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[mlir] Replace toy::DeadFunctionEliminationPass with symbolDCEPass.
Summary: The dead function elimination pass in toy was a temporary stopgap until we had proper dead function elimination support in MLIR. Now that this functionality is available, this pass is no longer necessary. Differential Revision: https://reviews.llvm.org/D72483
1 parent f4261e1 commit 57540c9

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+30
-253
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‎mlir/examples/toy/Ch4/CMakeLists.txt

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@@ -13,7 +13,6 @@ add_toy_chapter(toyc-ch4
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parser/AST.cpp
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mlir/MLIRGen.cpp
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mlir/Dialect.cpp
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mlir/DeadFunctionEliminationPass.cpp
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mlir/ShapeInferencePass.cpp
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mlir/ToyCombine.cpp
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)

‎mlir/examples/toy/Ch4/include/toy/Passes.h

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@@ -20,7 +20,6 @@ class Pass;
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namespace toy {
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std::unique_ptr<Pass> createShapeInferencePass();
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std::unique_ptr<Pass> createDeadFunctionEliminationPass();
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} // end namespace toy
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} // end namespace mlir
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‎mlir/examples/toy/Ch4/mlir/DeadFunctionEliminationPass.cpp

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‎mlir/examples/toy/Ch4/mlir/MLIRGen.cpp

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@@ -170,6 +170,10 @@ class MLIRGenImpl {
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getType(VarType{})));
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}
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// If this function isn't main, then set the visibility to private.
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if (funcAST.getProto()->getName() != "main")
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function.setVisibility(mlir::FuncOp::Visibility::Private);
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return function;
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}
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‎mlir/examples/toy/Ch4/toyc.cpp

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@@ -119,7 +119,7 @@ int dumpMLIR() {
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// Inline all functions into main and then delete them.
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pm.addPass(mlir::createInlinerPass());
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pm.addPass(mlir::toy::createDeadFunctionEliminationPass());
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pm.addPass(mlir::createSymbolDCEPass());
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// Now that there is only one function, we can infer the shapes of each of
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// the operations.

‎mlir/examples/toy/Ch5/CMakeLists.txt

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@@ -13,7 +13,6 @@ add_toy_chapter(toyc-ch5
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parser/AST.cpp
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mlir/MLIRGen.cpp
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mlir/Dialect.cpp
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mlir/DeadFunctionEliminationPass.cpp
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mlir/LowerToAffineLoops.cpp
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mlir/ShapeInferencePass.cpp
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mlir/ToyCombine.cpp

‎mlir/examples/toy/Ch5/include/toy/Passes.h

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@@ -19,7 +19,6 @@ namespace mlir {
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class Pass;
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namespace toy {
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std::unique_ptr<Pass> createDeadFunctionEliminationPass();
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std::unique_ptr<Pass> createShapeInferencePass();
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/// Create a pass for lowering to operations in the `Affine` and `Std` dialects,

‎mlir/examples/toy/Ch5/mlir/DeadFunctionEliminationPass.cpp

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‎mlir/examples/toy/Ch5/mlir/MLIRGen.cpp

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@@ -170,6 +170,10 @@ class MLIRGenImpl {
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getType(VarType{})));
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}
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// If this function isn't main, then set the visibility to private.
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if (funcAST.getProto()->getName() != "main")
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function.setVisibility(mlir::FuncOp::Visibility::Private);
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return function;
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}
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‎mlir/examples/toy/Ch5/toyc.cpp

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@@ -124,7 +124,7 @@ int dumpMLIR() {
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if (enableOpt || isLoweringToAffine) {
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// Inline all functions into main and then delete them.
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pm.addPass(mlir::createInlinerPass());
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pm.addPass(mlir::toy::createDeadFunctionEliminationPass());
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pm.addPass(mlir::createSymbolDCEPass());
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// Now that there is only one function, we can infer the shapes of each of
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// the operations.

