From 8a5aca204bb7ed1a0a05f14994274a70f732b3d6 Mon Sep 17 00:00:00 2001
From: Tzung-Han Juang <tzunghan.juang@gmail.com>
Date: Wed, 4 Sep 2024 15:04:36 -0400
Subject: [PATCH 01/17] Make OneShotModuleBufferize accept FunctionOpInterface
 and CallOpInterface

---
 .../Transforms/OneShotModuleBufferize.cpp     | 81 ++++++++++++-------
 1 file changed, 50 insertions(+), 31 deletions(-)

diff --git a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
index 0a4072605c265..2983af0fcbf3f 100644
--- a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
+++ b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
@@ -75,7 +75,7 @@ using namespace mlir::bufferization;
 using namespace mlir::bufferization::func_ext;
 
 /// A mapping of FuncOps to their callers.
-using FuncCallerMap = DenseMap<func::FuncOp, DenseSet<Operation *>>;
+using FuncCallerMap = DenseMap<FunctionOpInterface, DenseSet<Operation *>>;
 
 /// Get or create FuncAnalysisState.
 static FuncAnalysisState &
@@ -247,6 +247,15 @@ static func::FuncOp getCalledFunction(func::CallOp callOp) {
       SymbolTable::lookupNearestSymbolFrom(callOp, sym));
 }
 
+static FunctionOpInterface getCalledFunction(CallOpInterface callOp) {
+  SymbolRefAttr sym =
+      llvm::dyn_cast_if_present<SymbolRefAttr>(callOp.getCallableForCallee());
+  if (!sym)
+    return nullptr;
+  return dyn_cast_or_null<FunctionOpInterface>(
+      SymbolTable::lookupNearestSymbolFrom(callOp, sym));
+}
+
 /// Gather equivalence info of CallOps.
 /// Note: This only adds new equivalence info if the called function was already
 /// analyzed.
@@ -277,10 +286,10 @@ static void equivalenceAnalysis(func::FuncOp funcOp,
 }
 
 /// Return "true" if the given function signature has tensor semantics.
-static bool hasTensorSignature(func::FuncOp funcOp) {
-  return llvm::any_of(funcOp.getFunctionType().getInputs(),
+static bool hasTensorSignature(FunctionOpInterface funcOp) {
+  return llvm::any_of(funcOp.getArgumentTypes(),
                       llvm::IsaPred<TensorType>) ||
-         llvm::any_of(funcOp.getFunctionType().getResults(),
+         llvm::any_of(funcOp.getResultTypes(),
                       llvm::IsaPred<TensorType>);
 }
 
@@ -291,26 +300,30 @@ static bool hasTensorSignature(func::FuncOp funcOp) {
 /// retrieve the called FuncOp from any func::CallOp.
 static LogicalResult
 getFuncOpsOrderedByCalls(ModuleOp moduleOp,
-                         SmallVectorImpl<func::FuncOp> &orderedFuncOps,
+                         SmallVectorImpl<FunctionOpInterface> &orderedFuncOps,
                          FuncCallerMap &callerMap) {
   // For each FuncOp, the set of functions called by it (i.e. the union of
   // symbols of all nested func::CallOp).
-  DenseMap<func::FuncOp, DenseSet<func::FuncOp>> calledBy;
+  DenseMap<FunctionOpInterface, DenseSet<FunctionOpInterface>> calledBy;
   // For each FuncOp, the number of func::CallOp it contains.
-  DenseMap<func::FuncOp, unsigned> numberCallOpsContainedInFuncOp;
-  WalkResult res = moduleOp.walk([&](func::FuncOp funcOp) -> WalkResult {
-    if (!funcOp.getBody().empty()) {
-      func::ReturnOp returnOp = getAssumedUniqueReturnOp(funcOp);
-      if (!returnOp)
-        return funcOp->emitError()
-               << "cannot bufferize a FuncOp with tensors and "
-                  "without a unique ReturnOp";
+  DenseMap<FunctionOpInterface, unsigned> numberCallOpsContainedInFuncOp;
+  WalkResult res = moduleOp.walk([&](FunctionOpInterface funcOp) -> WalkResult {
+    // Only handle ReturnOp if funcOp is exactly the FuncOp type.
+    if(isa<FuncOp>(funcOp)) {
+      FuncOp funcOpCasted = cast<FuncOp>(funcOp);
+      if (!funcOpCasted.getBody().empty()) {
+        func::ReturnOp returnOp = getAssumedUniqueReturnOp(funcOpCasted);
+        if (!returnOp)
+          return funcOp->emitError()
+                 << "cannot bufferize a FuncOp with tensors and "
+                    "without a unique ReturnOp";
+      }
     }
 
     // Collect function calls and populate the caller map.
     numberCallOpsContainedInFuncOp[funcOp] = 0;
-    return funcOp.walk([&](func::CallOp callOp) -> WalkResult {
-      func::FuncOp calledFunction = getCalledFunction(callOp);
+    return funcOp.walk([&](CallOpInterface callOp) -> WalkResult {
+      FunctionOpInterface calledFunction = getCalledFunction(callOp);
       assert(calledFunction && "could not retrieved called func::FuncOp");
       // If the called function does not have any tensors in its signature, then
       // it is not necessary to bufferize the callee before the caller.
@@ -379,7 +392,7 @@ mlir::bufferization::analyzeModuleOp(ModuleOp moduleOp,
   FuncAnalysisState &funcState = getOrCreateFuncAnalysisState(state);
 
   // A list of functions in the order in which they are analyzed + bufferized.
-  SmallVector<func::FuncOp> orderedFuncOps;
+  SmallVector<FunctionOpInterface> orderedFuncOps;
 
   // A mapping of FuncOps to their callers.
   FuncCallerMap callerMap;
@@ -388,27 +401,33 @@ mlir::bufferization::analyzeModuleOp(ModuleOp moduleOp,
     return failure();
 
   // Analyze ops.
-  for (func::FuncOp funcOp : orderedFuncOps) {
-    if (!state.getOptions().isOpAllowed(funcOp))
+  for (FunctionOpInterface funcOp : orderedFuncOps) {
+
+    // The following analysis is specific to the FuncOp type.
+    if(!isa<FuncOp>(funcOp))
+        continue;
+    FuncOp funcOpCasted = cast<func::FuncOp>(funcOp);
+
+    if (!state.getOptions().isOpAllowed(funcOpCasted))
       continue;
 
     // Now analyzing function.
-    funcState.startFunctionAnalysis(funcOp);
+    funcState.startFunctionAnalysis(funcOpCasted);
 
     // Gather equivalence info for CallOps.
-    equivalenceAnalysis(funcOp, state, funcState);
+    equivalenceAnalysis(funcOpCasted, state, funcState);
 
     // Analyze funcOp.
-    if (failed(analyzeOp(funcOp, state, statistics)))
+    if (failed(analyzeOp(funcOpCasted, state, statistics)))
       return failure();
 
     // Run some extra function analyses.
-    if (failed(aliasingFuncOpBBArgsAnalysis(funcOp, state, funcState)) ||
-        failed(funcOpBbArgReadWriteAnalysis(funcOp, state, funcState)))
+    if (failed(aliasingFuncOpBBArgsAnalysis(funcOpCasted, state, funcState)) ||
+        failed(funcOpBbArgReadWriteAnalysis(funcOpCasted, state, funcState)))
       return failure();
 
     // Mark op as fully analyzed.
-    funcState.analyzedFuncOps[funcOp] = FuncOpAnalysisState::Analyzed;
+    funcState.analyzedFuncOps[funcOpCasted] = FuncOpAnalysisState::Analyzed;
   }
 
   return success();
@@ -430,20 +449,20 @@ LogicalResult mlir::bufferization::bufferizeModuleOp(
   IRRewriter rewriter(moduleOp.getContext());
 
