, h4], weights=[0.5, 0.5]) s1 = Sum(children=[g9, h5], weights=[0.5, 0.5]) @@ -112,26 +112,41 @@ for i in range(samples): # Randomly sample input values from Gaussian (normal) distributions. - inputs = np.column_stack((np.random.normal(0.5, 1), - np.random.normal(0.125, 0.25), - np.random.normal(0.345, 0.24), - np.random.normal(0.456, 0.1), - np.random.normal(0.94, 0.48), - np.random.normal(0.56, 0.42), - np.random.normal(0.76, 0.14), - np.random.normal(0.32, 0.58), - np.random.normal(0.58, 0.219), - np.random.normal(0.14, 0.52), - np.random.normal(0.24, 0.42), - np.random.normal(0.34, 0.1), - np.random.normal(0.44, 0.9), - np.random.normal(0.54, 0.7), - np.random.normal(0.64, 0.5), - np.random.normal(0.74, 0.4))) + inputs = np.column_stack(( + # gaussian + np.random.normal(0.5, 1), + np.random.normal(0.125, 0.25), + np.random.normal(0.345, 0.24), + np.random.normal(0.456, 0.1), + np.random.normal(0.94, 0.48), + np.random.normal(0.56, 0.42), + np.random.normal(0.76, 0.14), + np.random.normal(0.32, 0.58), + np.random.normal(0.58, 0.219), + np.random.normal(0.14, 0.52), + np.random.normal(0.24, 0.42), + np.random.normal(0.34, 0.1), + # histogram + np.random.randint(2, size=1), + np.random.randint(2, size=1), + np.random.randint(2, size=1), + np.random.randint(2, size=1), + np.random.randint(2, size=1), + np.random.randint(2, size=1), + np.random.randint(2, size=1), + np.random.randint(2, size=1), + # categorical + np.random.randint(3, size=1), + np.random.randint(3, size=1), + np.random.randint(3, size=1), + np.random.randint(3, size=1), + np.random.randint(3, size=1), + np.random.randint(3, size=1), + np.random.randint(3, size=1), + np.random.randint(3, size=1))).astype("float64") # Execute the compiled Kernel. results = k.execute(1, inputs) - # Compute the reference results using the inference from SPFlow. reference = log_likelihood(spn, inputs) reference = reference.reshape(1) @@ -140,7 +155,7 @@ # Compare computed results and reference to make sure the computation by the compiled Kernel is correct. if not np.all(np.isclose(results, reference)): - raise RuntimeError("COMPUTATION FAILED: Results did not match reference!") + raise RuntimeError(f"COMPUTATION FAILED: Results did not match reference! (input: {inputs})") print("COMPUTATION OK") From c2efd45b9ce1e9f05284431fbc8b61a5cc800e82 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 2 May 2021 03:02:10 +0200 Subject: [PATCH 138/385] Fix: chaotic insertion of vectorized instructions. --- .../Vectorization/SLP/SLPConversion.h | 23 ++++---- .../Vectorization/SLP/SLPConversion.cpp | 57 ++++++++++++------- .../SLP/SLPVectorizationPatterns.cpp | 38 ++++++------- .../VectorizeStructurePatterns.cpp | 54 +++++++++++++----- 4 files changed, 108 insertions(+), 64 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h index 32914a2e..384f8325 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h @@ -13,12 +13,11 @@ namespace mlir { namespace low { namespace slp { - enum CreationMode { - Default, - Constant, - Splat, - BroadcastInsert, - ConsecutiveLoad, + enum ElementFlag { + KeepNone, + KeepFirst, + KeepAll, + NoExtract, Skip }; @@ -29,16 +28,18 @@ namespace mlir { explicit ConversionManager(SLPNode* root); Value getInsertionPoint(NodeVector* vector) const; - bool isConverted(NodeVector* vector) const; + bool wasConverted(NodeVector* vector) const; - void update(NodeVector* vector, Value const& operation, CreationMode const& mode); + void update(NodeVector* vector, Value const& operation, ElementFlag const& flag); void markSkipped(NodeVector* vector); Value getValue(NodeVector* vector) const; - CreationMode getCreationMode(NodeVector* vector) const; + ElementFlag getElementFlag(NodeVector* vector) const; bool hasEscapingUsers(Value const& value) const; - Operation* moveEscapingUsersBehind(NodeVector* vector) const; + /// Recursively moves all users (and their users and so on) of the vector behind it. + void recursivelyMoveUsersAfter(NodeVector* vector) const; + Operation* getEarliestEscapingUser(Value const& value) const; private: @@ -46,7 +47,7 @@ namespace mlir { /// The operation that was created for this node vector. Optional operation{None}; /// The way it was created. - Optional mode{None}; + Optional flag{None}; }; DenseMap vectorData; diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp index 62253c1a..a5e8c041 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp @@ -68,7 +68,7 @@ Value ConversionManager::getInsertionPoint(NodeVector* vector) const { Value latestVal = latestInsertion; for (size_t i = 0; i < vector->numOperands(); ++i) { auto* operand = vector->getOperand(i); - if (vectorData.lookup(operand).mode == CreationMode::Skip) { + if (vectorData.lookup(operand).flag == ElementFlag::Skip) { continue; } assert(vectorData.lookup(operand).operation.hasValue() && "operand has not yet been converted"); @@ -83,67 +83,84 @@ Value ConversionManager::getInsertionPoint(NodeVector* vector) const { return latestElement(vector); } -void ConversionManager::update(NodeVector* vector, Value const& operation, CreationMode const& mode) { - assert(!vectorData[vector].operation.hasValue() && !vectorData[vector].mode.hasValue() +void ConversionManager::update(NodeVector* vector, Value const& operation, ElementFlag const& flag) { + assert(!vectorData[vector].operation.hasValue() && !vectorData[vector].flag.hasValue() && "vector has been converted already"); vectorData[vector].operation = operation; - vectorData[vector].mode = mode; + vectorData[vector].flag = flag; if (!latestInsertion || isBeforeInBlock(latestInsertion, operation)) { latestInsertion = operation; } } void ConversionManager::markSkipped(NodeVector* vector) { - assert(!vectorData[vector].operation.hasValue() && !vectorData[vector].mode.hasValue() + assert(!vectorData[vector].operation.hasValue() && !vectorData[vector].flag.hasValue() && "vector has been converted already"); - vectorData[vector].mode = CreationMode::Skip; + vectorData[vector].flag = ElementFlag::Skip; } -bool ConversionManager::isConverted(NodeVector* vector) const { +bool ConversionManager::wasConverted(NodeVector* vector) const { return vectorData.lookup(vector).operation.hasValue(); } Value ConversionManager::getValue(NodeVector* vector) const { - assert(isConverted(vector) && "vector has not yet been converted"); + assert(wasConverted(vector) && "vector has not yet been converted"); return vectorData.lookup(vector).operation.getValue(); } -CreationMode ConversionManager::getCreationMode(NodeVector* vector) const { - assert(vectorData.lookup(vector).mode.hasValue() && "vector has not yet been converted"); - return vectorData.lookup(vector).mode.getValue(); +ElementFlag ConversionManager::getElementFlag(NodeVector* vector) const { + assert(vectorData.lookup(vector).flag.hasValue() && "vector has not yet been converted"); + return vectorData.lookup(vector).flag.getValue(); } bool ConversionManager::hasEscapingUsers(Value const& value) const { return escapingUsers.count(value) && !escapingUsers.lookup(value).empty(); } -Operation* ConversionManager::moveEscapingUsersBehind(NodeVector* vector) const { - assert(isConverted(vector) && "vector has not yet been converted"); +void ConversionManager::recursivelyMoveUsersAfter(NodeVector* vector) const { + assert(wasConverted(vector) && "vector has not yet been converted"); + Operation* vectorOp = vectorData.lookup(vector).operation->getDefiningOp(); SmallVector users; - Operation* earliestEscapingUser = nullptr; for (size_t lane = 0; lane < vector->numLanes(); ++lane) { auto const& element = vector->getElement(lane); for (auto* user : escapingUsers.lookup(element)) { - if (!earliestEscapingUser) { - earliestEscapingUser = user; + if (user->isBeforeInBlock(vectorOp)) { + users.emplace_back(user); } - users.emplace_back(user); } } for (size_t i = 0; i < users.size(); ++i) { - users.append(std::begin(users[i]->getUsers()), std::end(users[i]->getUsers())); + for (auto* user : users[i]->getUsers()) { + if (user->isBeforeInBlock(vectorOp)) { + users.emplace_back(user); + } + } } std::sort(std::begin(users), std::end(users), [&](Operation* lhs, Operation* rhs) { return lhs->isBeforeInBlock(rhs); }); users.erase(std::unique(std::begin(users), std::end(users)), std::end(users)); - Operation* latestUser = vectorData.lookup(vector).operation->getDefiningOp(); + Operation* latestUser = vectorOp; for (size_t i = 0; i < users.size(); ++i) { if (users[i]->isBeforeInBlock(latestUser)) { users[i]->moveAfter(latestUser); } latestUser = users[i]; } - return earliestEscapingUser; +} + +Operation* ConversionManager::getEarliestEscapingUser(Value const& value) const { + assert(hasEscapingUsers(value) && "value does not have any escaping users"); + Operation* earliestUser = nullptr; + for (auto* user : escapingUsers.lookup(value)) { + if (!earliestUser) { + earliestUser = user; + continue; + } + if (user->isBeforeInBlock(earliestUser)) { + earliestUser = user; + } + } + return earliestUser; } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp index 8c44cc1d..95553de1 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp @@ -55,7 +55,7 @@ namespace { LogicalResult VectorizeConstant::matchAndRewrite(ConstantOp constantOp, PatternRewriter& rewriter) const { - assert(!conversionManager.isConverted(vector) && "vector has already been created"); + assert(!conversionManager.wasConverted(vector) && "vector has already been created"); auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), constantOp.getType()); rewriter.setInsertionPointAfterValue(conversionManager.getInsertionPoint(vector)); @@ -67,14 +67,14 @@ LogicalResult VectorizeConstant::matchAndRewrite(ConstantOp constantOp, PatternR auto const& elements = denseFloatingPoints(std::begin(constants), std::end(constants), vectorType); auto constVector = rewriter.create(constantOp.getLoc(), elements); - conversionManager.update(vector, constVector, CreationMode::Constant); + conversionManager.update(vector, constVector, ElementFlag::NoExtract); return success(); } LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead batchReadOp, PatternRewriter& rewriter) const { - assert(!conversionManager.isConverted(vector) && "vector has already been created"); + assert(!conversionManager.wasConverted(vector) && "vector has already been created"); auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), batchReadOp.getType()); rewriter.setInsertionPointAfterValue(conversionManager.getInsertionPoint(vector)); @@ -82,86 +82,86 @@ LogicalResult VectorizeBatchRead::matchAndRewrite(SPNBatchRead batchReadOp, Patt if (vector->splattable()) { auto const& element = vector->getElement(0); auto vectorOperation = rewriter.create(batchReadOp.getLoc(), vectorType, element); - conversionManager.update(vector, vectorOperation, CreationMode::Splat); + conversionManager.update(vector, vectorOperation, ElementFlag::KeepFirst); } else { auto vectorOperation = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); - conversionManager.update(vector, vectorOperation, CreationMode::BroadcastInsert); + conversionManager.update(vector, vectorOperation, ElementFlag::KeepAll); } } else { auto batchReadLoc = batchReadOp.getLoc(); auto memIndex = rewriter.create(batchReadLoc, rewriter.getIndexAttr(batchReadOp.sampleIndex())); ValueRange indices{batchReadOp.batchIndex(), memIndex}; auto vectorLoad = rewriter.create(batchReadLoc, vectorType, batchReadOp.batchMem(), indices); - conversionManager.update(vector, vectorLoad, CreationMode::ConsecutiveLoad); + conversionManager.update(vector, vectorLoad, ElementFlag::KeepNone); } - assert(conversionManager.isConverted(vector)); + assert(conversionManager.wasConverted(vector)); return success(); } LogicalResult VectorizeAdd::matchAndRewrite(SPNAdd addOp, PatternRewriter& rewriter) const { - assert(!conversionManager.isConverted(vector) && "vector has already been created"); + assert(!conversionManager.wasConverted(vector) && "vector has already been created"); auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), addOp.getType()); rewriter.setInsertionPointAfterValue(conversionManager.getInsertionPoint(vector)); if (vector->isLeaf()) { assert(!vector->splattable() && "addition vector should not be a leaf vector if it is splattable"); auto vectorOperation = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); - conversionManager.update(vector, vectorOperation, CreationMode::BroadcastInsert); + conversionManager.update(vector, vectorOperation, ElementFlag::KeepAll); return success(); } SmallVector operands; for (unsigned i = 0; i < addOp.getNumOperands(); ++i) { auto* operand = vector->getOperand(i); - assert(conversionManager.isConverted(operand) && "operand has not yet been vectorized"); + assert(conversionManager.wasConverted(operand) && "operand has not yet been vectorized"); operands.emplace_back(conversionManager.getValue(operand)); } auto vectorAdd = rewriter.create(addOp.getLoc(), vectorType, operands); - conversionManager.update(vector, vectorAdd, CreationMode::Default); + conversionManager.update(vector, vectorAdd, ElementFlag::KeepNone); return success(); } LogicalResult VectorizeMul::matchAndRewrite(SPNMul