‎mlir/examples/toy/Ch6/CMakeLists.txt

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@@ -14,7 +14,6 @@ add_toy_chapter(toyc-ch6
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parser/AST.cpp
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mlir/MLIRGen.cpp
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mlir/Dialect.cpp
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mlir/DeadFunctionEliminationPass.cpp
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mlir/LowerToAffineLoops.cpp
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mlir/LowerToLLVM.cpp
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mlir/ShapeInferencePass.cpp

‎mlir/examples/toy/Ch6/include/toy/Passes.h

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@@ -19,7 +19,6 @@ namespace mlir {
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class Pass;
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namespace toy {
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std::unique_ptr<Pass> createDeadFunctionEliminationPass();
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std::unique_ptr<Pass> createShapeInferencePass();
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/// Create a pass for lowering to operations in the `Affine` and `Std` dialects,

‎mlir/examples/toy/Ch6/mlir/DeadFunctionEliminationPass.cpp

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‎mlir/examples/toy/Ch6/mlir/MLIRGen.cpp

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@@ -170,6 +170,10 @@ class MLIRGenImpl {
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getType(VarType{})));
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}
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// If this function isn't main, then set the visibility to private.
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if (funcAST.getProto()->getName() != "main")
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function.setVisibility(mlir::FuncOp::Visibility::Private);
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return function;
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}
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‎mlir/examples/toy/Ch6/toyc.cpp

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@@ -138,7 +138,7 @@ int loadAndProcessMLIR(mlir::MLIRContext &context,
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if (enableOpt || isLoweringToAffine) {
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// Inline all functions into main and then delete them.
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pm.addPass(mlir::createInlinerPass());
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pm.addPass(mlir::toy::createDeadFunctionEliminationPass());
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pm.addPass(mlir::createSymbolDCEPass());
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// Now that there is only one function, we can infer the shapes of each of
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// the operations.

‎mlir/examples/toy/Ch7/CMakeLists.txt

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@@ -14,7 +14,6 @@ add_toy_chapter(toyc-ch7
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parser/AST.cpp
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mlir/MLIRGen.cpp
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mlir/Dialect.cpp
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mlir/DeadFunctionEliminationPass.cpp
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mlir/LowerToAffineLoops.cpp
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mlir/LowerToLLVM.cpp
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mlir/ShapeInferencePass.cpp

‎mlir/examples/toy/Ch7/include/toy/Passes.h

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@@ -19,7 +19,6 @@ namespace mlir {
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class Pass;
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namespace toy {
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std::unique_ptr<Pass> createDeadFunctionEliminationPass();
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std::unique_ptr<Pass> createShapeInferencePass();
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/// Create a pass for lowering to operations in the `Affine` and `Std` dialects,

‎mlir/examples/toy/Ch7/mlir/DeadFunctionEliminationPass.cpp

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‎mlir/examples/toy/Ch7/mlir/MLIRGen.cpp

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@@ -223,6 +223,10 @@ class MLIRGenImpl {
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*returnOp.operand_type_begin()));
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}
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// If this function isn't main, then set the visibility to private.
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if (funcAST.getProto()->getName() != "main")
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function.setVisibility(mlir::FuncOp::Visibility::Private);
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return function;
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}
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‎mlir/examples/toy/Ch7/toyc.cpp

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@@ -138,7 +138,7 @@ int loadAndProcessMLIR(mlir::MLIRContext &context,
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if (enableOpt || isLoweringToAffine) {
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// Inline all functions into main and then delete them.
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pm.addPass(mlir::createInlinerPass());
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pm.addPass(mlir::toy::createDeadFunctionEliminationPass());
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pm.addPass(mlir::createSymbolDCEPass());
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// Now that there is only one function, we can infer the shapes of each of
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// the operations.

‎mlir/test/Examples/Toy/Ch4/shape_inference.mlir

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// Check the result of inlining+shape inference on an input module.
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func @multiply_transpose(%arg0: tensor<*xf64>, %arg1: tensor<*xf64>) -> tensor<*xf64> {
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func @multiply_transpose(%arg0: tensor<*xf64>, %arg1: tensor<*xf64>) -> tensor<*xf64>
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attributes { sym_visibility = "private" } {
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%0 = "toy.transpose"(%arg0) : (tensor<*xf64>) -> tensor<*xf64>
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%1 = "toy.transpose"(%arg1) : (tensor<*xf64>) -> tensor<*xf64>
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%2 = "toy.mul"(%0, %1) : (tensor<*xf64>, tensor<*xf64>) -> tensor<*xf64>

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