   // A list of functions in the order in which they are analyzed + bufferized.
-  SmallVector<func::FuncOp> orderedFuncOps;
+  SmallVector<FunctionOpInterface> orderedFuncOps;
 
   // A mapping of FuncOps to their callers.
   FuncCallerMap callerMap;
 
   if (failed(getFuncOpsOrderedByCalls(moduleOp, orderedFuncOps, callerMap)))
     return failure();
+  SmallVector<FunctionOpInterface> ops;
 
   // Bufferize functions.
-  for (func::FuncOp funcOp : orderedFuncOps) {
+  for (FunctionOpInterface funcOp : orderedFuncOps) {
     // Note: It would be good to apply cleanups here but we cannot as aliasInfo
     // would be invalidated.
-
-    if (llvm::is_contained(options.noAnalysisFuncFilter, funcOp.getSymName())) {
+    if (llvm::is_contained(options.noAnalysisFuncFilter, funcOp.getName())) {
       // This function was not analyzed and RaW conflicts were not resolved.
       // Buffer copies must be inserted before every write.
       OneShotBufferizationOptions updatedOptions = options;
@@ -456,8 +475,8 @@ LogicalResult mlir::bufferization::bufferizeModuleOp(
     }
 
     // Change buffer return types to more precise layout maps.
-    if (options.inferFunctionResultLayout)
-      foldMemRefCasts(funcOp);
+    if (options.inferFunctionResultLayout && isa<func::FuncOp>(funcOp))
+      foldMemRefCasts(cast<func::FuncOp>(funcOp));
   }
 
   // Bufferize all other ops.

From 5153af3ee72d4322273b1614a6637a952b10cdcc Mon Sep 17 00:00:00 2001
From: Tzung-Han Juang <tzunghan.juang@gmail.com>
Date: Wed, 4 Sep 2024 15:42:08 -0400
Subject: [PATCH 02/17] Cleanup

---
 .../Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
index 2983af0fcbf3f..5231fe8605537 100644
--- a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
+++ b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
@@ -456,12 +456,12 @@ LogicalResult mlir::bufferization::bufferizeModuleOp(
 
   if (failed(getFuncOpsOrderedByCalls(moduleOp, orderedFuncOps, callerMap)))
     return failure();
-  SmallVector<FunctionOpInterface> ops;
 
   // Bufferize functions.
   for (FunctionOpInterface funcOp : orderedFuncOps) {
     // Note: It would be good to apply cleanups here but we cannot as aliasInfo
     // would be invalidated.
+
     if (llvm::is_contained(options.noAnalysisFuncFilter, funcOp.getName())) {
       // This function was not analyzed and RaW conflicts were not resolved.
       // Buffer copies must be inserted before every write.

From 1f8d847077716be2f0115c4fadcb7c2d4eafe945 Mon Sep 17 00:00:00 2001
From: Tzung-Han Juang <tzunghan.juang@gmail.com>
Date: Fri, 6 Sep 2024 10:37:18 -0400
Subject: [PATCH 03/17] Make getAssumedUniqueReturnOp detect ReturnLike and
 FuncAnalysisState use FunctionOpInterface

---
 .../FuncBufferizableOpInterfaceImpl.h         |  12 +-
 .../FuncBufferizableOpInterfaceImpl.cpp       |   2 +-
 .../Transforms/OneShotModuleBufferize.cpp     | 117 ++++++++----------
 3 files changed, 59 insertions(+), 72 deletions(-)

diff --git a/mlir/include/mlir/Dialect/Bufferization/Transforms/FuncBufferizableOpInterfaceImpl.h b/mlir/include/mlir/Dialect/Bufferization/Transforms/FuncBufferizableOpInterfaceImpl.h
index 0b91d3d675b7c..8bed0dfc5814b 100644
--- a/mlir/include/mlir/Dialect/Bufferization/Transforms/FuncBufferizableOpInterfaceImpl.h
+++ b/mlir/include/mlir/Dialect/Bufferization/Transforms/FuncBufferizableOpInterfaceImpl.h
@@ -50,24 +50,24 @@ struct FuncAnalysisState : public OneShotAnalysisState::Extension {
 
   /// A mapping of ReturnOp OpOperand indices to equivalent FuncOp BBArg
   /// indices.
-  DenseMap<FuncOp, IndexMapping> equivalentFuncArgs;
+  DenseMap<FunctionOpInterface, IndexMapping> equivalentFuncArgs;
 
   /// A mapping of FuncOp BBArg indices to aliasing ReturnOp OpOperand indices.
-  DenseMap<FuncOp, IndexToIndexListMapping> aliasingReturnVals;
+  DenseMap<FunctionOpInterface, IndexToIndexListMapping> aliasingReturnVals;
 
   /// A set of all read BlockArguments of FuncOps.
-  DenseMap<FuncOp, BbArgIndexSet> readBbArgs;
+  DenseMap<FunctionOpInterface, BbArgIndexSet> readBbArgs;
 
   /// A set of all written-to BlockArguments of FuncOps.
-  DenseMap<FuncOp, BbArgIndexSet> writtenBbArgs;
+  DenseMap<FunctionOpInterface, BbArgIndexSet> writtenBbArgs;
 
   /// Keep track of which FuncOps are fully analyzed or currently being
   /// analyzed.
-  DenseMap<FuncOp, FuncOpAnalysisState> analyzedFuncOps;
+  DenseMap<FunctionOpInterface, FuncOpAnalysisState> analyzedFuncOps;
 
   /// This function is called right before analyzing the given FuncOp. It
   /// initializes the data structures for the FuncOp in this state object.
-  void startFunctionAnalysis(FuncOp funcOp);
+  void startFunctionAnalysis(FunctionOpInterface funcOp);
 };
 
 void registerBufferizableOpInterfaceExternalModels(DialectRegistry &registry);
diff --git a/mlir/lib/Dialect/Bufferization/Transforms/FuncBufferizableOpInterfaceImpl.cpp b/mlir/lib/Dialect/Bufferization/Transforms/FuncBufferizableOpInterfaceImpl.cpp
index 9fbe574ec392d..9749a71f3514b 100644
--- a/mlir/lib/Dialect/Bufferization/Transforms/FuncBufferizableOpInterfaceImpl.cpp
+++ b/mlir/lib/Dialect/Bufferization/Transforms/FuncBufferizableOpInterfaceImpl.cpp
@@ -22,7 +22,7 @@ namespace mlir {
 namespace bufferization {
 namespace func_ext {
 
-void FuncAnalysisState::startFunctionAnalysis(FuncOp funcOp) {
+void FuncAnalysisState::startFunctionAnalysis(FunctionOpInterface funcOp) {
   analyzedFuncOps[funcOp] = FuncOpAnalysisState::InProgress;
   auto createdEquiv = equivalentFuncArgs.try_emplace(funcOp, IndexMapping());
   auto createdAliasingResults =
diff --git a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
index 5231fe8605537..cfb87aef6e64b 100644
--- a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
+++ b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
@@ -88,10 +88,11 @@ getOrCreateFuncAnalysisState(OneShotAnalysisState &state) {
 