mulOp, PatternRewriter& rewriter) const { - assert(!conversionManager.isConverted(vector) && "vector has already been created"); + assert(!conversionManager.wasConverted(vector) && "vector has already been created"); auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), mulOp.getType()); rewriter.setInsertionPointAfterValue(conversionManager.getInsertionPoint(vector)); if (vector->isLeaf()) { assert(!vector->splattable() && "multiplication vector should not be a leaf vector if it is splattable"); auto vectorOperation = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); - conversionManager.update(vector, vectorOperation, CreationMode::BroadcastInsert); + conversionManager.update(vector, vectorOperation, ElementFlag::KeepAll); return success(); } SmallVector operands; for (unsigned i = 0; i < mulOp.getNumOperands(); ++i) { auto* operand = vector->getOperand(i); - assert(conversionManager.isConverted(operand) && "operand has not yet been vectorized"); + assert(conversionManager.wasConverted(operand) && "operand has not yet been vectorized"); operands.emplace_back(conversionManager.getValue(operand)); } auto vectorAdd = rewriter.create(mulOp.getLoc(), vectorType, operands); - conversionManager.update(vector, vectorAdd, CreationMode::Default); + conversionManager.update(vector, vectorAdd, ElementFlag::KeepNone); return success(); } LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, PatternRewriter& rewriter) const { - assert(!conversionManager.isConverted(vector) && "vector has already been created"); + assert(!conversionManager.wasConverted(vector) && "vector has already been created"); auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), gaussianOp.getType()); rewriter.setInsertionPointAfterValue(conversionManager.getInsertionPoint(vector)); if (vector->isLeaf()) { assert(!vector->splattable() && "gaussian vector should not be a leaf vector if it is splattable"); auto vectorOperation = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); - conversionManager.update(vector, vectorOperation, CreationMode::BroadcastInsert); + conversionManager.update(vector, vectorOperation, ElementFlag::KeepAll); return success(); } @@ -197,7 +197,7 @@ LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, Pat auto const& stddevs = denseFloatingPoints(std::begin(stddevAttributes), std::end(stddevAttributes), vectorType); // Grab the input vector. - assert(conversionManager.isConverted(vector->getOperand(0)) && "input vector has not yet been vectorized"); + assert(conversionManager.wasConverted(vector->getOperand(0)) && "input vector has not yet been vectorized"); Value const& inputVector = conversionManager.getValue(vector->getOperand(0)); // Calculate Gaussian distribution using e^(-0.5 * ((x - mean) / stddev)^2)) / (stddev * sqrt(2 * PI)) @@ -221,7 +221,7 @@ LogicalResult VectorizeGaussian::matchAndRewrite(SPNGaussianLeaf gaussianOp, Pat auto coefficientVector = rewriter.create(gaussianLoc, coefficients); gaussianVector = rewriter.create(gaussianLoc, coefficientVector, gaussianVector); - conversionManager.update(vector, gaussianVector, CreationMode::Default); + conversionManager.update(vector, gaussianVector, ElementFlag::KeepNone); return success(); } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp index fd9f6c1f..96998c9d 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp @@ -145,25 +145,50 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, // Also traverse nodes in postorder to properly handle multinodes. for (size_t vectorIndex = node->numVectors(); vectorIndex-- > 0;) { vector = node->getVector(vectorIndex); + // This assumes that node vectors using these illegal vectors as operands don't need them. For example, + // SPNBatchReads being replaced with vector::LoadOps or 'broadcastInserts' instead of using their + // vectorized operands. + if (wouldBeIllegal(vector)) { + conversionManager.markSkipped(vector); + continue; + } // dumpSLPNodeVector(*vector); auto* vectorOp = vector->begin()->getDefiningOp(); if (vector->containsBlockArgs() || failed(applicator.matchAndRewrite(vectorOp, rewriter))) { - // This assumes that the node vectors using these illegal vectors know what they're doing and don't need them. - // For example, SPNBatchReads being replaced with vector::LoadOps or broadcastInserts instead of using their - // vectorized operands. - if (wouldBeIllegal(vector)) { - conversionManager.markSkipped(vector); - continue; - } + auto const& vectorType = VectorType::get(static_cast(vector->numLanes()), computationType); rewriter.setInsertionPointAfterValue(conversionManager.getInsertionPoint(vector)); + + // Create extractions from vectorized operands if present. + for (size_t lane = 0; lane < vector->numLanes(); ++lane) { + auto const& element = vector->getElement(lane); + if (auto* elementOp = element.getDefiningOp()) { + for (size_t i = 0; i < vector->numOperands(); ++i) { + auto* operand = vector->getOperand(i); + // E.g. illegal operand vectors. + if (!conversionManager.wasConverted(operand)) { + continue; + } + auto const& source = conversionManager.getValue(operand); + auto extractOp = rewriter.create(element.getLoc(), source, lane); + elementOp->setOperand(i, extractOp.result()); + if (elementOp->isBeforeInBlock(extractOp)) { + elementOp->moveAfter(extractOp); + rewriter.setInsertionPointAfter(elementOp); + } + } + } + if (lane == 0 && vector->splattable()) { + break; + } + } if (vector->splattable()) { auto const& element = vector->getElement(0); auto vectorizedOp = rewriter.create(element.getLoc(), vectorType, element); - conversionManager.update(vector, vectorizedOp, CreationMode::Splat); + conversionManager.update(vector, vectorizedOp, ElementFlag::KeepFirst); } else { auto vectorizedOp = broadcastFirstInsertRest(vector->begin(), vector->end(), vectorType, rewriter); - conversionManager.update(vector, vectorizedOp, CreationMode::BroadcastInsert); + conversionManager.update(vector, vectorizedOp, ElementFlag::KeepAll); } } } @@ -171,25 +196,26 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, // Create vector extractions for escaping uses & erase superfluous operations. for (size_t vectorIndex = node->numVectors(); vectorIndex-- > 0;) { vector = node->getVector(vectorIndex); - auto const& creationMode = conversionManager.getCreationMode(vector); - if (creationMode == CreationMode::Skip) { + auto const& creationMode = conversionManager.getElementFlag(vector); + if (creationMode == ElementFlag::Skip) { continue; } + conversionManager.recursivelyMoveUsersAfter(vector); for (size_t lane = 0; lane < vector->numLanes(); ++lane) { auto const& element = vector->getElement(lane); if (finishedValues.contains(element)) { continue; } finishedValues.insert(element); - if (creationMode == CreationMode::BroadcastInsert || (creationMode == CreationMode::Splat && lane == 0)) { + if (creationMode == ElementFlag::KeepAll || (creationMode == ElementFlag::KeepFirst && lane == 0)) { continue; } if (conversionManager.hasEscapingUsers(element)) { - if (creationMode == CreationMode::Constant) { + if (creationMode == ElementFlag::NoExtract) { continue; } + rewriter.setInsertionPoint(conversionManager.getEarliestEscapingUser(element)); auto const& source = conversionManager.getValue(vector); - rewriter.setInsertionPoint(conversionManager.moveEscapingUsersBehind(vector)); auto extractOp = rewriter.create(element.getLoc(), source, lane); element.replaceAllUsesWith(extractOp.result()); } From 9ea055ff1eab5a4726e59ffd7fd277df5a69c437 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 2 May 2021 03:55:44 +0200 Subject: [PATCH 139/385] Added speaker test case. --- .../test/test-slp-vectorized-speaker.py | 92 +++++++++++++++++++ 1 file changed, 92 insertions(+) create mode 100644 python-interface/test/test-slp-vectorized-speaker.py diff --git a/python-interface/test/test-slp-vectorized-speaker.py b/python-interface/test/test-slp-vectorized-speaker.py new file mode 100644 index 00000000..123f8cc9 --- /dev/null +++ b/python-interface/test/test-slp-vectorized-speaker.py @@ -0,0 +1,92 @@ +import numpy as np +import os +import spncpy as spnc +import tempfile +from datetime import datetime +from spn.algorithms.Inference import log_likelihood +from xspn.serialization.binary.BinarySerialization import BinaryDeserializer, BinarySerializer + +# Read the trained SPN from file +query = BinaryDeserializer(os.path.join(os.path.dirname(__file__), "..", "..", "mlir", "test", "test-resources", + "speaker_FADG0.bin")).deserialize_from_file() + +batchsize = 1 + +print("feature type:", query.graph.featureType) +print("saved batch size:", query.batchSize) +print("desired batch size:", batchsize) +query.batchSize = batchsize + +# Construct a temporary file that we will use to serialize the SPN to. +timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") +tempfile = os.path.join(tempfile.gettempdir(), f"spn_{timestamp}.bin") + +# Serialize the SPN to binary format as input to the compiler. +print("Serializing SPN to file...") +BinarySerializer(tempfile).serialize_to_file(query) +# Check that the serialization worked. +if not os.path.isfile(tempfile): + raise RuntimeError("Serialization of the SPN failed") + +# Invoke the compiler. +print("Invoking compiler...") +compiler = spnc.SPNCompiler() +# Compile the query into a Kernel. +options = dict({"target": "CPU", "cpu-vectorize": "true"}) +k = compiler.compileQuery(tempfile, options) +# Check that the comiled Kernel actually exists. +if not os.path.isfile(k.fileName()): + raise RuntimeError("Compilation failed, not Kernel produced") + +samples = 50 + +for i in range(samples): + + # Randomly sample input values from Gaussian (normal) distributions. + inputs = np.column_stack(( + # 26 gaussian inputs + np.random.normal(0.01, 1.00), + np.random.normal(0.02, 0.90), + np.random.normal(0.03, 0.80), + np.random.normal(0.04, 0.70), + np.random.normal(0.05, 0.60), + np.random.normal(0.06, 0.50), + np.random.normal(0.07, 0.40), + np.random.normal(0.08, 0.30), + np.random.normal(0.09, 0.20), + np.random.normal(0.10, 0.10), + np.random.normal(0.11, 1.00), + np.random.normal(0.12, 0.90), + np.random.normal(0.13, 0.80), + np.random.normal(0.14, 0.70), + np.random.normal(0.15, 0.60), + np.random.normal(0.16, 0.50), + np.random.normal(0.17, 0.40), + np.random.normal(0.18, 0.30), + np.random.normal(0.19, 0.20), + np.random.normal(0.20, 0.10), + np.random.normal(0.21, 1.00), + np.random.normal(0.22, 0.90), + np.random.normal(0.23, 0.80), + np.random.normal(0.24, 0.70), + np.random.normal(0.25, 0.60), + np.random.normal(0.26, 0.50), + )).astype("float64") + + # Execute the compiled Kernel. + results = k.execute(1, inputs) + # Compute the reference results using the inference from SPFlow. + reference = log_likelihood(query.graph.root, inputs) + reference = reference.reshape(1) + + print(f"evaluation #{i}: result: {results}, reference: {reference}") + + # Compare computed results and reference to make sure the computation by the compiled Kernel is correct. + if not np.all(np.isclose(results, reference)): + raise RuntimeError(f"COMPUTATION FAILED: Results did not match reference! (input: {inputs})") + +print("COMPUTATION OK") + +# Remove the serialized SPN file and the compiled Kernel. +os.remove(tempfile) +# os.remove(k.fileName()) From 004f9b6ca4f0171e61a489bfcd243e212a368f76 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 2 May 2021 05:07:00 +0200 Subject: [PATCH 140/385] Formatting. --- .../HiSPNtoLoSPN/ArithmeticPrecisionAnalysis.h | 2 +- mlir/include/Conversion/HiSPNtoLoSPN/NodePatterns.h | 2 +- mlir/include/Conversion/HiSPNtoLoSPN/QueryPatterns.h | 2 +- mlir/include/Conversion/LoSPNtoCPU/NodePatterns.h | 2 +- .../LoSPNtoCPU/Vectorization/VectorizationPatterns.h | 4 ++-- mlir/lib/Conversion/HiSPNtoLoSPN/NodePatterns.cpp | 2 +- mlir/lib/Conversion/LoSPNtoCPU/NodePatterns.cpp | 2 +- .../LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp | 12 ++++++++++++ .../LoSPN/Bufferize/LoSPNBufferizationPatterns.h | 2 +- .../LoSPN/Passes/LoSPNGraphStatsCollection.cpp | 4 ++-- 10 files changed, 23 insertions(+), 11 deletions(-) diff --git a/mlir/include/Conversion/HiSPNtoLoSPN/ArithmeticPrecisionAnalysis.h b/mlir/include/Conversion/HiSPNtoLoSPN/ArithmeticPrecisionAnalysis.h index 0311df05..c29cd643 100644 --- a/mlir/include/Conversion/HiSPNtoLoSPN/ArithmeticPrecisionAnalysis.h +++ b/mlir/include/Conversion/HiSPNtoLoSPN/ArithmeticPrecisionAnalysis.h @@ -136,7 +136,7 @@ namespace mlir { /// \param isSum Boolean If set to true will treat operands as addends, otherwise as multiplicands. /// \return SmallVector with a single element, representing the whole Sum or Product. llvm::SmallVector - estimateErrorBinaryOperation(SmallVector operands, bool isSum); + estimateErrorBinaryOperation(SmallVector operands, bool isSum); /// Estimate the error introduced by the given (weighted) addition operation w.r.t. the current format. /// \param op Pointer to the defining operation, representing a SPN node. diff --git a/mlir/include/Conversion/HiSPNtoLoSPN/NodePatterns.h b/mlir/include/Conversion/HiSPNtoLoSPN/NodePatterns.h index 7470e017..5b766907 100644 --- a/mlir/include/Conversion/HiSPNtoLoSPN/NodePatterns.h +++ b/mlir/include/Conversion/HiSPNtoLoSPN/NodePatterns.h @@ -119,7 +119,7 @@ namespace mlir { }; static inline void populateHiSPNtoLoSPNNodePatterns(OwningRewritePatternList& patterns, MLIRContext* context, - TypeConverter& typeConverter) { + TypeConverter& typeConverter) { patterns.insert(typeConverter, context); patterns.insert(typeConverter, context); patterns.insert(typeConverter, context); diff --git a/mlir/include/Conversion/HiSPNtoLoSPN/QueryPatterns.h b/mlir/include/Conversion/HiSPNtoLoSPN/QueryPatterns.h index b734d6e9..308c62cf 100644 --- a/mlir/include/Conversion/HiSPNtoLoSPN/QueryPatterns.h +++ b/mlir/include/Conversion/HiSPNtoLoSPN/QueryPatterns.h @@ -28,7 +28,7 @@ namespace mlir { }; static inline void populateHiSPNtoLoSPNQueryPatterns(OwningRewritePatternList& patterns, MLIRContext* context, - TypeConverter& typeConverter) { + TypeConverter& typeConverter) { patterns.insert(typeConverter, context); } diff --git a/mlir/include/Conversion/LoSPNtoCPU/NodePatterns.h b/mlir/include/Conversion/LoSPNtoCPU/NodePatterns.h index c588707d..2bb3257f 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/NodePatterns.h +++ b/mlir/include/Conversion/LoSPNtoCPU/NodePatterns.h @@ -165,7 +165,7 @@ namespace mlir { }; static inline void populateLoSPNtoCPUNodePatterns(OwningRewritePatternList& patterns, MLIRContext* context, - TypeConverter& typeConverter) { + TypeConverter& typeConverter) { patterns.insert(typeConverter, context); patterns.insert(typeConverter, context); patterns.insert(typeConverter, context); diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/VectorizationPatterns.