Convert the Canonicalize and CSE passes to generic Operation Passes.

This allows for them to be used on other non-function, or even other function-like, operations. The algorithms are already generic, so this is simply changing the derived pass type. The majority of this change is just ensuring that the nesting of these passes remains the same, as the pass manager won't auto-nest them anymore.

PiperOrigin-RevId: 276573038

4 files changed, 25 insertions, 18 deletions

diff --git a/mlir/examples/toy/Ch3/toyc.cpp b/mlir/examples/toy/Ch3/toyc.cpp
index 72e1c6f1966..7e62e13432c 100644
--- a/mlir/examples/toy/Ch3/toyc.cpp
+++ b/mlir/examples/toy/Ch3/toyc.cpp
@@ -124,7 +124,7 @@ int dumpMLIR() {
     applyPassManagerCLOptions(pm);
 
     // Add a run of the canonicalizer to optimize the mlir module.
-    pm.addPass(mlir::createCanonicalizerPass());
+    pm.addNestedPass<mlir::FuncOp>(mlir::createCanonicalizerPass());
     if (mlir::failed(pm.run(*module)))
       return 4;
   }
diff --git a/mlir/examples/toy/Ch4/toyc.cpp b/mlir/examples/toy/Ch4/toyc.cpp
index 3dd079d7798..d8e04d6ee89 100644
--- a/mlir/examples/toy/Ch4/toyc.cpp
+++ b/mlir/examples/toy/Ch4/toyc.cpp
@@ -130,9 +130,10 @@ int dumpMLIR() {
 
     // Now that there is only one function, we can infer the shapes of each of
     // the operations.
-    pm.addPass(mlir::toy::createShapeInferencePass());
-    pm.addPass(mlir::createCanonicalizerPass());
-    pm.addPass(mlir::createCSEPass());
+    mlir::OpPassManager &optPM = pm.nest<mlir::FuncOp>();
+    optPM.addPass(mlir::toy::createShapeInferencePass());
+    optPM.addPass(mlir::createCanonicalizerPass());
+    optPM.addPass(mlir::createCSEPass());
 
     if (mlir::failed(pm.run(*module)))
       return 4;
diff --git a/mlir/examples/toy/Ch5/toyc.cpp b/mlir/examples/toy/Ch5/toyc.cpp
index 9b47b43812f..54cbdf1405f 100644
--- a/mlir/examples/toy/Ch5/toyc.cpp
+++ b/mlir/examples/toy/Ch5/toyc.cpp
@@ -135,21 +135,24 @@ int dumpMLIR() {
 
     // Now that there is only one function, we can infer the shapes of each of
     // the operations.
-    pm.addPass(mlir::toy::createShapeInferencePass());
-    pm.addPass(mlir::createCanonicalizerPass());
-    pm.addPass(mlir::createCSEPass());
+    mlir::OpPassManager &optPM = pm.nest<mlir::FuncOp>();
+    optPM.addPass(mlir::toy::createShapeInferencePass());
+    optPM.addPass(mlir::createCanonicalizerPass());
+    optPM.addPass(mlir::createCSEPass());
   }
 
   if (isLoweringToAffine) {
     // Partially lower the toy dialect with a few cleanups afterwards.
     pm.addPass(mlir::toy::createLowerToAffinePass());
-    pm.addPass(mlir::createCanonicalizerPass());
-    pm.addPass(mlir::createCSEPass());
+
+    mlir::OpPassManager &optPM = pm.nest<mlir::FuncOp>();
+    optPM.addPass(mlir::createCanonicalizerPass());
+    optPM.addPass(mlir::createCSEPass());
 
     // Add optimizations if enabled.
     if (EnableOpt) {
-      pm.addPass(mlir::createLoopFusionPass());
-      pm.addPass(mlir::createMemRefDataFlowOptPass());
+      optPM.addPass(mlir::createLoopFusionPass());
+      optPM.addPass(mlir::createMemRefDataFlowOptPass());
     }
   }
 
diff --git a/mlir/examples/toy/Ch6/toyc.cpp b/mlir/examples/toy/Ch6/toyc.cpp
index 018dd0f8236..b7aed1b440c 100644
--- a/mlir/examples/toy/Ch6/toyc.cpp
+++ b/mlir/examples/toy/Ch6/toyc.cpp
@@ -149,21 +149,24 @@ int loadAndProcessMLIR(mlir::MLIRContext &context,
 
     // Now that there is only one function, we can infer the shapes of each of
     // the operations.
-    pm.addPass(mlir::toy::createShapeInferencePass());
-    pm.addPass(mlir::createCanonicalizerPass());
-    pm.addPass(mlir::createCSEPass());
+    mlir::OpPassManager &optPM = pm.nest<mlir::FuncOp>();
+    optPM.addPass(mlir::toy::createShapeInferencePass());
+    optPM.addPass(mlir::createCanonicalizerPass());
+    optPM.addPass(mlir::createCSEPass());
   }
 
   if (isLoweringToAffine) {
     // Partially lower the toy dialect with a few cleanups afterwards.
     pm.addPass(mlir::toy::createLowerToAffinePass());
-    pm.addPass(mlir::createCanonicalizerPass());
-    pm.addPass(mlir::createCSEPass());
+
+    mlir::OpPassManager &optPM = pm.nest<mlir::FuncOp>();
+    optPM.addPass(mlir::createCanonicalizerPass());
+    optPM.addPass(mlir::createCSEPass());
 
     // Add optimizations if enabled.
     if (EnableOpt) {
-      pm.addPass(mlir::createLoopFusionPass());
-      pm.addPass(mlir::createMemRefDataFlowOptPass());
+      optPM.addPass(mlir::createLoopFusionPass());
+      optPM.addPass(mlir::createMemRefDataFlowOptPass());
     }
   }