 /// Return the unique ReturnOp that terminates `funcOp`.
 /// Return nullptr if there is no such unique ReturnOp.
-static func::ReturnOp getAssumedUniqueReturnOp(func::FuncOp funcOp) {
-  func::ReturnOp returnOp;
-  for (Block &b : funcOp.getBody()) {
-    if (auto candidateOp = dyn_cast<func::ReturnOp>(b.getTerminator())) {
+static Operation* getAssumedUniqueReturnOp(FunctionOpInterface funcOp) {
+  Operation *returnOp = nullptr;
+  for (Block &b : funcOp.getFunctionBody()) {
+    auto candidateOp = b.getTerminator();
+    if (candidateOp && candidateOp->hasTrait<OpTrait::ReturnLike>()) {
       if (returnOp)
         return nullptr;
       returnOp = candidateOp;
@@ -126,16 +127,15 @@ static void annotateEquivalentReturnBbArg(OpOperand &returnVal,
 /// Store function BlockArguments that are equivalent to/aliasing a returned
 /// value in FuncAnalysisState.
 static LogicalResult
-aliasingFuncOpBBArgsAnalysis(FuncOp funcOp, OneShotAnalysisState &state,
+aliasingFuncOpBBArgsAnalysis(FunctionOpInterface funcOp, OneShotAnalysisState &state,
                              FuncAnalysisState &funcState) {
-  if (funcOp.getBody().empty()) {
+  if (funcOp.getFunctionBody().empty()) {
     // No function body available. Conservatively assume that every tensor
     // return value may alias with any tensor bbArg.
-    FunctionType type = funcOp.getFunctionType();
-    for (const auto &inputIt : llvm::enumerate(type.getInputs())) {
+    for (const auto &inputIt : llvm::enumerate(funcOp.getArgumentTypes())) {
       if (!isa<TensorType>(inputIt.value()))
         continue;
-      for (const auto &resultIt : llvm::enumerate(type.getResults())) {
+      for (const auto &resultIt : llvm::enumerate(funcOp.getResultTypes())) {
         if (!isa<TensorType>(resultIt.value()))
           continue;
         int64_t returnIdx = resultIt.index();
@@ -147,7 +147,9 @@ aliasingFuncOpBBArgsAnalysis(FuncOp funcOp, OneShotAnalysisState &state,
   }
 
   // Support only single return-terminated block in the function.
-  func::ReturnOp returnOp = getAssumedUniqueReturnOp(funcOp);
+  if (!isa<func::FuncOp>(funcOp))
+    return success();
+  Operation *returnOp = getAssumedUniqueReturnOp(funcOp);
   assert(returnOp && "expected func with single return op");
 
   for (OpOperand &returnVal : returnOp->getOpOperands())
@@ -168,7 +170,7 @@ aliasingFuncOpBBArgsAnalysis(FuncOp funcOp, OneShotAnalysisState &state,
   return success();
 }
 
-static void annotateFuncArgAccess(func::FuncOp funcOp, int64_t idx, bool isRead,
+static void annotateFuncArgAccess(FunctionOpInterface funcOp, int64_t idx, bool isRead,
                                   bool isWritten) {
   OpBuilder b(funcOp.getContext());
   Attribute accessType;
@@ -189,12 +191,12 @@ static void annotateFuncArgAccess(func::FuncOp funcOp, int64_t idx, bool isRead,
 /// function with unknown ops, we conservatively assume that such ops bufferize
 /// to a read + write.
 static LogicalResult
-funcOpBbArgReadWriteAnalysis(FuncOp funcOp, OneShotAnalysisState &state,
+funcOpBbArgReadWriteAnalysis(FunctionOpInterface funcOp, OneShotAnalysisState &state,
                              FuncAnalysisState &funcState) {
-  for (int64_t idx = 0, e = funcOp.getFunctionType().getNumInputs(); idx < e;
+  for (int64_t idx = 0, e = funcOp.getNumArguments(); idx < e;
        ++idx) {
     // Skip non-tensor arguments.
-    if (!isa<TensorType>(funcOp.getFunctionType().getInput(idx)))
+    if (!isa<TensorType>(funcOp.getArgumentTypes()[idx]))
       continue;
     bool isRead;
     bool isWritten;
@@ -204,7 +206,7 @@ funcOpBbArgReadWriteAnalysis(FuncOp funcOp, OneShotAnalysisState &state,
       StringRef str = accessAttr.getValue();
       isRead = str == "read" || str == "read-write";
       isWritten = str == "write" || str == "read-write";
-    } else if (funcOp.getBody().empty()) {
+    } else if (funcOp.getFunctionBody().empty()) {
       // If the function has no body, conservatively assume that all args are
       // read + written.
       isRead = true;
@@ -230,23 +232,13 @@ funcOpBbArgReadWriteAnalysis(FuncOp funcOp, OneShotAnalysisState &state,
 
 /// Remove bufferization attributes on FuncOp arguments.
 static void removeBufferizationAttributes(BlockArgument bbArg) {
-  auto funcOp = cast<func::FuncOp>(bbArg.getOwner()->getParentOp());
+  auto funcOp = cast<FunctionOpInterface>(bbArg.getOwner()->getParentOp());
   funcOp.removeArgAttr(bbArg.getArgNumber(),
                        BufferizationDialect::kBufferLayoutAttrName);
   funcOp.removeArgAttr(bbArg.getArgNumber(),
                        BufferizationDialect::kWritableAttrName);
 }
 
-/// Return the func::FuncOp called by `callOp`.
-static func::FuncOp getCalledFunction(func::CallOp callOp) {
-  SymbolRefAttr sym =
-      llvm::dyn_cast_if_present<SymbolRefAttr>(callOp.getCallableForCallee());
-  if (!sym)
-    return nullptr;
-  return dyn_cast_or_null<func::FuncOp>(
-      SymbolTable::lookupNearestSymbolFrom(callOp, sym));
-}
-
 static FunctionOpInterface getCalledFunction(CallOpInterface callOp) {
   SymbolRefAttr sym =
       llvm::dyn_cast_if_present<SymbolRefAttr>(callOp.getCallableForCallee());
@@ -260,12 +252,12 @@ static FunctionOpInterface getCalledFunction(CallOpInterface callOp) {
 /// Note: This only adds new equivalence info if the called function was already
 /// analyzed.
 // TODO: This does not handle cyclic function call graphs etc.
-static void equivalenceAnalysis(func::FuncOp funcOp,
+static void equivalenceAnalysis(FunctionOpInterface funcOp,
                                 OneShotAnalysisState &state,
                                 FuncAnalysisState &funcState) {
-  funcOp->walk([&](func::CallOp callOp) {
-    func::FuncOp calledFunction = getCalledFunction(callOp);
-    assert(calledFunction && "could not retrieved called func::FuncOp");
+  funcOp->walk([&](CallOpInterface callOp) {
+    FunctionOpInterface calledFunction = getCalledFunction(callOp);
+    assert(calledFunction && "could not retrieved called FunctionOpInterface");
 
     // No equivalence info available for the called function.
     if (!funcState.equivalentFuncArgs.count(calledFunction))
@@ -276,7 +268,7 @@ static void equivalenceAnalysis(func::FuncOp funcOp,
       int64_t bbargIdx = it.second;
       if (!state.isInPlace(callOp->getOpOperand(bbargIdx)))
         continue;
-      Value returnVal = callOp.getResult(returnIdx);
+      Value returnVal = callOp->getResult(returnIdx);
       Value argVal = callOp->getOperand(bbargIdx);
       state.unionEquivalenceClasses(returnVal, argVal);
     }
@@ -308,23 +300,19 @@ getFuncOpsOrderedByCalls(ModuleOp moduleOp,
   // For each FuncOp, the number of func::CallOp it contains.
   DenseMap<FunctionOpInterface, unsigned> numberCallOpsContainedInFuncOp;
   WalkResult res = moduleOp.walk([&](FunctionOpInterface funcOp) -> WalkResult {
-    // Only handle ReturnOp if funcOp is exactly the FuncOp type.
-    if(isa<FuncOp>(funcOp)) {
-      FuncOp funcOpCasted = cast<FuncOp>(funcOp);
-      if (!funcOpCasted.getBody().empty()) {
-        func::ReturnOp returnOp = getAssumedUniqueReturnOp(funcOpCasted);
-        if (!returnOp)
-          return funcOp->emitError()
-                 << "cannot bufferize a FuncOp with tensors and "
-                    "without a unique ReturnOp";
-      }
+    if (!funcOp.getFunctionBody().empty() && isa<func::FuncOp>(funcOp)) {
+      Operation *returnOp = getAssumedUniqueReturnOp(funcOp);
+      if (!returnOp)
+        return funcOp->emitError()
+               << "cannot bufferize a FuncOp with tensors and "
+                  "without a unique ReturnOp";
     }
 