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/VectorizationPatterns.h index b0b7f76d..925bfe7e 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/VectorizationPatterns.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/VectorizationPatterns.h @@ -66,8 +66,8 @@ namespace mlir { }; static inline void populateLoSPNtoCPUVectorizationTaskPatterns(OwningRewritePatternList& patterns, - MLIRContext* context, - TypeConverter& typeConverter) { + MLIRContext* context, + TypeConverter& typeConverter) { patterns.insert(typeConverter, context, 5); } diff --git a/mlir/lib/Conversion/HiSPNtoLoSPN/NodePatterns.cpp b/mlir/lib/Conversion/HiSPNtoLoSPN/NodePatterns.cpp index 821d8c98..0fe4b575 100644 --- a/mlir/lib/Conversion/HiSPNtoLoSPN/NodePatterns.cpp +++ b/mlir/lib/Conversion/HiSPNtoLoSPN/NodePatterns.cpp @@ -145,7 +145,7 @@ LogicalResult RootNodeLowering::matchAndRewriteChecked(high::RootNode op, if (!isLogType(result.getType())) { // Insert a conversion to log before returning the result. // Currently always uses F64 type to represent log results. - result = rewriter.create(op->getLoc(),operands[0].getType(), result); + result = rewriter.create(op->getLoc(), operands[0].getType(), result); } rewriter.replaceOpWithNewOp(op, result); return success(); diff --git a/mlir/lib/Conversion/LoSPNtoCPU/NodePatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/NodePatterns.cpp index 47c96ed3..4174243c 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/NodePatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/NodePatterns.cpp @@ -342,7 +342,7 @@ namespace { auto visibility = rewriter.getStringAttr("private"); auto memrefType = mlir::MemRefType::get({(long) arrayValues.size()}, resultType); (void) rewriter.create(op.getLoc(), symbolName, visibility, - mlir::TypeAttr::get(memrefType), valArrayAttr, true); + mlir::TypeAttr::get(memrefType), valArrayAttr, true); // Restore insertion point rewriter.restoreInsertionPoint(restore); diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp index 7ca8d8e3..33001065 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp @@ -69,6 +69,11 @@ namespace { return lhsOp->getName().getStringRef() < rhsOp->getName().getStringRef(); }); } + + SmallVector sortedByLoc() { + return SmallVector{}; + } + } // end namespace void SLPGraphBuilder::buildGraph(NodeVector* vector, SLPNode* currentNode) const { @@ -86,7 +91,14 @@ void SLPGraphBuilder::buildGraph(NodeVector* vector, SLPNode* currentNode) const for (auto& operands : allOperands) { sortByOpcode(operands, currentOpCode); } + // Determine lanes whose elements aren't allowed to be reordered. This is the case if they have escaping uses. + // DenseMap> lockedLanes; for (unsigned i = 0; i < arity; ++i) { + bool canAppendToNode = true; + // for(size_t n = 0; n < allOperands.size(); ++n) { + // SmallVector const& operands = allOperands[n]; + // for(size_t ) + // } if (std::all_of(std::begin(allOperands), std::end(allOperands), [&](SmallVector const& operands) { return operands[i].getDefiningOp()->getName() == currentOpCode && !escapesMultinode(operands[i], currentNode); })) { diff --git a/mlir/lib/Dialect/LoSPN/Bufferize/LoSPNBufferizationPatterns.h b/mlir/lib/Dialect/LoSPN/Bufferize/LoSPNBufferizationPatterns.h index 05496873..6d26bb68 100644 --- a/mlir/lib/Dialect/LoSPN/Bufferize/LoSPNBufferizationPatterns.h +++ b/mlir/lib/Dialect/LoSPN/Bufferize/LoSPNBufferizationPatterns.h @@ -47,7 +47,7 @@ namespace mlir { }; static inline void populateLoSPNBufferizationPatterns(OwningRewritePatternList& patterns, MLIRContext* context, - TypeConverter& typeConverter) { + TypeConverter& typeConverter) { patterns.insert(typeConverter, context); patterns.insert(typeConverter, context); } diff --git a/mlir/lib/Dialect/LoSPN/Passes/LoSPNGraphStatsCollection.cpp b/mlir/lib/Dialect/LoSPN/Passes/LoSPNGraphStatsCollection.cpp index 51d43546..f45f1e43 100644 --- a/mlir/lib/Dialect/LoSPN/Passes/LoSPNGraphStatsCollection.cpp +++ b/mlir/lib/Dialect/LoSPN/Passes/LoSPNGraphStatsCollection.cpp @@ -22,8 +22,8 @@ namespace { struct LoSPNGraphStatsCollection : public PassWrapper> { public: - explicit LoSPNGraphStatsCollection(std::string graphStatsFilename) : - graphStatsFile{std::move(graphStatsFilename)} {} + explicit LoSPNGraphStatsCollection(std::string graphStatsFilename) : + graphStatsFile{std::move(graphStatsFilename)} {} protected: void runOnOperation() override { From 33315ef416e7f3cbce4c0b6ab06ff14405d0692a Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Tue, 4 May 2021 06:13:08 +0200 Subject: [PATCH 141/385] Add tree & re-use functionality to graph builder. --- .../Vectorization/SLP/SLPGraphBuilder.h | 14 +++-- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.h | 4 +- .../Vectorization/SLP/SLPGraphBuilder.cpp | 63 +++++++++++++++---- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp | 23 ++++--- .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp | 1 - 5 files changed, 76 insertions(+), 29 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h index 94b02f32..6963a707 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h @@ -21,7 +21,7 @@ namespace mlir { explicit SLPGraphBuilder(size_t maxLookAhead); - std::unique_ptr build(ArrayRef const& seed) const; + std::shared_ptr build(ArrayRef const& seed); private: @@ -37,17 +37,19 @@ namespace mlir { // vectorization has failed, give higher priority to others FAILED }; - static Mode modeFromValue(Value const& value); - - void buildGraph(NodeVector* vector, SLPNode* currentNode) const; + void buildGraph(NodeVector* vector, SLPNode* currentNode); void reorderOperands(SLPNode* multinode) const; - std::pair getBest(Mode const& mode, Value const& last, SmallVector& candidates) const; - int getLookAheadScore(Value const& last, Value const& candidate, unsigned maxLevel) const; + static Mode modeFromValue(Value const& value); + std::pair, size_t> getOrCreateOperand(ArrayRef values, + bool* isNewOperand = nullptr); + size_t const maxLookAhead; + SmallVector> nodes; + SmallPtrSet reorderWorklist; }; } diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h index f2bf71f1..587e50ea 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h @@ -65,7 +65,7 @@ namespace mlir { NodeVector* addVector(ArrayRef const& operations); NodeVector* getVector(size_t index) const; - SLPNode* addOperand(ArrayRef const& values, NodeVector* definingVector); + void addOperand(std::shared_ptr operandNode, size_t vectorIndex, NodeVector* definingVector); SLPNode* getOperand(size_t index) const; std::vector getOperands() const; size_t numOperands() const; @@ -74,7 +74,7 @@ namespace mlir { private: SmallVector> vectors; - SmallVector> operandNodes; + SmallVector> operandNodes; }; } } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp index 33001065..eadc616a 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp @@ -13,8 +13,10 @@ using namespace mlir::spn::low::slp; SLPGraphBuilder::SLPGraphBuilder(size_t maxLookAhead) : maxLookAhead{maxLookAhead} {} -std::unique_ptr SLPGraphBuilder::build(ArrayRef const& seed) const { - auto root = std::make_unique(SLPNode{seed}); +std::shared_ptr SLPGraphBuilder::build(ArrayRef const& seed) { + auto root = std::make_shared(seed); + nodes.emplace_back(root); + reorderWorklist.insert(root.get()); buildGraph(root->getVector(0), root.get()); return root; } @@ -70,13 +72,9 @@ namespace { }); } - SmallVector sortedByLoc() { - return SmallVector{}; - } - } // end namespace -void SLPGraphBuilder::buildGraph(NodeVector* vector, SLPNode* currentNode) const { +void SLPGraphBuilder::buildGraph(NodeVector* vector, SLPNode* currentNode) { // Stop growing graph if (!vectorizable(vector->begin(), vector->end())) { return; @@ -94,7 +92,7 @@ void SLPGraphBuilder::buildGraph(NodeVector* vector, SLPNode* currentNode) const // Determine lanes whose elements aren't allowed to be reordered. This is the case if they have escaping uses. // DenseMap> lockedLanes; for (unsigned i = 0; i < arity; ++i) { - bool canAppendToNode = true; + // bool canAppendToNode = true; // for(size_t n = 0; n < allOperands.size(); ++n) { // SmallVector const& operands = allOperands[n]; // for(size_t ) @@ -114,12 +112,15 @@ void SLPGraphBuilder::buildGraph(NodeVector* vector, SLPNode* currentNode) const for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { operandValues.emplace_back(allOperands[lane][i]); } - currentNode->addOperand(operandValues, vector); + auto operandAndVector = getOrCreateOperand(operandValues); + currentNode->addOperand(operandAndVector.first, operandAndVector.second, vector); } } // B. Normal Mode: Finished building multi-node if (currentNode->isRootOfNode(*vector)) { - reorderOperands(currentNode); + if (reorderWorklist.erase(currentNode)) { + reorderOperands(currentNode); + } for (auto* operandNode : currentNode->getOperands()) { buildGraph(operandNode->getVector(operandNode->numVectors() - 1), operandNode); } @@ -133,14 +134,20 @@ void SLPGraphBuilder::buildGraph(NodeVector* vector, SLPNode* currentNode) const auto operand = currentNode->getValue(lane, 0).getDefiningOp()->getOperand(i); operandValues.emplace_back(operand); } - auto* operandNode = currentNode->addOperand(operandValues, vector); - buildGraph(operandNode->getVector(0), operandNode); + bool isNewOperand = false; + auto operandAndVector = getOrCreateOperand(operandValues, &isNewOperand); + currentNode->addOperand(operandAndVector.first, operandAndVector.second, vector); + if (isNewOperand) { + buildGraph(operandAndVector.first->getVector(0), operandAndVector.first.get()); + } } } } void SLPGraphBuilder::reorderOperands(SLPNode* multinode) const { auto const& numOperands = multinode->numOperands(); + llvm::dbgs() << "Reordering multinode " << multinode << " with " << numOperands << " operands (" + << multinode->numVectors() << " vectors)\n"; SmallVector> finalOrder{multinode->numLanes()}; SmallVector> mode{multinode->numLanes()}; // 1. Strip first lane @@ -299,3 +306,35 @@ SLPGraphBuilder::Mode SLPGraphBuilder::modeFromValue(Value const& value) { } return OPCODE; } + +std::pair, size_t> SLPGraphBuilder::getOrCreateOperand(ArrayRef values, + bool* isNewOperand) { + for (auto const& node : nodes) { + if (node->numLanes() != values.size()) { + continue; + } + for (size_t i = 0; i < node->numVectors(); ++i) { + auto* vector = node->getVector(i); + bool equal = true; + for (size_t lane = 0; lane < vector->numLanes(); ++lane) { + if (vector->getElement(lane) != values[lane]) { + equal = false; + break; + } + } + if (equal) { + if (isNewOperand) { + *isNewOperand = false; + } + return std::make_pair(node, i); + } + } + } + if (isNewOperand) { + *isNewOperand = true; + } + auto operandNode = std::make_shared(values); + nodes.emplace_back(operandNode); + reorderWorklist.insert(operandNode.get()); + return std::make_pair(operandNode, 0); +} diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp index c9810d33..8cd501ec 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp @@ -129,7 +129,8 @@ size_t SLPNode::numVectors() const { } NodeVector* SLPNode::addVector(ArrayRef const& values, NodeVector* definingVector) { - auto newVector = vectors.emplace_back(std::make_shared(values)); + auto const& newVector = std::make_shared(values); + vectors.emplace_back(newVector); if (definingVector) { definingVector->operands.emplace_back(newVector); } @@ -137,7 +138,9 @@ NodeVector* SLPNode::addVector(ArrayRef const& values, NodeVector* defini } NodeVector* SLPNode::addVector(ArrayRef