     // Collect function calls and populate the caller map.
     numberCallOpsContainedInFuncOp[funcOp] = 0;
     return funcOp.walk([&](CallOpInterface callOp) -> WalkResult {
       FunctionOpInterface calledFunction = getCalledFunction(callOp);
-      assert(calledFunction && "could not retrieved called func::FuncOp");
+      assert(calledFunction && "could not retrieved called FunctionOpInterface");
       // If the called function does not have any tensors in its signature, then
       // it is not necessary to bufferize the callee before the caller.
       if (!hasTensorSignature(calledFunction))
@@ -362,11 +350,15 @@ getFuncOpsOrderedByCalls(ModuleOp moduleOp,
 /// most generic layout map as function return types. After bufferizing the
 /// entire function body, a more concise memref type can potentially be used for
 /// the return type of the function.
-static void foldMemRefCasts(func::FuncOp funcOp) {
-  if (funcOp.getBody().empty())
+static void foldMemRefCasts(FunctionOpInterface funcOp) {
+  if (funcOp.getFunctionBody().empty())
+    return;
+
+  Operation *returnOp = getAssumedUniqueReturnOp(funcOp);
+
+  if (!returnOp)
     return;
 
-  func::ReturnOp returnOp = getAssumedUniqueReturnOp(funcOp);
   SmallVector<Type> resultTypes;
 
   for (OpOperand &operand : returnOp->getOpOperands()) {
@@ -379,7 +371,7 @@ static void foldMemRefCasts(func::FuncOp funcOp) {
   }
 
   auto newFuncType = FunctionType::get(
-      funcOp.getContext(), funcOp.getFunctionType().getInputs(), resultTypes);
+      funcOp.getContext(), funcOp.getArgumentTypes(), resultTypes);
   funcOp.setType(newFuncType);
 }
 
@@ -403,31 +395,26 @@ mlir::bufferization::analyzeModuleOp(ModuleOp moduleOp,
   // Analyze ops.
   for (FunctionOpInterface funcOp : orderedFuncOps) {
 
-    // The following analysis is specific to the FuncOp type.
-    if(!isa<FuncOp>(funcOp))
-        continue;
-    FuncOp funcOpCasted = cast<func::FuncOp>(funcOp);
-
-    if (!state.getOptions().isOpAllowed(funcOpCasted))
+    if (!state.getOptions().isOpAllowed(funcOp))
       continue;
 
     // Now analyzing function.
-    funcState.startFunctionAnalysis(funcOpCasted);
+    funcState.startFunctionAnalysis(funcOp);
 
     // Gather equivalence info for CallOps.
-    equivalenceAnalysis(funcOpCasted, state, funcState);
+    equivalenceAnalysis(funcOp, state, funcState);
 
     // Analyze funcOp.
-    if (failed(analyzeOp(funcOpCasted, state, statistics)))
+    if (failed(analyzeOp(funcOp, state, statistics)))
       return failure();
 
     // Run some extra function analyses.
-    if (failed(aliasingFuncOpBBArgsAnalysis(funcOpCasted, state, funcState)) ||
-        failed(funcOpBbArgReadWriteAnalysis(funcOpCasted, state, funcState)))
+    if (failed(aliasingFuncOpBBArgsAnalysis(funcOp, state, funcState)) ||
+        failed(funcOpBbArgReadWriteAnalysis(funcOp, state, funcState)))
       return failure();
 
     // Mark op as fully analyzed.
-    funcState.analyzedFuncOps[funcOpCasted] = FuncOpAnalysisState::Analyzed;
+    funcState.analyzedFuncOps[funcOp] = FuncOpAnalysisState::Analyzed;
   }
 
   return success();
@@ -435,7 +422,7 @@ mlir::bufferization::analyzeModuleOp(ModuleOp moduleOp,
 
 void mlir::bufferization::removeBufferizationAttributesInModule(
     ModuleOp moduleOp) {
-  moduleOp.walk([&](func::FuncOp op) {
+  moduleOp.walk([&](FunctionOpInterface op) {
     for (BlockArgument bbArg : op.getArguments())
       removeBufferizationAttributes(bbArg);
   });
@@ -475,14 +462,14 @@ LogicalResult mlir::bufferization::bufferizeModuleOp(
     }
 
     // Change buffer return types to more precise layout maps.
-    if (options.inferFunctionResultLayout && isa<func::FuncOp>(funcOp))
-      foldMemRefCasts(cast<func::FuncOp>(funcOp));
+    if (options.inferFunctionResultLayout)
+      foldMemRefCasts(funcOp);
   }
 
   // Bufferize all other ops.
   for (Operation &op : llvm::make_early_inc_range(moduleOp.getOps())) {
     // Functions were already bufferized.
-    if (isa<func::FuncOp>(&op))
+    if (isa<FunctionOpInterface>(&op))
       continue;
     if (failed(bufferizeOp(&op, options, statistics)))
       return failure();
@@ -509,12 +496,12 @@ LogicalResult mlir::bufferization::runOneShotModuleBufferize(
       // FuncOps whose names are specified in options.noAnalysisFuncFilter will
       // not be analyzed. Ops in these FuncOps will not be analyzed as well.
       OpFilter::Entry::FilterFn analysisFilterFn = [=](Operation *op) {
-        auto func = dyn_cast<func::FuncOp>(op);
+        auto func = dyn_cast<FunctionOpInterface>(op);
         if (!func)
-          func = op->getParentOfType<func::FuncOp>();
+          func = op->getParentOfType<FunctionOpInterface>();
         if (func)
           return llvm::is_contained(options.noAnalysisFuncFilter,
-                                    func.getSymName());
+                                    func.getName());
         return false;
       };
       OneShotBufferizationOptions updatedOptions(options);

From 26e69ad35197b7c1b7a2084810b714898af2aeb7 Mon Sep 17 00:00:00 2001
From: Tzung-Han Juang <tzunghan.juang@gmail.com>
Date: Fri, 6 Sep 2024 10:56:50 -0400
Subject: [PATCH 04/17] Make getAssumedUniqueReturnOp return funcOp if there is
 no return

---
 .../Transforms/OneShotModuleBufferize.cpp          | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
index cfb87aef6e64b..bd054ac4e7b87 100644
--- a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
+++ b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
@@ -88,6 +88,7 @@ getOrCreateFuncAnalysisState(OneShotAnalysisState &state) {
 
 /// Return the unique ReturnOp that terminates `funcOp`.
 /// Return nullptr if there is no such unique ReturnOp.
+/// Return `funcOp` it self if there is no ReturnOp.
 static Operation* getAssumedUniqueReturnOp(FunctionOpInterface funcOp) {
   Operation *returnOp = nullptr;
   for (Block &b : funcOp.getFunctionBody()) {
@@ -98,6 +99,8 @@ static Operation* getAssumedUniqueReturnOp(FunctionOpInterface funcOp) {
       returnOp = candidateOp;
     }
   }
+  if (!returnOp)
+    return funcOp;
   return returnOp;
 }
 
@@ -147,9 +150,10 @@ aliasingFuncOpBBArgsAnalysis(FunctionOpInterface funcOp, OneShotAnalysisState &s
   }
 
   // Support only single return-terminated block in the function.
-  if (!isa<func::FuncOp>(funcOp))
-    return success();
+  // If funcOp has no returnOp, skip the following analysis.
   Operation *returnOp = getAssumedUniqueReturnOp(funcOp);
+  if (returnOp == funcOp)
+    return success();
   assert(returnOp && "expected func with single return op");
 
   for (OpOperand &returnVal : returnOp->getOpOperands())
@@ -300,9 +304,9 @@ getFuncOpsOrderedByCalls(ModuleOp moduleOp,
   // For each FuncOp, the number of func::CallOp it contains.
   DenseMap<FunctionOpInterface, unsigned> numberCallOpsContainedInFuncOp;
   WalkResult res = moduleOp.walk([&](FunctionOpInterface funcOp) -> WalkResult {
-    if (!funcOp.getFunctionBody().empty() && isa<func::FuncOp>(funcOp)) {
+    if (!funcOp.getFunctionBody().empty()) {
       Operation *returnOp = getAssumedUniqueReturnOp(funcOp);
-      if (!returnOp)
+      if (!returnOp && returnOp != funcOp)
         return funcOp->emitError()
                << "cannot bufferize a FuncOp with tensors and "
                   "without a unique ReturnOp";
@@ -356,7 +360,7 @@ static void foldMemRefCasts(FunctionOpInterface funcOp) {
 
   Operation *returnOp = getAssumedUniqueReturnOp(funcOp);
 
-  if (!returnOp)
+  if (!returnOp || returnOp == funcOp)
     return;
 
   SmallVector<Type> resultTypes;

From 074192ca0e62ba600f63de4e914d44fb4bf86ffb Mon Sep 17 00:00:00 2001
From: Tzung-Han Juang <tzunghan.juang@gmail.com>
Date: Fri, 6 Sep 2024 14:35:56 -0400
Subject: [PATCH 05/17] Use getNumResults to guard functions without any return
 type

---
 .../Transforms/OneShotModuleBufferize.cpp     | 19 ++++---------------
 1 file changed, 4 insertions(+), 15 deletions(-)

diff --git a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
index bd054ac4e7b87..6933fde7f9565 100644
--- a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
+++ b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
@@ -88,7 +88,6 @@ getOrCreateFuncAnalysisState(OneShotAnalysisState &state) {
 
 /// Return the unique ReturnOp that terminates `funcOp`.
 /// Return nullptr if there is no such unique ReturnOp.
-/// Return `funcOp` it self if there is no ReturnOp.
 static Operation* getAssumedUniqueReturnOp(FunctionOpInterface funcOp) {
   Operation *returnOp = nullptr;
   for (Block &b : funcOp.getFunctionBody()) {
@@ -99,8 +98,6 @@ static Operation* getAssumedUniqueReturnOp(FunctionOpInterface funcOp) {
       returnOp = candidateOp;
     }
   }
-  if (!returnOp)
-    return funcOp;
   return returnOp;
 }
 
@@ -132,7 +129,7 @@ static void annotateEquivalentReturnBbArg(OpOperand &returnVal,
 static LogicalResult
 aliasingFuncOpBBArgsAnalysis(FunctionOpInterface funcOp, OneShotAnalysisState &state,
                              FuncAnalysisState &funcState) {
-  if (funcOp.getFunctionBody().empty()) {
+  if (funcOp.getFunctionBody().empty() || funcOp.getNumResults() == 0) {
     // No function body available. Conservatively assume that every tensor
     // return value may alias with any tensor bbArg.
     for (const auto &inputIt : llvm::enumerate(funcOp.getArgumentTypes())) {
@@ -150,10 +147,7 @@ aliasingFuncOpBBArgsAnalysis(FunctionOpInterface funcOp, OneShotAnalysisState &s
   }
 
   // Support only single return-terminated block in the function.
-  // If funcOp has no returnOp, skip the following analysis.
   Operation *returnOp = getAssumedUniqueReturnOp(funcOp);
-  if (returnOp == funcOp)
-    return success();
   assert(returnOp && "expected func with single return op");
 
   for (OpOperand &returnVal : returnOp->getOpOperands())
@@ -304,9 +298,9 @@ getFuncOpsOrderedByCalls(ModuleOp moduleOp,
   // For each FuncOp, the number of func::CallOp it contains.
   DenseMap<FunctionOpInterface, unsigned> numberCallOpsContainedInFuncOp;
   WalkResult res = moduleOp.walk([&](FunctionOpInterface funcOp) -> WalkResult {
-    if (!funcOp.getFunctionBody().empty()) {
+    if (!funcOp.getFunctionBody().empty() && funcOp.getNumResults() != 0) {
       Operation *returnOp = getAssumedUniqueReturnOp(funcOp);
-      if (!returnOp && returnOp != funcOp)
+      if (!returnOp)
         return funcOp->emitError()
                << "cannot bufferize a FuncOp with tensors and "
                   "without a unique ReturnOp";
@@ -355,14 +349,10 @@ getFuncOpsOrderedByCalls(ModuleOp moduleOp,
 /// entire function body, a more concise memref type can potentially be used for
 /// the return type of the function.
 static void foldMemRefCasts(FunctionOpInterface funcOp) {
-  if (funcOp.getFunctionBody().empty())
+  if (funcOp.getFunctionBody().empty() || funcOp.getNumResults() == 0)
     return;
 
   Operation *returnOp = getAssumedUniqueReturnOp(funcOp);
-
-  if (!returnOp || returnOp == funcOp)
-    return;
-
   SmallVector<Type> resultTypes;
 
   for (OpOperand &operand : returnOp->getOpOperands()) {
@@ -398,7 +388,6 @@ mlir::bufferization::analyzeModuleOp(ModuleOp moduleOp,
 
   // Analyze ops.
   for (FunctionOpInterface funcOp : orderedFuncOps) {
-
     if (!state.getOptions().isOpAllowed(funcOp))
       continue;
 

From 4ba535b93e607698f3319cc5d13a3432fb0c67c4 Mon Sep 17 00:00:00 2001
From: Tzung-Han Juang <tzunghan.juang@xanadu.ai>
Date: Tue, 10 Sep 2024 14:30:18 -0400
Subject: [PATCH 06/17] Update
 mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp

Co-authored-by: erick-xanadu <110487834+erick-xanadu@users.noreply.github.com>
---
 .../Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
index 6933fde7f9565..bf29b7e86a46d 100644
--- a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
+++ b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
@@ -349,7 +349,7 @@ getFuncOpsOrderedByCalls(ModuleOp moduleOp,
 /// entire function body, a more concise memref type can potentially be used for
 /// the return type of the function.
 static void foldMemRefCasts(FunctionOpInterface funcOp) {
-  if (funcOp.getFunctionBody().empty() || funcOp.getNumResults() == 0)
+  if (funcOp.getFunctionBody().empty())
     return;
 
   Operation *returnOp = getAssumedUniqueReturnOp(funcOp);

From caa69cdfcde278bda7da41b78c668610e8a6c519 Mon Sep 17 00:00:00 2001
From: Tzung-Han Juang <tzunghan.juang@xanadu.ai>
Date: Tue, 10 Sep 2024 14:30:31 -0400
Subject: [PATCH 07/17] Update
 mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp

Co-authored-by: erick-xanadu <110487834+erick-xanadu@users.noreply.github.com>
---
 .../Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
index bf29b7e86a46d..67323715ee424 100644
--- a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
+++ b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
@@ -129,7 +129,7 @@ static void annotateEquivalentReturnBbArg(OpOperand &returnVal,
 static LogicalResult
 aliasingFuncOpBBArgsAnalysis(FunctionOpInterface funcOp, OneShotAnalysisState &state,
                              FuncAnalysisState &funcState) {
-  if (funcOp.getFunctionBody().empty() || funcOp.getNumResults() == 0) {
+  if (funcOp.getFunctionBody().empty()) {
     // No function body available. Conservatively assume that every tensor
     // return value may alias with any tensor bbArg.
     for (const auto &inputIt : llvm::enumerate(funcOp.getArgumentTypes())) {