const& operations) { - return vectors.emplace_back(std::make_shared(operations)).get(); + auto const& newVector = std::make_shared(operations); + vectors.emplace_back(newVector); + return newVector.get(); } NodeVector* SLPNode::getVector(size_t index) const { @@ -145,12 +148,11 @@ NodeVector* SLPNode::getVector(size_t index) const { return vectors[index].get(); } -SLPNode* SLPNode::addOperand(ArrayRef const& values, NodeVector* definingVector) { - auto const& operandNode = operandNodes.emplace_back(std::make_unique(values)); +void SLPNode::addOperand(std::shared_ptr operandNode, size_t vectorIndex, NodeVector* definingVector) { + auto const& node = operandNodes.emplace_back(std::move(operandNode)); if (definingVector) { - definingVector->operands.emplace_back(operandNode->vectors.front()); + definingVector->operands.emplace_back(node->vectors[vectorIndex]); } - return operandNode.get(); } SLPNode* SLPNode::getOperand(size_t index) const { @@ -176,8 +178,13 @@ size_t SLPNode::numOperands() const { SmallVector SLPNode::postOrder(SLPNode* root) { SmallVector order; for (auto* operand : root->getOperands()) { - assert(std::find(std::begin(order), std::end(order), operand) == std::end(order)); - order.append(postOrder(operand)); + if (std::find(std::begin(order), std::end(order), operand) == std::end(order)) { + for (auto* node : postOrder(operand)) { + if (std::find(std::begin(order), std::end(order), node) == std::end(order)) { + order.emplace_back(node); + } + } + } } order.emplace_back(root); return order; diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp index c4643930..b06c3b91 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp @@ -53,7 +53,6 @@ void slp::dumpSLPNode(SLPNode const& node) { for (size_t i = node.numVectors(); i-- > 0;) { dumpSLPNodeVector(*node.getVector(i)); } - llvm::dbgs() << "\n"; } // Helper functions in an anonymous namespace. From 0b34497cec9d028456543b128dbe11d2a2e2bdef Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Tue, 4 May 2021 18:45:41 +0200 Subject: [PATCH 142/385] Remove recursive postOrder, simplify node vector structure. --- .../Vectorization/SLP/SLPGraphBuilder.h | 4 +- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.h | 9 ++- .../Vectorization/SLP/SLPGraphBuilder.cpp | 26 +++----- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp | 60 +++++++++---------- 4 files changed, 44 insertions(+), 55 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h index 6963a707..bca06f25 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h @@ -44,8 +44,8 @@ namespace mlir { int getLookAheadScore(Value const& last, Value const& candidate, unsigned maxLevel) const; static Mode modeFromValue(Value const& value); - std::pair, size_t> getOrCreateOperand(ArrayRef values, - bool* isNewOperand = nullptr); + std::pair, NodeVector*> getOrCreateOperand(ArrayRef values, + bool* isNewOperand = nullptr); size_t const maxLookAhead; SmallVector> nodes; diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h index 587e50ea..bad89300 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h @@ -25,7 +25,6 @@ namespace mlir { bool contains(Value const& value) const; bool containsBlockArgs() const; - bool vectorizable() const; bool splattable() const; bool isLeaf() const; @@ -41,7 +40,7 @@ namespace mlir { private: SmallVector values; - SmallVector> operands; + SmallVector operands; }; class SLPNode { @@ -62,10 +61,10 @@ namespace mlir { size_t numVectors() const; NodeVector* addVector(ArrayRef const& values, NodeVector* definingVector); - NodeVector* addVector(ArrayRef const& operations); NodeVector* getVector(size_t index) const; + NodeVector* getVectorOrNull(ArrayRef const& values) const; - void addOperand(std::shared_ptr operandNode, size_t vectorIndex, NodeVector* definingVector); + void addOperand(std::shared_ptr operandNode, NodeVector* operandVector, NodeVector* definingVector); SLPNode* getOperand(size_t index) const; std::vector getOperands() const; size_t numOperands() const; @@ -73,7 +72,7 @@ namespace mlir { static SmallVector postOrder(SLPNode* root); private: - SmallVector> vectors; + SmallVector> vectors; SmallVector> operandNodes; }; } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp index eadc616a..9dfa8a25 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp @@ -307,27 +307,19 @@ SLPGraphBuilder::Mode SLPGraphBuilder::modeFromValue(Value const& value) { return OPCODE; } -std::pair, size_t> SLPGraphBuilder::getOrCreateOperand(ArrayRef values, - bool* isNewOperand) { +std::pair, NodeVector*> SLPGraphBuilder::getOrCreateOperand(ArrayRef values, + bool* isNewOperand) { for (auto const& node : nodes) { if (node->numLanes() != values.size()) { continue; } - for (size_t i = 0; i < node->numVectors(); ++i) { - auto* vector = node->getVector(i); - bool equal = true; - for (size_t lane = 0; lane < vector->numLanes(); ++lane) { - if (vector->getElement(lane) != values[lane]) { - equal = false; - break; - } - } - if (equal) { - if (isNewOperand) { - *isNewOperand = false; - } - return std::make_pair(node, i); + auto* vector = node->getVectorOrNull(values); + if (vector) { + if (isNewOperand) { + *isNewOperand = false; } + llvm::dbgs() << "Duplicate found, skipping operand building & reordering...\n"; + return std::make_pair(node, vector); } } if (isNewOperand) { @@ -336,5 +328,5 @@ std::pair, size_t> SLPGraphBuilder::getOrCreateOperand( auto operandNode = std::make_shared(values); nodes.emplace_back(operandNode); reorderWorklist.insert(operandNode.get()); - return std::make_pair(operandNode, 0); + return std::make_pair(operandNode, operandNode->getVector(0)); } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp index 8cd501ec..76b4f7e2 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp @@ -39,12 +39,6 @@ bool NodeVector::containsBlockArgs() const { }); } -bool NodeVector::vectorizable() const { - return std::all_of(std::begin(values), std::end(values), [&](Value const& value) { - return low::slp::vectorizable(value); - }); -} - bool NodeVector::splattable() const { if (containsBlockArgs()) { return std::all_of(std::begin(values), std::end(values), [&](Value const& element) { @@ -70,7 +64,7 @@ size_t NodeVector::numOperands() const { NodeVector* NodeVector::getOperand(size_t index) const { assert(index < operands.size()); - return operands[index].get(); + return operands[index]; } SmallVectorImpl::const_iterator NodeVector::begin() const { @@ -93,11 +87,11 @@ Value const& NodeVector::operator[](size_t lane) const { // === SLPNode === // SLPNode::SLPNode(ArrayRef const& values) { - addVector(values, nullptr); + vectors.emplace_back(std::make_unique(values)); } SLPNode::SLPNode(ArrayRef const& operations) { - addVector(operations); + vectors.emplace_back(std::make_unique(operations)); } Value SLPNode::getValue(size_t lane, size_t index) const { @@ -129,17 +123,8 @@ size_t SLPNode::numVectors() const { } NodeVector* SLPNode::addVector(ArrayRef const& values, NodeVector* definingVector) { - auto const& newVector = std::make_shared(values); - vectors.emplace_back(newVector); - if (definingVector) { - definingVector->operands.emplace_back(newVector); - } - return newVector.get(); -} - -NodeVector* SLPNode::addVector(ArrayRef const& operations) { - auto const& newVector = std::make_shared(operations); - vectors.emplace_back(newVector); + auto const& newVector = vectors.emplace_back(std::make_unique(values)); + definingVector->operands.emplace_back(newVector.get()); return newVector.get(); } @@ -148,11 +133,16 @@ NodeVector* SLPNode::getVector(size_t index) const { return vectors[index].get(); } -void SLPNode::addOperand(std::shared_ptr operandNode, size_t vectorIndex, NodeVector* definingVector) { - auto const& node = operandNodes.emplace_back(std::move(operandNode)); - if (definingVector) { - definingVector->operands.emplace_back(node->vectors[vectorIndex]); - } +NodeVector* SLPNode::getVectorOrNull(ArrayRef const& values) const { + auto it = std::find_if(std::begin(vectors), std::end(vectors), [&](auto const& vector) { + return values.equals(vector->values); + }); + return it != std::end(vectors) ? it->get() : nullptr; +} + +void SLPNode::addOperand(std::shared_ptr operandNode, NodeVector* operandVector, NodeVector* definingVector) { + operandNodes.emplace_back(std::move(operandNode)); + definingVector->operands.emplace_back(operandVector); } SLPNode* SLPNode::getOperand(size_t index) const { @@ -177,15 +167,23 @@ size_t SLPNode::numOperands() const { SmallVector SLPNode::postOrder(SLPNode* root) { SmallVector order; - for (auto* operand : root->getOperands()) { - if (std::find(std::begin(order), std::end(order), operand) == std::end(order)) { - for (auto* node : postOrder(operand)) { - if (std::find(std::begin(order), std::end(order), node) == std::end(order)) { - order.emplace_back(node); + // false = visit operands, true = insert into order + std::vector> worklist; + worklist.emplace_back(root, false); + while (!worklist.empty()) { + auto* node = worklist.back().first; + bool insert = worklist.back().second; + worklist.pop_back(); + if (insert) { + order.emplace_back(node); + } else { + worklist.emplace_back(node, true); + for (auto* operand: node->getOperands()) { + if (std::find(std::begin(order), std::end(order), operand) == std::end(order)) { + worklist.emplace_back(operand, false); } } } } - order.emplace_back(root); return order; } From 1210a577c3ed7833f4d590004e389c51da018e09 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Wed, 5 May 2021 06:19:54 +0200 Subject: [PATCH 143/385] Fix: postOrder computation containing duplicates. --- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.h | 2 +- .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.h | 3 +++ .../Vectorization/SLP/SLPConversion.cpp | 2 +- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp | 19 +++++++++------- .../LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp | 22 ++++++++++++++----- .../VectorizeStructurePatterns.cpp | 8 ++++++- 6 files changed, 39 insertions(+), 17 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h index bad89300..7c52ae82 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h @@ -69,7 +69,7 @@ namespace mlir { std::vector getOperands() const; size_t numOperands() const; - static SmallVector postOrder(SLPNode* root); + static SmallVector postOrder(SLPNode const& root); private: SmallVector> vectors; diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h index b52e9a6d..cf12f8b9 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h @@ -80,6 +80,9 @@ namespace mlir { return vectorOp; } + size_t numNodes(SLPNode const& root); + size_t numVectors(SLPNode const& root); + void dumpOpTree(ArrayRef const& values); void dumpSLPGraph(SLPNode const& root); void dumpSLPNode(SLPNode const& node); diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp index a5e8c041..cd83314d 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp @@ -37,7 +37,7 @@ namespace { } ConversionManager::ConversionManager(SLPNode* root) { - for (auto* node : SLPNode::postOrder(root)) { + for (auto* node : SLPNode::postOrder(*root)) { for (size_t i = node->numVectors(); i-- > 0;) { auto* vector = node->getVector(i); for (size_t lane = 0; lane < vector->numLanes(); ++lane) { diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp index 76b4f7e2..d4254a34 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp @@ -165,11 +165,11 @@ size_t SLPNode::numOperands() const { // === Utilities === // -SmallVector SLPNode::postOrder(SLPNode* root) { - SmallVector order; +SmallVector SLPNode::postOrder(SLPNode const& root) { + SmallVector order; // false = visit operands, true = insert into order - std::vector> worklist; - worklist.emplace_back(root, false); + std::vector> worklist; + worklist.emplace_back(&root, false); while (!worklist.empty()) { auto* node = worklist.back().first; bool insert = worklist.back().second; @@ -177,10 +177,13 @@ SmallVector SLPNode::postOrder(SLPNode* root) { if (insert) { order.emplace_back(node); } else { - worklist.emplace_back(node, true); - for (auto* operand: node->getOperands()) { - if (std::find(std::begin(order), std::end(order), operand) == std::end(order)) { - worklist.emplace_back(operand, false); + if (std::find(std::begin(order), std::end(order), node) == std::end(order)) { + worklist.emplace_back(node, true); + } + for (auto const* operand: node->getOperands()) { + auto pair = std::make_pair(operand, false); + if (std::find(std::begin(worklist), std::end(worklist), pair) == std::end(worklist)) { + worklist.emplace_back(pair); } } } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp index b06c3b91..66c051e1 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp @@ -6,7 +6,6 @@ #include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" #include "mlir/Dialect/StandardOps/IR/Ops.h" #include -#include using namespace mlir; using namespace mlir::spn; @@ -55,6 +54,17 @@ void slp::dumpSLPNode(SLPNode const& node) { } } +size_t slp::numNodes(SLPNode const& root) { + return SLPNode::postOrder(root).size(); +} +size_t slp::numVectors(SLPNode const& root) { + size_t n = 0; + for (auto* node : SLPNode::postOrder(root)) { + n += node->numVectors(); + } + return n; +} + // Helper functions in an anonymous namespace. namespace { void dumpBlockArgOrDefiningAddress(Value const& val) { @@ -91,14 +101,14 @@ void slp::dumpOpTree(ArrayRef const& values) { DenseMap nodes; SmallVector> edges; - std::stack worklist; + std::vector worklist; for (auto const& value : values) { - worklist.push(value); + worklist.emplace_back(value); } while (!worklist.empty()) { - auto value = worklist.top(); - worklist.pop(); + auto value = worklist.back(); + worklist.pop_back(); if (nodes.count(value)) { continue; } @@ -107,7 +117,7 @@ void slp::dumpOpTree(ArrayRef const& values) { for (unsigned i = 0; i < definingOp->getNumOperands(); ++i) { auto const& operand = definingOp->getOperand(i); edges.emplace_back(std::make_tuple(value, operand, i)); - worklist.push(operand); + worklist.emplace_back(operand); } } } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp index e3653626..3529a4ff 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp @@ -116,17 +116,23 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, // Apply SLP vectorization. //task->getParentOfType()->dump(); + task->emitRemark() << "Beginning SLP vectorization"; auto computationType = operands.back().getType().dyn_cast().getElementType(); auto hwVectorWidth = TargetInformation::nativeCPUTarget().getHWVectorEntries(computationType); SeedAnalysis seedAnalysis{taskFunc, hwVectorWidth}; SmallVector seed; + task->emitRemark("Computing seed..."); seedAnalysis.fillSeed(seed, SearchMode::UseBeforeDef); assert(!seed.empty() && "couldn't find a seed!"); //low::slp::dumpOpTree(seed); SLPGraphBuilder builder{3}; + task->emitRemark("Computing graph..."); auto graph = builder.build(seed); + task->emitRemark("Number of SLP nodes in graph: " + std::to_string(numNodes(*graph))); + task->emitRemark("Number of SLP vectors in graph: " + std::to_string(numVectors(*graph))); //low::slp::dumpSLPGraph(*graph); + task->emitRemark("Converting graph..."); // The current vector being transformed. NodeVector* vector = nullptr; ConversionManager conversionManager{graph.get()}; @@ -144,7 +150,7 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, applicator.applyDefaultCostModel(); // Traverse the SLP graph in postorder and apply the vectorization patterns. - for (auto* node : SLPNode::postOrder(graph.get())) { + for (auto* node : SLPNode::postOrder(*graph)) { // Also traverse nodes in postorder to properly handle multinodes. for (size_t vectorIndex = node->numVectors(); vectorIndex-- > 0;) { vector = node->getVector(vectorIndex); From 43f6b327501150fcbdebe8a0221038668889e8a6 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Wed, 5 May 2021 06:21:08 +0200 Subject: [PATCH 144/385] Clean SLP graph builder. --- .../Vectorization/SLP/SLPGraphBuilder.h | 5 +- .../Vectorization/SLP/SLPGraphBuilder.cpp | 132 ++++++++---------- .../Vectorization/SLP/SLPSeeding.cpp | 2 +- 3 files changed, 64 insertions(+), 75 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h index bca06f25..82e7b8c4 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h @@ -41,11 +41,10 @@ namespace mlir { void buildGraph(NodeVector* vector, SLPNode* currentNode); void reorderOperands(SLPNode* multinode) const; std::pair getBest(Mode const& mode, Value const& last, SmallVector& candidates) const; - int getLookAheadScore(Value const& last, Value const& candidate, unsigned maxLevel) const; + unsigned getLookAheadScore(Value const& last, Value const& candidate, unsigned maxLevel) const; static Mode modeFromValue(Value const& value); - std::pair, NodeVector*> getOrCreateOperand(ArrayRef values, - bool* isNewOperand = nullptr); + Optional> nodeOrNone(ArrayRef const& values) const; size_t const maxLookAhead; SmallVector> nodes; diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp index 9dfa8a25..b714de32 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp @@ -6,7 +6,7 @@ #include "mlir/Dialect/StandardOps/IR/Ops.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" -#include +#include "llvm/ADT/SmallSet.h" using namespace mlir; using namespace mlir::spn::low::slp; @@ -24,11 +24,20 @@ std::shared_ptr SLPGraphBuilder::build(ArrayRef const& seed) { // Some helper functions in an anonymous namespace. namespace { - bool escapesMultinode(Value const& value, SLPNode* currentNode) { - // TODO: determine if multinodes should stop building if an operation escapes it - // or if simply disallowing reordering in this lane might be better - return std::any_of(std::begin(value.getUsers()), std::end(value.getUsers()), [&](auto* user) { - return !currentNode->contains(user->getResult(0)); + bool appendable(SLPNode* node, + OperationName const& opCode, + ArrayRef> const& allOperands, + unsigned operandIndex) { + return std::all_of(std::begin(allOperands), std::end(allOperands), [&](auto const& operands) { + auto const& operand = operands[operandIndex]; + if (operand.getDefiningOp()->getName() != opCode) { + return false; + } + // Check if any operand escapes the current node. + // TODO: determine if multinodes should stop building if an operation escapes it or if simply disallowing reordering in this lane might be better + return std::all_of(std::begin(operand.getUsers()), std::end(operand.getUsers()), [&](auto* user) { + return node->contains(user->getResult(0)); + }); }); } @@ -41,15 +50,6 @@ namespace { return operands; } - SmallVector> getAllOperands(NodeVector* vector) { - SmallVector> allOperands; - allOperands.reserve(vector->numLanes()); - for (auto const& value : *vector) { - allOperands.emplace_back(getOperands(value)); - } - return allOperands; - } - void sortByOpcode(SmallVector& values, Optional const& smallestOpcode) { std::sort(std::begin(values), std::end(values), [&](Value const& lhs, Value const& rhs) { auto* lhsOp = lhs.getDefiningOp(); @@ -72,6 +72,18 @@ namespace { }); } + SmallVector> getAllOperandsSorted(NodeVector* vector, OperationName const& currentOpCode) { + SmallVector> allOperands; + allOperands.reserve(vector->numLanes()); + for (auto const& value : *vector) { + allOperands.emplace_back(getOperands(value)); + } + for (auto& operands : allOperands) { + sortByOpcode(operands, currentOpCode); + } + return allOperands; + } + } // end namespace void SLPGraphBuilder::buildGraph(NodeVector* vector, SLPNode* currentNode) { @@ -85,41 +97,30 @@ void SLPGraphBuilder::buildGraph(NodeVector* vector, SLPNode* currentNode) { // 1. Commutative if (commutative(vector->begin(), vector->end())) { // A. Coarsening Mode - auto allOperands = getAllOperands(vector); - for (auto& operands : allOperands) { - sortByOpcode(operands, currentOpCode); - } - // Determine lanes whose elements aren't allowed to be reordered. This is the case if they have escaping uses. - // DenseMap> lockedLanes; + auto allOperands = getAllOperandsSorted(vector, currentOpCode); for (unsigned i = 0; i < arity; ++i) { - // bool canAppendToNode = true; - // for(size_t n = 0; n < allOperands.size(); ++n) { - // SmallVector const& operands = allOperands[n]; - // for(size_t ) - // } - if (std::all_of(std::begin(allOperands), std::end(allOperands), [&](SmallVector const& operands) { - return operands[i].getDefiningOp()->getName() == currentOpCode && !escapesMultinode(operands[i], currentNode); - })) { - SmallVector vectorValues; - for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { - vectorValues.emplace_back(allOperands[lane][i]); - } + SmallVector vectorValues; + for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { + vectorValues.emplace_back(allOperands[lane][i]); + } + auto const existingNode = nodeOrNone(vectorValues); + if (existingNode.hasValue()) { + currentNode->addOperand(nodes[existingNode->first], existingNode->second, vector); + } else if (appendable(currentNode, currentOpCode, allOperands, i)) { auto* newVector = currentNode->addVector(vectorValues, vector); buildGraph(newVector, currentNode); } else { // TODO: here might be a good place to implement variable vector width - SmallVector operandValues; - for (size_t lane = 0; lane < currentNode->numLanes(); ++lane) { - operandValues.emplace_back(allOperands[lane][i]); - } - auto operandAndVector = getOrCreateOperand(operandValues); - currentNode->addOperand(operandAndVector.first, operandAndVector.second, vector); + auto operandNode = std::make_shared(vectorValues); + nodes.emplace_back(operandNode); + reorderWorklist.insert(operandNode.get()); + currentNode->addOperand(operandNode, operandNode->getVector(0), vector); } } // B. Normal Mode: Finished building multi-node if (currentNode->isRootOfNode(*vector)) { if (reorderWorklist.erase(currentNode)) { - reorderOperands(currentNode); + //reorderOperands(currentNode); } for (auto* operandNode : currentNode->getOperands()) { buildGraph(operandNode->getVector(operandNode->numVectors() - 1), operandNode); @@ -134,11 +135,15 @@ void SLPGraphBuilder::buildGraph(NodeVector* vector, SLPNode* currentNode) { auto operand = currentNode->getValue(lane, 0).getDefiningOp()->getOperand(i); operandValues.emplace_back(operand); } - bool isNewOperand = false; - auto operandAndVector = getOrCreateOperand(operandValues, &isNewOperand); - currentNode->addOperand(operandAndVector.first, operandAndVector.second, vector); - if (isNewOperand) { - buildGraph(operandAndVector.first->getVector(0), operandAndVector.first.get()); + auto const existingNode = nodeOrNone(operandValues); + if (existingNode.hasValue()) { + currentNode->addOperand(nodes[existingNode->first], existingNode->second, vector); + } else { + auto operandNode = std::make_shared(operandValues); + nodes.emplace_back(operandNode); + reorderWorklist.insert(operandNode.get()); + currentNode->addOperand(operandNode, operandNode->getVector(0), vector); + buildGraph(operandNode->getVector(0), operandNode.get()); } } } @@ -146,8 +151,7 @@ void SLPGraphBuilder::buildGraph(NodeVector* vector, SLPNode* currentNode) { void SLPGraphBuilder::reorderOperands(SLPNode* multinode) const { auto const& numOperands = multinode->numOperands(); - llvm::dbgs() << "Reordering multinode " << multinode << " with " << numOperands << " operands (" - << multinode->numVectors() << " vectors)\n"; + //llvm::dbgs() << "Reordering multinode " << multinode << " with " << numOperands << " operands (" << multinode->numVectors() << " vectors)\n"; SmallVector> finalOrder{multinode->numLanes()}; SmallVector> mode{multinode->numLanes()}; // 1. Strip first lane @@ -246,11 +250,11 @@ std::pair SLPGraphBuilder::getBest(Mode const& mod // TODO: when the level is increased, we recompute everything from the level before. change that maybe? for (size_t level = 1; level <= maxLookAhead; ++level) { // Best is the candidate with max score - auto bestScore = 0; - std::set scores; + unsigned bestScore = 0; + llvm::SmallSet scores; for (auto const& candidate : bestCandidates) { // Get the look-ahead score - auto const& score = getLookAheadScore(last, candidate, level); + unsigned score = getLookAheadScore(last, candidate, level); if (scores.empty() || score > bestScore) { best = candidate; bestScore = score; @@ -272,7 +276,7 @@ std::pair SLPGraphBuilder::getBest(Mode const& mod return {best, resultMode}; } -int SLPGraphBuilder::getLookAheadScore(Value const& last, Value const& candidate, unsigned maxLevel) const { +unsigned SLPGraphBuilder::getLookAheadScore(Value const& last, Value const& candidate, unsigned maxLevel) const { if (maxLevel == 0 || last.isa() || candidate.isa()) { if (last == candidate) { return 1; @@ -307,26 +311,12 @@ SLPGraphBuilder::Mode SLPGraphBuilder::modeFromValue(Value const& value) { return OPCODE; } -std::pair, NodeVector*> SLPGraphBuilder::getOrCreateOperand(ArrayRef values, - bool* isNewOperand) { - for (auto const& node : nodes) { - if (node->numLanes() != values.size()) { - continue; - } - auto* vector = node->getVectorOrNull(values); - if (vector) { - if (isNewOperand) { - *isNewOperand = false; - } - llvm::dbgs() << "Duplicate found, skipping operand building & reordering...\n"; - return std::make_pair(node, vector); +Optional> SLPGraphBuilder::nodeOrNone(ArrayRef const& values) const { + for (size_t i = 0; i < nodes.size(); ++i) { + auto const& node = nodes[i]; + if (auto* vector = node->getVectorOrNull(values)) { + return std::make_pair(i, vector); } } - if (isNewOperand) { - *isNewOperand = true; - } - auto operandNode = std::make_shared(values); - nodes.emplace_back(operandNode); - reorderWorklist.insert(operandNode.get()); - return std::make_pair(operandNode, operandNode->getVector(0)); + return None; } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp index 5463f730..dbf10bd2 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp @@ -34,7 +34,7 @@ namespace { void SeedAnalysis::fillSeed(SmallVectorImpl& seed, SearchMode const& mode) const { llvm::StringMap>> seedsByOpName; auto const& opDepths = getOpDepths(rootOp); - + rootOp->emitRemark("Computing seed out of " + std::to_string(opDepths.size()) + " operations..."); rootOp->walk([&](Operation* op) { if (!vectorizable(op)) { return WalkResult::advance(); From 814a22272e7d4d4686708ae1e0e5564b7adebcb5 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Wed, 5 May 2021 06:22:02 +0200 Subject: [PATCH 145/385] Add nlts & fashion-mnist tests. --- ...ashion_mnist_200_100_1_14_15_10_1_True.bin | Bin 0 -> 8741816 bytes ...mnist_200_100_1_14_15_10_1_True_output.csv | 1000 ++ .../test/examples/fashion-mnist/input.csv | 1000 ++ .../test_vector_fashion-mnist.py | 48 + .../test/examples/nltcs/input.csv | 10000 ++++++++++++++++ .../nltcs/nltcs_100_200_2_10_8_8_1_True.bin | Bin 0 -> 288880 bytes .../nltcs_100_200_2_10_8_8_1_True_output.csv | 10000 ++++++++++++++++ .../test/examples/nltcs/spn_0.bin | Bin 0 -> 40144 bytes .../test/examples/nltcs/test_vector_nltcs.py | 47 + 9 files changed, 22095 insertions(+) create mode 100644 python-interface/test/examples/fashion-mnist/fashion_mnist_200_100_1_14_15_10_1_True.bin create mode 100644 python-interface/test/examples/fashion-mnist/fashion_mnist_200_100_1_14_15_10_1_True_output.csv create mode 100644 python-interface/test/examples/fashion-mnist/input.csv create mode 100644 python-interface/test/examples/fashion-mnist/test_vector_fashion-mnist.py create mode 100644 python-interface/test/examples/nltcs/input.csv create mode 100644 python-interface/test/examples/nltcs/nltcs_100_200_2_10_8_8_1_True.bin create mode 100644 python-interface/test/examples/nltcs/nltcs_100_200_2_10_8_8_1_True_output.csv create mode 100644 python-interface/test/examples/nltcs/spn_0.bin create mode 100644 python-interface/test/examples/nltcs/test_vector_nltcs.py diff --git a/python-interface/test/examples/fashion-mnist/fashion_mnist_200_100_1_14_15_10_1_True.bin b/python-interface/test/examples/fashion-mnist/fashion_mnist_200_100_1_14_15_10_1_True.bin new file mode 100644 index 0000000000000000000000000000000000000000..59ce1411bdb8eb50b12b9b9a5cfd04ef65024207 GIT binary patch literal 8741816 zcmYh^b(Es@!^Yti8yg!N8yg!Ndu(iM?6I-2v9YnSv9YnSv9Yno#vU7ctoOUW=lbL2 zoX?rL2KKp~WtSn`nF+^@8&{iP+_;5O6OUVPfr-bRUz{X-BsxtnE}ZfIP7{oq=zp4a z!g1rK3h!k?kmZ&M??oRUe}4Go|2xve 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Normal Mode: Finished building multi-node if (currentNode->isRootOfNode(*vector)) { - if (reorderWorklist.erase(currentNode)) { - //reorderOperands(currentNode); - } + //reorderOperands(currentNode); for (auto* operandNode : currentNode->getOperands()) { - buildGraph(operandNode->getVector(operandNode->numVectors() - 1), operandNode); + if (buildWorklist.erase(operandNode)) { + buildGraph(operandNode->getVector(operandNode->numVectors() - 1), operandNode); + } } } } @@ -141,7 +141,7 @@ void SLPGraphBuilder::buildGraph(NodeVector* vector, SLPNode* currentNode) { } else { auto operandNode = std::make_shared(operandValues); nodes.emplace_back(operandNode); - reorderWorklist.insert(operandNode.get()); + buildWorklist.insert(operandNode.get()); currentNode->addOperand(operandNode, operandNode->getVector(0), vector); buildGraph(operandNode->getVector(0), operandNode.get()); } From b1ab2d420cae9ff0d73a94ba414a5c9917e8bca0 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Fri, 7 May 2021 17:26:03 +0200 Subject: [PATCH 147/385] Speed up postOrder computation. --- .../LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp index d4254a34..76b6e37f 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp @@ -5,6 +5,7 @@ #include "LoSPNtoCPU/Vectorization/SLP/SLPNode.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" +#include "llvm/ADT/SmallSet.h" using namespace mlir; using namespace mlir::spn; @@ -168,23 +169,25 @@ size_t SLPNode::numOperands() const { SmallVector SLPNode::postOrder(SLPNode const& root) { SmallVector order; // false = visit operands, true = insert into order - std::vector> worklist; + SmallVector> worklist; + llvm::SmallSet finishedNodes; worklist.emplace_back(&root, false); while (!worklist.empty()) { + if (finishedNodes.contains(worklist.back().first)) { + worklist.pop_back(); + continue; + } auto* node = worklist.back().first; bool insert = worklist.back().second; worklist.pop_back(); if (insert) { order.emplace_back(node); + finishedNodes.insert(node); } else { - if (std::find(std::begin(order), std::end(order), node) == std::end(order)) { - worklist.emplace_back(node, true); - } + worklist.emplace_back(node, true); for (auto const* operand: node->getOperands()) { auto pair = std::make_pair(operand, false); - if (std::find(std::begin(worklist), std::end(worklist), pair) == std::end(worklist)) { - worklist.emplace_back(pair); - } + worklist.emplace_back(pair); } } } From b0e98337cf07a9cabca0fe752531492c40eaa610 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Fri, 7 May 2021 17:33:08 +0200 Subject: [PATCH 148/385] Speed up postOrder computation even more. --- mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp index 76b6e37f..a13bd103 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp @@ -186,8 +186,7 @@ SmallVector SLPNode::postOrder(SLPNode const& root) { } else { worklist.emplace_back(node, true); for (auto const* operand: node->getOperands()) { - auto pair = std::make_pair(operand, false); - worklist.emplace_back(pair); + worklist.emplace_back(operand, false); } } } From bdca6de82fb2f0106d95854f69c4d14c16959606 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Fri, 7 May 2021 17:37:39 +0200 Subject: [PATCH 149/385] Add basic cost model draft. --- .../LoSPNtoCPU/Vectorization/SLP/CostModel.h | 39 +++++++++++++++ mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt | 1 + .../Vectorization/SLP/CostModel.cpp | 47 +++++++++++++++++++ .../VectorizeStructurePatterns.cpp | 1 + 4 files changed, 88 insertions(+) create mode 100644 mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/CostModel.h create mode 100644 mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/CostModel.cpp diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/CostModel.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/CostModel.h new file mode 100644 index 00000000..9bfa6426 --- /dev/null +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/CostModel.h @@ -0,0 +1,39 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_COSTMODEL_H +#define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_COSTMODEL_H + +#include "SLPNode.h" + +namespace mlir { + namespace spn { + namespace low { + namespace slp { + + // TODO: google "use weak_ptrs as cache" + + class CostModel { + public: + unsigned getScalarCost(Value const& value); + unsigned getVectorCost(NodeVector const* vector); + protected: + virtual unsigned computeScalarCost(Value const& value) = 0; + virtual unsigned computeVectorCost(NodeVector const* vector) = 0; + private: + DenseMap cachedScalarCosts; + DenseMap cachedVectorCosts; + }; + + class UnitCostModel : public CostModel { + unsigned computeScalarCost(Value const& value) override; + unsigned computeVectorCost(NodeVector const* vector) override; + }; + } + } + } +} + +#endif //SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_COSTMODEL_H diff --git a/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt b/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt index 775fb3be..f32d3489 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt +++ b/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt @@ -14,6 +14,7 @@ add_mlir_conversion_library(MLIRLoSPNtoCPUTransforms Vectorization/VectorizeStructurePatterns.cpp Vectorization/VectorizeNodePatterns.cpp Vectorization/ReplaceGatherWithShuffle.cpp + Vectorization/SLP/CostModel.cpp Vectorization/SLP/SLPNode.cpp Vectorization/SLP/SLPSeeding.cpp Vectorization/SLP/SLPGraphBuilder.cpp diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/CostModel.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/CostModel.cpp new file mode 100644 index 00000000..b2b23288 --- /dev/null +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/CostModel.cpp @@ -0,0 +1,47 @@ +// +// This file is part of the SPNC project. +// Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. +// + +#include "LoSPNtoCPU/Vectorization/SLP/CostModel.h" + +using namespace mlir; +using namespace mlir::spn::low::slp; + +// === CostModel === // + +unsigned CostModel::getScalarCost(Value const& value) { + auto const& entry = cachedScalarCosts.try_emplace(value, 0); + if (entry.second) { + entry.first->getSecond() = computeScalarCost(value); + } + return entry.first->second; +} + +unsigned CostModel::getVectorCost(NodeVector const* vector) { + auto const& entry = cachedVectorCosts.try_emplace(vector, 0); + if (entry.second) { + entry.first->getSecond() = computeVectorCost(vector); + } + return entry.first->second; +} + +// === UnitCostModel === // + +unsigned UnitCostModel::computeScalarCost(Value const& value) { + unsigned cost = 1; + if (auto* definingOp = value.getDefiningOp()) { + for (auto const& operand : definingOp->getOperands()) { + cost += getScalarCost(operand); + } + } + return cost; +} + +unsigned UnitCostModel::computeVectorCost(NodeVector const* vector) { + unsigned cost = 1; + for (size_t i = 0; i < vector->numOperands(); ++i) { + cost += getVectorCost(vector->getOperand(i)); + } + return cost; +} diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp index 3529a4ff..d966f92a 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp @@ -232,6 +232,7 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, } } } + task->emitRemark("Graph conversion complete."); rewriter.restoreInsertionPoint(callPoint); rewriter.replaceOpWithNewOp(task, taskFunc, operands); return success(); From 4528de26097b93ce58096e8561e8255a57c47129 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Fri, 7 May 2021 17:47:43 +0200 Subject: [PATCH 150/385] Rename SLP files. --- .../Conversion/LoSPNtoCPU/Vectorization/SLP/CostModel.h | 2 +- .../SLP/{SLPConversion.h => GraphConversion.h} | 8 ++++---- .../Vectorization/SLP/{SLPNode.h => SLPGraph.h} | 6 +++--- .../LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h | 4 ++-- .../Vectorization/SLP/SLPVectorizationPatterns.h | 4 ++-- .../Vectorization/SLP/{SLPSeeding.h => Seeding.h} | 8 ++++---- .../LoSPNtoCPU/Vectorization/SLP/{SLPUtil.h => Util.h} | 8 ++++---- mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt | 8 ++++---- .../SLP/{SLPConversion.cpp => GraphConversion.cpp} | 2 +- .../Vectorization/SLP/{SLPNode.cpp => SLPGraph.cpp} | 4 ++-- .../LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp | 2 +- .../Vectorization/SLP/SLPVectorizationPatterns.cpp | 2 +- .../Vectorization/SLP/{SLPSeeding.cpp => Seeding.cpp} | 4 ++-- .../Vectorization/SLP/{SLPUtil.cpp => Util.cpp} | 2 +- .../Vectorization/VectorizeStructurePatterns.cpp | 6 +++--- 15 files changed, 35 insertions(+), 35 deletions(-) rename mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/{SLPConversion.h => GraphConversion.h} (95%) rename mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/{SLPNode.h => SLPGraph.h} (98%) rename mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/{SLPSeeding.h => Seeding.h} (93%) rename mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/{SLPUtil.h => Util.h} (98%) rename mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/{SLPConversion.cpp => GraphConversion.cpp} (98%) rename mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/{SLPNode.cpp => SLPGraph.cpp} (98%) rename mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/{SLPSeeding.cpp => Seeding.cpp} (96%) rename mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/{SLPUtil.cpp => Util.cpp} (99%) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/CostModel.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/CostModel.h index 9bfa6426..64765dc9 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/CostModel.