From eb2f8884ca6a95ff3e8d74b155d89c812a2ee866 Mon Sep 17 00:00:00 2001
From: Tzung-Han Juang <tzunghan.juang@xanadu.ai>
Date: Tue, 10 Sep 2024 14:30:39 -0400
Subject: [PATCH 08/17] Update
 mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp

Co-authored-by: erick-xanadu <110487834+erick-xanadu@users.noreply.github.com>
---
 .../Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
index 67323715ee424..ce90d907b4ca5 100644
--- a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
+++ b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
@@ -298,7 +298,7 @@ getFuncOpsOrderedByCalls(ModuleOp moduleOp,
   // For each FuncOp, the number of func::CallOp it contains.
   DenseMap<FunctionOpInterface, unsigned> numberCallOpsContainedInFuncOp;
   WalkResult res = moduleOp.walk([&](FunctionOpInterface funcOp) -> WalkResult {
-    if (!funcOp.getFunctionBody().empty() && funcOp.getNumResults() != 0) {
+    if (!funcOp.getFunctionBody().empty()) {
       Operation *returnOp = getAssumedUniqueReturnOp(funcOp);
       if (!returnOp)
         return funcOp->emitError()

From 695b945ce5259d888643339d66546bcedb9e6043 Mon Sep 17 00:00:00 2001
From: Tzung-Han Juang <tzunghan.juang@gmail.com>
Date: Tue, 10 Sep 2024 14:54:50 -0400
Subject: [PATCH 09/17] Add debug-payload-root-tag to transform.named_sequence
 tests

---
 .../Transforms/transform-ops.mlir             | 142 +++++++++--------
 mlir/test/Dialect/LLVM/transform-e2e.mlir     |  22 +--
 .../Linalg/matmul-shared-memory-padding.mlir  |  52 +++---
 .../Linalg/pad-to-specific-memory-space.mlir  | 148 +++++++++---------
 .../test/Dialect/Vector/transform-vector.mlir |  84 +++++-----
 5 files changed, 241 insertions(+), 207 deletions(-)

diff --git a/mlir/test/Dialect/Bufferization/Transforms/transform-ops.mlir b/mlir/test/Dialect/Bufferization/Transforms/transform-ops.mlir
index 3c50a9e72d9d9..588aa8a85a84e 100644
--- a/mlir/test/Dialect/Bufferization/Transforms/transform-ops.mlir
+++ b/mlir/test/Dialect/Bufferization/Transforms/transform-ops.mlir
@@ -1,4 +1,4 @@
-// RUN: mlir-opt --transform-interpreter %s -split-input-file -verify-diagnostics | FileCheck %s
+// RUN: mlir-opt --transform-interpreter="debug-payload-root-tag=payload" %s -split-input-file -verify-diagnostics | FileCheck %s
 
 // Test One-Shot Bufferize.
 
@@ -12,19 +12,21 @@ module attributes {transform.with_named_sequence} {
 
 // CHECK-LABEL: func @test_function(
 //  CHECK-SAME:     %[[A:.*]]: tensor<?xf32>
-func.func @test_function(%A : tensor<?xf32>, %v : vector<4xf32>) -> (tensor<?xf32>) {
-  %c0 = arith.constant 0 : index
+module @payload attributes { transform.target_tag = "payload" } {
+  func.func @test_function(%A : tensor<?xf32>, %v : vector<4xf32>) -> (tensor<?xf32>) {
+    %c0 = arith.constant 0 : index
 
-  // CHECK: %[[A_memref:.*]] = bufferization.to_memref %[[A]]
-  // CHECK: %[[dim:.*]] = memref.dim %[[A_memref]]
-  // CHECK: %[[alloc:.*]] = memref.alloc(%[[dim]])
-  // CHECK: memref.copy %[[A_memref]], %[[alloc]]
-  // CHECK: vector.transfer_write %{{.*}}, %[[alloc]]
-  // CHECK: %[[res_tensor:.*]] = bufferization.to_tensor %[[alloc]]
-  %0 = vector.transfer_write %v, %A[%c0] : vector<4xf32>, tensor<?xf32>
+    // CHECK: %[[A_memref:.*]] = bufferization.to_memref %[[A]]
+    // CHECK: %[[dim:.*]] = memref.dim %[[A_memref]]
+    // CHECK: %[[alloc:.*]] = memref.alloc(%[[dim]])
+    // CHECK: memref.copy %[[A_memref]], %[[alloc]]
+    // CHECK: vector.transfer_write %{{.*}}, %[[alloc]]
+    // CHECK: %[[res_tensor:.*]] = bufferization.to_tensor %[[alloc]]
+    %0 = vector.transfer_write %v, %A[%c0] : vector<4xf32>, tensor<?xf32>
 
-  // CHECK: return %[[res_tensor]]
-  return %0 : tensor<?xf32>
+    // CHECK: return %[[res_tensor]]
+    return %0 : tensor<?xf32>
+  }
 }
 
 // -----
@@ -42,19 +44,21 @@ module attributes {transform.with_named_sequence} {
 // CHECK-LABEL: func @test_function(
 //  CHECK-SAME:     %[[A:.*]]: tensor<?xf32>
 //   CHECK-NOT:   memref.copy
-func.func @test_function(%A : tensor<?xf32>, %v : vector<4xf32>) -> (tensor<?xf32>) {
-  %c0 = arith.constant 0 : index
+module @payload attributes { transform.target_tag = "payload" } {
+  func.func @test_function(%A : tensor<?xf32>, %v : vector<4xf32>) -> (tensor<?xf32>) {
+    %c0 = arith.constant 0 : index
 
-  // CHECK: %[[A_memref:.*]] = bufferization.to_memref %[[A]]
-  // CHECK: %[[dim:.*]] = memref.dim %[[A_memref]]
-  // CHECK: %[[alloc:.*]] = memref.alloc(%[[dim]])
-  // CHECK: linalg.copy ins(%[[A_memref]] : memref<{{.*}}>) outs(%[[alloc]]
-  // CHECK: vector.transfer_write %{{.*}}, %[[alloc]]
-  // CHECK: %[[res_tensor:.*]] = bufferization.to_tensor %[[alloc]]
-  %0 = vector.transfer_write %v, %A[%c0] : vector<4xf32>, tensor<?xf32>
+    // CHECK: %[[A_memref:.*]] = bufferization.to_memref %[[A]]
+    // CHECK: %[[dim:.*]] = memref.dim %[[A_memref]]
+    // CHECK: %[[alloc:.*]] = memref.alloc(%[[dim]])
+    // CHECK: linalg.copy ins(%[[A_memref]] : memref<{{.*}}>) outs(%[[alloc]]
+    // CHECK: vector.transfer_write %{{.*}}, %[[alloc]]
+    // CHECK: %[[res_tensor:.*]] = bufferization.to_tensor %[[alloc]]
+    %0 = vector.transfer_write %v, %A[%c0] : vector<4xf32>, tensor<?xf32>
 
-  // CHECK: return %[[res_tensor]]
-  return %0 : tensor<?xf32>
+    // CHECK: return %[[res_tensor]]
+    return %0 : tensor<?xf32>
+  }
 }
 
 // -----
@@ -72,13 +76,15 @@ module attributes {transform.with_named_sequence} {
 
 // CHECK-LABEL: func @test_function_analysis(
 //  CHECK-SAME:     %[[A:.*]]: tensor<?xf32>
-func.func @test_function_analysis(%A : tensor<?xf32>, %v : vector<4xf32>) -> (tensor<?xf32>) {
-  %c0 = arith.constant 0 : index
-  // CHECK: vector.transfer_write
-  // CHECK-SAME: {__inplace_operands_attr__ = ["none", "false", "none"]}
-  // CHECK-SAME: tensor<?xf32>
-  %0 = vector.transfer_write %v, %A[%c0] : vector<4xf32>, tensor<?xf32>
-  return %0 : tensor<?xf32>
+module @payload attributes { transform.target_tag = "payload" } {
+  func.func @test_function_analysis(%A : tensor<?xf32>, %v : vector<4xf32>) -> (tensor<?xf32>) {
+    %c0 = arith.constant 0 : index
+    // CHECK: vector.transfer_write
+    // CHECK-SAME: {__inplace_operands_attr__ = ["none", "false", "none"]}
+    // CHECK-SAME: tensor<?xf32>
+    %0 = vector.transfer_write %v, %A[%c0] : vector<4xf32>, tensor<?xf32>
+    return %0 : tensor<?xf32>
+  }
 }
 