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/CostModel.h @@ -6,7 +6,7 @@ #ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_COSTMODEL_H #define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_COSTMODEL_H -#include "SLPNode.h" +#include "SLPGraph.h" namespace mlir { namespace spn { diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/GraphConversion.h similarity index 95% rename from mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h rename to mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/GraphConversion.h index 384f8325..01bb3127 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/GraphConversion.h @@ -3,10 +3,10 @@ // Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. // -#ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPCONVERSION_H -#define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPCONVERSION_H +#ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_GRAPHCONVERSION_H +#define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_GRAPHCONVERSION_H -#include "SLPNode.h" +#include "SLPGraph.h" namespace mlir { namespace spn { @@ -62,4 +62,4 @@ namespace mlir { } } -#endif //SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPCONVERSION_H +#endif //SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_GRAPHCONVERSION_H diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h similarity index 98% rename from mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h rename to mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h index 7c52ae82..f0c47bf5 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h @@ -3,8 +3,8 @@ // Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. // -#ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPNODE_H -#define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPNODE_H +#ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPGRAPH_H +#define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPGRAPH_H #include "mlir/IR/Operation.h" #include "llvm/ADT/SmallPtrSet.h" @@ -80,4 +80,4 @@ namespace mlir { } } -#endif //SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPNODE_H +#endif //SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPGRAPH_H diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h index 3478a1d6..42894148 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h @@ -7,8 +7,8 @@ #define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPGRAPHBUILDER_H #include "mlir/IR/Operation.h" -#include "SLPNode.h" -#include "SLPSeeding.h" +#include "SLPGraph.h" +#include "Seeding.h" namespace mlir { namespace spn { diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h index 1022a3ba..5d4fc0b5 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h @@ -8,8 +8,8 @@ #include -#include "SLPNode.h" -#include "SLPConversion.h" +#include "SLPGraph.h" +#include "GraphConversion.h" #include "LoSPN/LoSPNDialect.h" #include "LoSPN/LoSPNOps.h" #include "mlir/Dialect/StandardOps/IR/Ops.h" diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/Seeding.h similarity index 93% rename from mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h rename to mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/Seeding.h index 8ec14d09..9b58b06f 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/Seeding.h @@ -3,11 +3,11 @@ // Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. // -#ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPSEEDING_H -#define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPSEEDING_H +#ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SEEDING_H +#define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SEEDING_H #include "mlir/IR/Operation.h" -#include "SLPNode.h" +#include "SLPGraph.h" namespace mlir { namespace spn { @@ -40,4 +40,4 @@ namespace mlir { } } -#endif //SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPSEEDING_H +#endif //SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SEEDING_H diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/Util.h similarity index 98% rename from mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h rename to mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/Util.h index cf12f8b9..ce46edb5 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/Util.h @@ -3,10 +3,10 @@ // Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. // -#ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPUTIL_H -#define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPUTIL_H +#ifndef SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_UTIL_H +#define SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_UTIL_H -#include "SLPNode.h" +#include "SLPGraph.h" #include "LoSPN/LoSPNOps.h" #include "mlir/IR/Operation.h" #include "mlir/IR/PatternMatch.h" @@ -93,4 +93,4 @@ namespace mlir { } } -#endif //SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_SLPUTIL_H +#endif //SPNC_MLIR_INCLUDE_CONVERSION_LOSPNTOCPU_VECTORIZATION_SLP_UTIL_H diff --git a/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt b/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt index f32d3489..5ef585ef 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt +++ b/mlir/lib/Conversion/LoSPNtoCPU/CMakeLists.txt @@ -14,13 +14,13 @@ add_mlir_conversion_library(MLIRLoSPNtoCPUTransforms Vectorization/VectorizeStructurePatterns.cpp Vectorization/VectorizeNodePatterns.cpp Vectorization/ReplaceGatherWithShuffle.cpp + Vectorization/SLP/GraphConversion.cpp Vectorization/SLP/CostModel.cpp - Vectorization/SLP/SLPNode.cpp - Vectorization/SLP/SLPSeeding.cpp + Vectorization/SLP/Seeding.cpp + Vectorization/SLP/Util.cpp + Vectorization/SLP/SLPGraph.cpp Vectorization/SLP/SLPGraphBuilder.cpp - Vectorization/SLP/SLPUtil.cpp Vectorization/SLP/SLPVectorizationPatterns.cpp - Vectorization/SLP/SLPConversion.cpp LoSPNtoCPUConversionPasses.cpp ADDITIONAL_HEADER_DIRS diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/GraphConversion.cpp similarity index 98% rename from mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp rename to mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/GraphConversion.cpp index cd83314d..cf581ea0 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPConversion.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/GraphConversion.cpp @@ -3,7 +3,7 @@ // Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. // -#include "LoSPNtoCPU/Vectorization/SLP/SLPConversion.h" +#include "LoSPNtoCPU/Vectorization/SLP/GraphConversion.h" using namespace mlir; using namespace mlir::spn::low::slp; diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp similarity index 98% rename from mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp rename to mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp index a13bd103..98a5eeee 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPNode.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp @@ -3,8 +3,8 @@ // Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. // -#include "LoSPNtoCPU/Vectorization/SLP/SLPNode.h" -#include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" +#include "LoSPNtoCPU/Vectorization/SLP/SLPGraph.h" +#include "LoSPNtoCPU/Vectorization/SLP/Util.h" #include "llvm/ADT/SmallSet.h" using namespace mlir; diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp index 80900da3..5a691a80 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.cpp @@ -5,7 +5,7 @@ #include "mlir/Dialect/StandardOps/IR/Ops.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h" -#include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" +#include "LoSPNtoCPU/Vectorization/SLP/Util.h" #include "llvm/ADT/SmallSet.h" using namespace mlir; diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp index 95553de1..d439ed92 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.cpp @@ -4,7 +4,7 @@ // #include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h" -#include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" +#include "LoSPNtoCPU/Vectorization/SLP/Util.h" #include "mlir/Dialect/Math/IR/Math.h" #include "mlir/IR/BlockAndValueMapping.h" #include "llvm/Support/FormatVariadic.h" diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/Seeding.cpp similarity index 96% rename from mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp rename to mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/Seeding.cpp index dbf10bd2..2078cdd5 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPSeeding.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/Seeding.cpp @@ -3,8 +3,8 @@ // Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. // -#include "LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h" -#include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" +#include "LoSPNtoCPU/Vectorization/SLP/Seeding.h" +#include "LoSPNtoCPU/Vectorization/SLP/Util.h" using namespace mlir; using namespace mlir::spn::low::slp; diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/Util.cpp similarity index 99% rename from mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp rename to mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/Util.cpp index 66c051e1..8dee669c 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPUtil.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/Util.cpp @@ -3,7 +3,7 @@ // Copyright (c) 2020 Embedded Systems and Applications Group, TU Darmstadt. All rights reserved. // -#include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" +#include "LoSPNtoCPU/Vectorization/SLP/Util.h" #include "mlir/Dialect/StandardOps/IR/Ops.h" #include diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp index d966f92a..e85a403a 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp @@ -7,9 +7,9 @@ //============================================================================== #include "LoSPNtoCPU/Vectorization/VectorizationPatterns.h" -#include "LoSPNtoCPU/Vectorization/SLP/SLPSeeding.h" +#include "LoSPNtoCPU/Vectorization/SLP/Seeding.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPGraphBuilder.h" -#include "LoSPNtoCPU/Vectorization/SLP/SLPUtil.h" +#include "LoSPNtoCPU/Vectorization/SLP/Util.h" #include "LoSPNtoCPU/Vectorization/SLP/SLPVectorizationPatterns.h" #include "llvm/Support/FormatVariadic.h" #include "mlir/Dialect/SCF/SCF.h" @@ -150,7 +150,7 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, applicator.applyDefaultCostModel(); // Traverse the SLP graph in postorder and apply the vectorization patterns. - for (auto* node : SLPNode::postOrder(*graph)) { + for (auto* node : SLPGraph::postOrder(*graph)) { // Also traverse nodes in postorder to properly handle multinodes. for (size_t vectorIndex = node->numVectors(); vectorIndex-- > 0;) { vector = node->getVector(vectorIndex); From ee2e686016b7a3a32465f9b873fbb7e1021ad9bc Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Fri, 7 May 2021 17:57:52 +0200 Subject: [PATCH 151/385] Separate postOrder from SLPNode. --- .../Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h | 7 +++++-- .../LoSPNtoCPU/Vectorization/SLP/GraphConversion.cpp | 2 +- .../Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp | 2 +- mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/Util.cpp | 4 ++-- .../Vectorization/VectorizeStructurePatterns.cpp | 2 +- 5 files changed, 10 insertions(+), 7 deletions(-) diff --git a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h index f0c47bf5..9ebe45bf 100644 --- a/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h +++ b/mlir/include/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.h @@ -69,12 +69,15 @@ namespace mlir { std::vector getOperands() const; size_t numOperands() const; - static SmallVector postOrder(SLPNode const& root); - private: SmallVector> vectors; SmallVector> operandNodes; }; + + namespace graph { + SmallVector postOrder(SLPNode const& root); + } + } } } diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/GraphConversion.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/GraphConversion.cpp index cf581ea0..78333c96 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/GraphConversion.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/GraphConversion.cpp @@ -37,7 +37,7 @@ namespace { } ConversionManager::ConversionManager(SLPNode* root) { - for (auto* node : SLPNode::postOrder(*root)) { + for (auto* node : graph::postOrder(*root)) { for (size_t i = node->numVectors(); i-- > 0;) { auto* vector = node->getVector(i); for (size_t lane = 0; lane < vector->numLanes(); ++lane) { diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp index 98a5eeee..c81e1533 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/SLPGraph.cpp @@ -166,7 +166,7 @@ size_t SLPNode::numOperands() const { // === Utilities === // -SmallVector SLPNode::postOrder(SLPNode const& root) { +SmallVector graph::postOrder(SLPNode const& root) { SmallVector order; // false = visit operands, true = insert into order SmallVector> worklist; diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/Util.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/Util.cpp index 8dee669c..2f317dcf 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/Util.