 // -----
@@ -95,10 +101,12 @@ module attributes {transform.with_named_sequence} {
   }
 }
 
-func.func @test_unknown_op_failure() -> (tensor<?xf32>) {
-  // expected-error @+1 {{op was not bufferized}}
-  %0 = "test.dummy_op"() : () -> (tensor<?xf32>)
-  return %0 : tensor<?xf32>
+module @payload attributes { transform.target_tag = "payload" } {
+  func.func @test_unknown_op_failure() -> (tensor<?xf32>) {
+    // expected-error @+1 {{op was not bufferized}}
+    %0 = "test.dummy_op"() : () -> (tensor<?xf32>)
+    return %0 : tensor<?xf32>
+  }
 }
 
 // -----
@@ -111,7 +119,7 @@ module attributes {transform.with_named_sequence} {
   }
 }
 
-module {
+module @payload attributes { transform.target_tag = "payload" } {
   // CHECK-LABEL: func @test_function(
   //  CHECK-SAME:     %[[A:.*]]: tensor<?xf32>
   func.func @test_function(%A : tensor<?xf32>, %v : vector<4xf32>) -> (tensor<?xf32>) {
@@ -146,11 +154,13 @@ module attributes {transform.with_named_sequence} {
 // CHECK-SAME:  %[[A:.*]]: memref<12x9xf32>,
 // CHECK-SAME:  %[[B:.*]]: memref<9x6xf32>,
 // CHECK-SAME:  %[[C:.*]]: memref<12x6xf32>) -> memref<12x6xf32> {
-func.func @matmul(%A: tensor<12x9xf32>, %B: tensor<9x6xf32>, %C: tensor<12x6xf32>) -> tensor<12x6xf32> {
-  // CHECK: linalg.matmul ins(%[[A]], %[[B]] : memref<12x9xf32>, memref<9x6xf32>) outs(%[[C]] : memref<12x6xf32>)
-  %D = linalg.matmul ins(%A, %B: tensor<12x9xf32>, tensor<9x6xf32>) outs(%C: tensor<12x6xf32>) -> tensor<12x6xf32>
-  // CHECK: return %[[C]] : memref<12x6xf32>
-  return %D : tensor<12x6xf32>
+module @payload attributes { transform.target_tag = "payload" } {
+  func.func @matmul(%A: tensor<12x9xf32>, %B: tensor<9x6xf32>, %C: tensor<12x6xf32>) -> tensor<12x6xf32> {
+    // CHECK: linalg.matmul ins(%[[A]], %[[B]] : memref<12x9xf32>, memref<9x6xf32>) outs(%[[C]] : memref<12x6xf32>)
+    %D = linalg.matmul ins(%A, %B: tensor<12x9xf32>, tensor<9x6xf32>) outs(%C: tensor<12x6xf32>) -> tensor<12x6xf32>
+    // CHECK: return %[[C]] : memref<12x6xf32>
+    return %D : tensor<12x6xf32>
+  }
 }
 
 // -----
@@ -165,10 +175,12 @@ module attributes {transform.with_named_sequence} {
 }
 
 // Expect `bufferization.empty_tensor_to_alloc_tensor` to replace the tensor.empty.
-func.func @empty_to_tensor_alloc() -> tensor<2x2xf32> {
-  // CHECK: bufferization.alloc_tensor
-  %0 = tensor.empty() : tensor<2x2xf32>
-  return %0 : tensor<2x2xf32>
+module @payload attributes { transform.target_tag = "payload" } {
+  func.func @empty_to_tensor_alloc() -> tensor<2x2xf32> {
+    // CHECK: bufferization.alloc_tensor
+    %0 = tensor.empty() : tensor<2x2xf32>
+    return %0 : tensor<2x2xf32>
+  }
 }
 
 // -----
@@ -185,13 +197,15 @@ module attributes {transform.with_named_sequence} {
 //       CHECK:   tensor.extract_slice
 //       CHECK:   linalg.fill
 //       CHECK:   tensor.insert_slice
-func.func @empty_tensor_elimination(
-    %t: tensor<10xf32>, %f: f32) -> tensor<10xf32> {
-  %0 = tensor.empty() : tensor<5xf32>
-  %1 = linalg.fill ins(%f : f32) outs(%0 : tensor<5xf32>) -> tensor<5xf32>
-  %2 = tensor.insert_slice %1 into %t [1][5][1]
-      : tensor<5xf32> into tensor<10xf32>
-  return %2 : tensor<10xf32>
+module @payload attributes { transform.target_tag = "payload" } {
+  func.func @empty_tensor_elimination(
+      %t: tensor<10xf32>, %f: f32) -> tensor<10xf32> {
+    %0 = tensor.empty() : tensor<5xf32>
+    %1 = linalg.fill ins(%f : f32) outs(%0 : tensor<5xf32>) -> tensor<5xf32>
+    %2 = tensor.insert_slice %1 into %t [1][5][1]
+        : tensor<5xf32> into tensor<10xf32>
+    return %2 : tensor<10xf32>
+  }
 }
 
 // -----
@@ -208,12 +222,14 @@ module attributes {transform.with_named_sequence} {
 //       CHECK:   memref.alloca
 //       CHECK:   scf.for
 //       CHECK:     memref.store
-func.func @buffer_loop_hoisting(%lb: index, %ub: index, %step: index, %f: f32, %pos: index) {
-  scf.for %iv = %lb to %ub step %step {
-    %0 = memref.alloca() : memref<5xf32>
-    memref.store %f, %0[%pos] : memref<5xf32>
+module @payload attributes { transform.target_tag = "payload" } {
+  func.func @buffer_loop_hoisting(%lb: index, %ub: index, %step: index, %f: f32, %pos: index) {
+    scf.for %iv = %lb to %ub step %step {
+      %0 = memref.alloca() : memref<5xf32>
+      memref.store %f, %0[%pos] : memref<5xf32>
+    }
+    return
   }
-  return
 }
 
 // -----
@@ -231,10 +247,12 @@ module attributes {transform.with_named_sequence} {
 
 // Expect `bufferization.bufferize_to_allocation` to create an alloc.
 //  CHECK-LABEL: func.func @empty_to_tensor_alloc()
-func.func @empty_to_tensor_alloc() -> tensor<2x2xf32> {
-  // CHECK-NEXT: %[[alloca:.*]] = memref.alloca() : memref<2x2xf32>
-  // CHECK-NEXT: %[[tensor:.*]] = bufferization.to_tensor %[[alloca]] restrict writable : memref<2x2xf32>
-  // CHECK-NEXT: return %[[tensor]] : tensor<2x2xf32>
-  %0 = bufferization.alloc_tensor() : tensor<2x2xf32>
-  return %0 : tensor<2x2xf32>
+module @payload attributes { transform.target_tag = "payload" } {
+  func.func @empty_to_tensor_alloc() -> tensor<2x2xf32> {
+    // CHECK-NEXT: %[[alloca:.*]] = memref.alloca() : memref<2x2xf32>
+    // CHECK-NEXT: %[[tensor:.*]] = bufferization.to_tensor %[[alloca]] restrict writable : memref<2x2xf32>
+    // CHECK-NEXT: return %[[tensor]] : tensor<2x2xf32>
+    %0 = bufferization.alloc_tensor() : tensor<2x2xf32>
+    return %0 : tensor<2x2xf32>
+  }
 }
diff --git a/mlir/test/Dialect/LLVM/transform-e2e.mlir b/mlir/test/Dialect/LLVM/transform-e2e.mlir
index c00b47fb936e9..3e637a3ec49a4 100644
--- a/mlir/test/Dialect/LLVM/transform-e2e.mlir
+++ b/mlir/test/Dialect/LLVM/transform-e2e.mlir
@@ -1,15 +1,17 @@
-// RUN: mlir-opt %s --transform-interpreter -test-transform-dialect-erase-schedule --test-lower-to-llvm --split-input-file | FileCheck %s
+// RUN: mlir-opt %s --transform-interpreter="debug-payload-root-tag=payload" -test-transform-dialect-erase-schedule --test-lower-to-llvm --split-input-file | FileCheck %s
 