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/SLP/Util.cpp @@ -55,11 +55,11 @@ void slp::dumpSLPNode(SLPNode const& node) { } size_t slp::numNodes(SLPNode const& root) { - return SLPNode::postOrder(root).size(); + return graph::postOrder(root).size(); } size_t slp::numVectors(SLPNode const& root) { size_t n = 0; - for (auto* node : SLPNode::postOrder(root)) { + for (auto* node : graph::postOrder(root)) { n += node->numVectors(); } return n; diff --git a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp index e85a403a..09aaa2ff 100644 --- a/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp +++ b/mlir/lib/Conversion/LoSPNtoCPU/Vectorization/VectorizeStructurePatterns.cpp @@ -150,7 +150,7 @@ LogicalResult VectorizeSingleTask::matchAndRewrite(SPNTask task, applicator.applyDefaultCostModel(); // Traverse the SLP graph in postorder and apply the vectorization patterns. - for (auto* node : SLPGraph::postOrder(*graph)) { + for (auto* node : graph::postOrder(*graph)) { // Also traverse nodes in postorder to properly handle multinodes. for (size_t vectorIndex = node->numVectors(); vectorIndex-- > 0;) { vector = node->getVector(vectorIndex); From 955235ff7c89c4196ad5a43c2da982e4a105cf85 Mon Sep 17 00:00:00 2001 From: Sebastian Vollbrecht Date: Sun, 9 May 2021 20:29:10 +0200 Subject: [PATCH 152/385] Update tests, remove outdated .mlir files. --- mini-example.mlir | 29 - mini-example_blockargs.mlir | 60 - mini-example_blockargs_only.mlir | 50 - .../test_vector_fashion-mnist.py | 10 +- .../test/examples/nltcs/test_vector_nltcs.py | 10 +- .../test/examples/plants/input.csv | 10000 ++++++++++++++++ .../plants_100_200_1_50_45_10_1_True.bin | Bin 0 -> 13491488 bytes ...lants_100_200_1_50_45_10_1_True_output.csv | 10000 ++++++++++++++++ .../plants/plants_100_200_3_3_5_3_1_True.bin | Bin 0 -> 89136 bytes .../plants_100_200_3_3_5_3_1_True_output.csv | 10000 ++++++++++++++++ .../examples/plants/test_vector_plants.py | 52 + .../test/test-slp-vectorized-consecutive.py | 7 +- .../test/test-slp-vectorized-escaping.py | 7 +- .../test/test-slp-vectorized-mini.py | 7 +- .../test/test-slp-vectorized-speaker.py | 19 +- .../test/test-slp-vectorized-tree.py | 7 +- 16 files changed, 30093 insertions(+), 165 deletions(-) delete mode 100644 mini-example.mlir delete mode 100644 mini-example_blockargs.mlir delete mode 100644 mini-example_blockargs_only.mlir create mode 100644 python-interface/test/examples/plants/input.csv create mode 100644 python-interface/test/examples/plants/plants_100_200_1_50_45_10_1_True.bin create mode 100644 python-interface/test/examples/plants/plants_100_200_1_50_45_10_1_True_output.csv create mode 100644 python-interface/test/examples/plants/plants_100_200_3_3_5_3_1_True.bin create mode 100644 python-interface/test/examples/plants/plants_100_200_3_3_5_3_1_True_output.csv create mode 100644 python-interface/test/examples/plants/test_vector_plants.py diff --git a/mini-example.mlir b/mini-example.mlir deleted file mode 100644 index ae45c2e8..00000000 --- a/mini-example.mlir +++ /dev/null @@ -1,29 +0,0 @@ -module { - "hi_spn.joint_query"() ( { - "hi_spn.graph"() ( { - ^bb0(%arg0: f64, %arg1: f64, %arg2: f64, %arg3: f64, %arg4: f64, %arg5: f64, %arg6: f64, %arg7: f64, %arg8: f64, %arg9: f64, %arg10: f64, %arg11: f64, %arg12: f64, %arg13: f64, %arg14: f64, %arg15: f64): // no predecessors - %0 = "hi_spn.gaussian"(%arg0) {mean = 1.100000e-01 : f64, stddev = 1.000000e+00 : f64} : (f64) -> !hi_spn.probability - %1 = "hi_spn.gaussian"(%arg1) {mean = 1.200000e-01 : f64, stddev = 7.500000e-01 : f64} : (f64) -> !hi_spn.probability - %2 = "hi_spn.gaussian"(%arg2) {mean = 1.300000e-01 : f64, stddev = 5.000000e-01 : f64} : (f64) -> !hi_spn.probability - %3 = "hi_spn.gaussian"(%arg3) {mean = 1.400000e-01 : f64, stddev = 2.500000e-01 : f64} : (f64) -> !hi_spn.probability - %4 = "hi_spn.product"(%0, %1, %2, %3) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability - %5 = "hi_spn.gaussian"(%arg4) {mean = 2.100000e-01 : f64, stddev = 1.000000e+00 : f64} : (f64) -> !hi_spn.probability - %6 = "hi_spn.gaussian"(%arg5) {mean = 2.200000e-01 : f64, stddev = 7.500000e-01 : f64} : (f64) -> !hi_spn.probability - %7 = "hi_spn.gaussian"(%arg6) {mean = 2.300000e-01 : f64, stddev = 5.000000e-01 : f64} : (f64) -> !hi_spn.probability - %8 = "hi_spn.gaussian"(%arg7) {mean = 2.400000e-01 : f64, stddev = 2.500000e-01 : f64} : (f64) -> !hi_spn.probability - %9 = "hi_spn.product"(%5, %6, %7, %8) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability - %10 = "hi_spn.gaussian"(%arg8) {mean = 3.100000e-01 : f64, stddev = 1.000000e+00 : f64} : (f64) -> !hi_spn.probability - %11 = "hi_spn.gaussian"(%arg9) {mean = 3.200000e-01 : f64, stddev = 7.500000e-01 : f64} : (f64) -> !hi_spn.probability - %12 = "hi_spn.gaussian"(%arg10) {mean = 3.300000e-01 : f64, stddev = 5.000000e-01 : f64} : (f64) -> !hi_spn.probability - %13 = "hi_spn.gaussian"(%arg11) {mean = 3.400000e-01 : f64, stddev = 2.500000e-01 : f64} : (f64) -> !hi_spn.probability - %14 = "hi_spn.product"(%10, %11, %12, %13) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability - %15 = "hi_spn.gaussian"(%arg12) {mean = 4.100000e-01 : f64, stddev = 1.000000e+00 : f64} : (f64) -> !hi_spn.probability - %16 = "hi_spn.gaussian"(%arg13) {mean = 4.200000e-01 : f64, stddev = 7.500000e-01 : f64} : (f64) -> !hi_spn.probability - %17 = "hi_spn.gaussian"(%arg14) {mean = 4.300000e-01 : f64, stddev = 5.000000e-01 : f64} : (f64) -> !hi_spn.probability - %18 = "hi_spn.gaussian"(%arg15) {mean = 4.400000e-01 : f64, stddev = 2.500000e-01 : f64} : (f64) -> !hi_spn.probability - %19 = "hi_spn.product"(%15, %16, %17, %18) : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability - %20 = "hi_spn.sum"(%4, %9, %14, %19) {weights = [2.500000e-01, 2.500000e-01, 2.500000e-01, 2.500000e-01]} : (!hi_spn.probability, !hi_spn.probability, !hi_spn.probability, !hi_spn.probability) -> !hi_spn.probability - "hi_spn.root"(%20) : (!hi_spn.probability) -> () - }) {numFeatures = 16 : ui32} : () -> () - }) {batchSize = 1 : ui32, errorModel = 1 : i32, inputType = f64, kernelName = "spn_vector", maxError = 2.000000e-02 : f64, numFeatures = 16 : ui32} : () -> () -} \ No newline at end of file diff --git a/mini-example_blockargs.mlir b/mini-example_blockargs.mlir deleted file mode 100644 index c9279581..00000000 --- a/mini-example_blockargs.mlir +++ /dev/null @@ -1,60 +0,0 @@ -module { - "hi_spn.joint_query"() ( { - "hi_spn.graph"() ( { - ^bb0(%arg0: f64, %arg1: f64, %arg2: f64, %arg3: f64, %arg4: f64, %arg5: f64, %arg6: f64, %arg7: f64, %arg8: f64, %arg9: f64, %arg10: f64, %arg11: f64, %arg12: f64, %arg13: f64, %arg14: f64, %arg15: f64): // no predecessors - %0 = "lo_spn.gaussian"(%arg0) {mean = 1.100000e-01 : f64, stddev = 1.000000e+00 : f64, supportMarginal = false} : (f64) -> f64 - %1 = "lo_spn.gaussian"(%arg1) {mean = 1.200000e-01 : f64, stddev = 7.500000e-01 : f64, supportMarginal = false} : (f64) -> f64 - %2 = "lo_spn.gaussian"(%arg2) {mean = 1.300000e-01 : f64, stddev = 5.000000e-01 : f64, supportMarginal = false} : (f64) -> f64 - %3 = "lo_spn.gaussian"(%arg3) {mean = 1.400000e-01 : f64, stddev = 2.500000e-01 : f64, supportMarginal = false} : (f64) -> f64 - %4 = "lo_spn.mul"(%0, %1) : (f64, f64) -> f64 - %5 = "lo_spn.mul"(%2, %3) : (f64, f64) -> f64 - %6 = "lo_spn.mul"(%4, %5) : (f64, f64) -> f64 - - %7 = "lo_spn.gaussian"(%arg4) {mean = 2.100000e-01 : f64, stddev = 1.000000e+00 : f64, supportMarginal = false} : (f64) -> f64 - %8 = "lo_spn.gaussian"(%arg5) {mean = 2.200000e-01 : f64, stddev = 7.500000e-01 : f64, supportMarginal = false} : (f64) -> f64 - %9 = "lo_spn.gaussian"(%arg6) {mean = 2.300000e-01 : f64, stddev = 5.000000e-01 : f64, supportMarginal = false} : (f64) -> f64 - %10 = "lo_spn.gaussian"(%arg7) {mean = 2.400000e-01 : f64, stddev = 2.500000e-01 : f64, supportMarginal = false} : (f64) -> f64 - %11 = "lo_spn.mul"(%7, %8) : (f64, f64) -> f64 - %12 = "lo_spn.mul"(%9, %10) : (f64, f64) -> f64 - %13 = "lo_spn.mul"(%11, %12) : (f64, f64) -> f64 - - %14 = "lo_spn.mul"(%arg8, %arg9) : (f64, f64) -> f64 - %15 = "lo_spn.mul"(%arg10, %arg11) : (f64, f64) -> f64 - %16 = "lo_spn.mul"(%14, %15) : (f64, f64) -> f64 - - %17 = "lo_spn.gaussian"(%arg12) {mean = 4.100000e-01 : f64, stddev = 1.000000e+00 : f64, supportMarginal = false} : (f64) -> f64 - %18 = "lo_spn.gaussian"(%arg13) {mean = 4.200000e-01 : f64, stddev = 7.500000e-01 : f64, supportMarginal = false} : (f64) -> f64 - %19 = "lo_spn.mul"(%arg14, %17) : (f64, f64) -> f64 - %20 = "lo_spn.mul"(%18, %arg15) : (f64, f64) -> f64 - %21 = "lo_spn.mul"(%19, %20) : (f64, f64) -> f64 - - - %22 = "lo_spn.constant"() {type = f64, value = 2.500000e-01 : f64} : () -> f64 - %23 = "lo_spn.mul"(%6, %22) : (f64, f64) -> f64 - - - %24 = "lo_spn.constant"() {type = f64, value = 2.500000e-01 : f64} : () -> f64 - %25 = "lo_spn.mul"(%13, %24) : (f64, f64) -> f64 - - %26 = "lo_spn.add"(%23, %25) : (f64, f64) -> f64 - - - %27 = "lo_spn.constant"() {type = f64, value = 2.500000e-01 : f64} : () -> f64 - %28 = "lo_spn.mul"(%16, %27) : (f64, f64) -> f64 - - - %29 = "lo_spn.constant"() {type = f64, value = 2.500000e-01 : f64} : () -> f64 - %30 = "lo_spn.mul"(%21, %29) : (f64, f64) -> f64 - - - %31 = "lo_spn.add"(%28, %30) : (f64, f64) -> f64 - - - %32 = "lo_spn.add"(%26, %31) : (f64, f64) -> f64 - - - %33 = "lo_spn.log"(%32) : (f64) -> f64 - "lo_spn.yield"(%33) : (f64) -> () - }) {numFeatures = 16 : ui32} : () -> () - }) {batchSize = 1 : ui32, errorModel = 1 : i32, inputType = f64, kernelName = "spn_vector", maxError = 2.000000e-02 : f64, numFeatures = 16 : ui32} : () -> () -} \ No newline at end of file diff --git a/mini-example_blockargs_only.mlir b/mini-example_blockargs_only.mlir deleted file mode 100644 index 215d8606..00000000 --- a/mini-example_blockargs_only.mlir +++ /dev/null @@ -1,50 +0,0 @@ -module { - "hi_spn.joint_query"() ( { - "hi_spn.graph"() ( { - ^bb0(%arg0: f64, %arg1: f64, %arg2: f64, %arg3: f64, %arg4: f64, %arg5: f64, %arg6: f64, %arg7: f64, %arg8: f64, %arg9: f64, %arg10: f64, %arg11: f64, %arg12: f64, %arg13: f64, %arg14: f64, %arg15: f64): // no predecessors - %4 = "lo_spn.mul"(%arg0, %arg1) : (f64, f64) -> f64 - %5 = "lo_spn.mul"(%arg2, %arg3) : (f64, f64) -> f64 - %6 = "lo_spn.mul"(%4, %5) : (f64, f64) -> f64 - - %11 = "lo_spn.mul"(%arg4, %arg5) : (f64, f64) -> f64 - %12 = "lo_spn.mul"(%arg6, %arg7) : (f64, f64) -> f64 - %13 = "lo_spn.mul"(%11, %12) : (f64, f64) -> f64 - - %14 = "lo_spn.mul"(%arg8, %arg9) : (f64, f64) -> f64 - %15 = "lo_spn.mul"(%arg10, %arg11) : (f64, f64) -> f64 - %16 = "lo_spn.mul"(%14, %15) : (f64, f64) -> f64 - - %19 = "lo_spn.mul"(%arg12, %arg13) : (f64, f64) -> f64 - %20 = "lo_spn.mul"(%arg14, %arg15) : (f64, f64) -> f64 - %21 = "lo_spn.mul"(%19, %20) : (f64, f64) -> f64 - - - %22 = "lo_spn.constant"() {type = f64, value = 2.500000e-01 : f64} : () -> f64 - %23 = "lo_spn.mul"(%6, %22) : (f64, f64) -> f64 - - - %24 = "lo_spn.constant"() {type = f64, value = 2.500000e-01 : f64} : () -> f64 - %25 = "lo_spn.mul"(%13, %24) : (f64, f64) -> f64 - - %26 = "lo_spn.add"(%23, %25) : (f64, f64) -> f64 - - - %27 = "lo_spn.constant"() {type = f64, value = 2.500000e-01 : f64} : () -> f64 - %28 = "lo_spn.mul"(%16, %27) : (f64, f64) -> f64 - - - %29 = "lo_spn.constant"() {type = f64, value = 2.500000e-01 : f64} : () -> f64 - %30 = "lo_spn.mul"(%21, %29) : (f64, f64) -> f64 - - - %31 = "lo_spn.add"(%28, %30) : (f64, f64) -> f64 - - - %32 = "lo_spn.add"(%26, %31) : (f64, f64) -> f64 - - - %33 = "lo_spn.log"(%32) : (f64) -> f64 - "lo_spn.yield"(%33) : (f64) -> () - }) {numFeatures = 16 : ui32} : () -> () - }) {batchSize = 1 : ui32, errorModel = 1 : i32, inputType = f64, kernelName = "spn_vector", maxError = 2.000000e-02 : f64, numFeatures = 16 : ui32} : () -> () -} \ No newline at end of file diff --git a/python-interface/test/examples/fashion-mnist/test_vector_fashion-mnist.py b/python-interface/test/examples/fashion-mnist/test_vector_fashion-mnist.py index 17df5d33..c3360b90 100644 --- a/python-interface/test/examples/fashion-mnist/test_vector_fashion-mnist.py +++ b/python-interface/test/examples/fashion-mnist/test_vector_fashion-mnist.py @@ -31,16 +31,18 @@ def test_vector_fashion_mnist(): dtype="float64") reference = reference.reshape(1000) # Compile the kernel. - compiler = CPUCompiler(vectorize=False, computeInLogSpace=False) + compiler = CPUCompiler(vectorize=True, computeInLogSpace=False) kernel = compiler.compile_ll(spn=spn, batchSize=1, supportMarginal=False) # Execute the compiled Kernel. - start = time.time() + time_sum = 0 for i in range(len(reference)): # Check the computation results against the reference + start = time.time() result = compiler.execute(kernel, inputs=np.array([inputs[i]])) + time_sum = time_sum + time.time() - start + print(f"evaluation #{i}: result: {result[0]:16.8f}, reference: {reference[i]:16.8f}", end='\r') assert np.isclose(result, reference[i]) - end = time.time() - print(f"Execution took {end - start} seconds.") + print(f"Execution took {time_sum} seconds.") if __name__ == "__main__": diff --git a/python-interface/test/examples/nltcs/test_vector_nltcs.py b/python-interface/test/examples/nltcs/test_vector_nltcs.py index 4072dad3..0d908f15 100644 --- a/python-interface/test/examples/nltcs/test_vector_nltcs.py +++ b/python-interface/test/examples/nltcs/test_vector_nltcs.py @@ -30,16 +30,18 @@ def test_vector_fashion_mnist(): dtype="float64") reference = reference.reshape(10000) # Compile the kernel. - compiler = CPUCompiler(vectorize=False, computeInLogSpace=False) + compiler = CPUCompiler(vectorize=True, computeInLogSpace=False) kernel = compiler.compile_ll(spn=spn, batchSize=1, supportMarginal=False) # Execute the compiled Kernel. - start = time.time() + time_sum = 0 for i in range(len(reference)): # Check the computation results against the reference + start = time.time() result = compiler.execute(kernel, inputs=np.array([inputs[i]])) + time_sum = time_sum + time.time() - start + print(f"evaluation #{i}: result: {result[0]:16.8f}, reference: {reference[i]:16.8f}", end='\r') assert np.isclose(result, reference[i]) - end = time.time() - print(f"Execution took {end - start} seconds.") + print(f"Execution took {time_sum} seconds.") if __name__ == "__main__": diff --git a/python-interface/test/examples/plants/input.csv b/python-interface/test/examples/plants/input.csv new file mode 100644 index 00000000..dbabd325 --- /dev/null +++ b/python-interface/test/examples/plants/input.csv @@ -0,0 +1,10000 @@ +0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,1,0,1,1,0,0,0,0,1,0,1,1,1,1,0,0,0,1,1,0,0,0,1,1,0,1,0,0,0,1,1,0,1,0,1,0,1,1,0,0,0,1,0,0,1,0,1,0,1,1,0,0 +0,1,0,1,0,0,1,1,0,0,0,1,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,1,1,0 +0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0 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