 // CHECK-LABEL: llvm.func @matmul_tensors
-func.func @matmul_tensors(
-  %arg0: tensor<2x4xf32>, %arg1: tensor<4x6xf32>, %arg2: tensor<2x6xf32>)
-    -> tensor<2x6xf32> {
-// CHECK-NOT: linalg
-// CHECK: llvm.intr.fmuladd{{.*}}
-  %0 = linalg.matmul  ins(%arg0, %arg1: tensor<2x4xf32>, tensor<4x6xf32>)
-                     outs(%arg2: tensor<2x6xf32>)
-    -> tensor<2x6xf32>
-  return %0 : tensor<2x6xf32>
+module @payload attributes { transform.target_tag = "payload" } {
+  func.func @matmul_tensors(
+    %arg0: tensor<2x4xf32>, %arg1: tensor<4x6xf32>, %arg2: tensor<2x6xf32>)
+      -> tensor<2x6xf32> {
+  // CHECK-NOT: linalg
+  // CHECK: llvm.intr.fmuladd{{.*}}
+    %0 = linalg.matmul  ins(%arg0, %arg1: tensor<2x4xf32>, tensor<4x6xf32>)
+                       outs(%arg2: tensor<2x6xf32>)
+      -> tensor<2x6xf32>
+    return %0 : tensor<2x6xf32>
+  }
 }
 
 module attributes {transform.with_named_sequence} {
diff --git a/mlir/test/Dialect/Linalg/matmul-shared-memory-padding.mlir b/mlir/test/Dialect/Linalg/matmul-shared-memory-padding.mlir
index 3f8d2ea06641e..9c223737750a9 100644
--- a/mlir/test/Dialect/Linalg/matmul-shared-memory-padding.mlir
+++ b/mlir/test/Dialect/Linalg/matmul-shared-memory-padding.mlir
@@ -1,4 +1,4 @@
-// RUN: mlir-opt --split-input-file --transform-interpreter %s | FileCheck %s
+// RUN: mlir-opt --split-input-file --transform-interpreter="debug-payload-root-tag=payload" %s | FileCheck %s
 
 // CHECK-LABEL: func @matmul_divisible
 //       CHECK:   scf.forall
@@ -24,19 +24,21 @@
 //       CHECK:       scf.forall
 //       CHECK:         vector.transfer_read
 //       CHECK:         vector.transfer_write
-func.func @matmul_divisible(%A: tensor<1024x1024xf32>,
-                            %B: tensor<1024x1024xf32>,
-                            %C: tensor<1024x1024xf32>)
-    -> tensor<1024x1024xf32>
-{
-  %cst = arith.constant 0.000000e+00 : f32
-  %0 = linalg.fill ins(%cst : f32)
-                   outs(%C : tensor<1024x1024xf32>)
+module @payload attributes { transform.target_tag = "payload" } {
+  func.func @matmul_divisible(%A: tensor<1024x1024xf32>,
+                              %B: tensor<1024x1024xf32>,
+                              %C: tensor<1024x1024xf32>)
       -> tensor<1024x1024xf32>
-  %1 = linalg.matmul ins(%A, %B : tensor<1024x1024xf32>, tensor<1024x1024xf32>)
-                     outs(%0 : tensor<1024x1024xf32>)
-      -> tensor<1024x1024xf32>
-  return %1 : tensor<1024x1024xf32>
+  {
+    %cst = arith.constant 0.000000e+00 : f32
+    %0 = linalg.fill ins(%cst : f32)
+                     outs(%C : tensor<1024x1024xf32>)
+        -> tensor<1024x1024xf32>
+    %1 = linalg.matmul ins(%A, %B : tensor<1024x1024xf32>, tensor<1024x1024xf32>)
+                       outs(%0 : tensor<1024x1024xf32>)
+        -> tensor<1024x1024xf32>
+    return %1 : tensor<1024x1024xf32>
+  }
 }
 
 module attributes {transform.with_named_sequence} {
@@ -143,19 +145,21 @@ module attributes {transform.with_named_sequence} {
 //       CHECK:       linalg.matmul
 //       CHECK:       vector.transfer_read
 //       CHECK:       vector.transfer_write
+module @payload attributes { transform.target_tag = "payload" } {
 func.func @matmul_not_divisible(%A: tensor<1023x1023xf32>,
-                                %B: tensor<1023x1023xf32>,
-                                %C: tensor<1023x1023xf32>)
-    -> tensor<1023x1023xf32>
-{
-  %cst = arith.constant 0.000000e+00 : f32
-  %0 = linalg.fill ins(%cst : f32)
-                   outs(%C : tensor<1023x1023xf32>)
+                                  %B: tensor<1023x1023xf32>,
+                                  %C: tensor<1023x1023xf32>)
       -> tensor<1023x1023xf32>
-  %1 = linalg.matmul ins(%A, %B : tensor<1023x1023xf32>, tensor<1023x1023xf32>)
-                     outs(%0 : tensor<1023x1023xf32>)
-      -> tensor<1023x1023xf32>
-  return %1 : tensor<1023x1023xf32>
+  {
+    %cst = arith.constant 0.000000e+00 : f32
+    %0 = linalg.fill ins(%cst : f32)
+                     outs(%C : tensor<1023x1023xf32>)
+        -> tensor<1023x1023xf32>
+    %1 = linalg.matmul ins(%A, %B : tensor<1023x1023xf32>, tensor<1023x1023xf32>)
+                       outs(%0 : tensor<1023x1023xf32>)
+        -> tensor<1023x1023xf32>
+    return %1 : tensor<1023x1023xf32>
+  }
 }
 
 module attributes {transform.with_named_sequence} {
diff --git a/mlir/test/Dialect/Linalg/pad-to-specific-memory-space.mlir b/mlir/test/Dialect/Linalg/pad-to-specific-memory-space.mlir
index f2e9e839b7c46..5e5657980ba12 100644
--- a/mlir/test/Dialect/Linalg/pad-to-specific-memory-space.mlir
+++ b/mlir/test/Dialect/Linalg/pad-to-specific-memory-space.mlir
@@ -1,5 +1,5 @@
 
-// RUN: mlir-opt --transform-interpreter -cse -canonicalize -split-input-file -verify-diagnostics %s | FileCheck %s
+// RUN: mlir-opt --transform-interpreter="debug-payload-root-tag=payload" -cse -canonicalize -split-input-file -verify-diagnostics %s | FileCheck %s
 
 #map = affine_map<()[s0] -> (-s0 + 12, 7)>
 
@@ -7,43 +7,45 @@
 //  CHECK-SAME:     %[[arg0:.*]]: memref<24x12xf32, strided<[?, ?], offset: ?>>,
 //  CHECK-SAME:     %[[arg1:.*]]: memref<12x25xf32, strided<[?, ?], offset: ?>>,
 //  CHECK-SAME:     %[[arg2:.*]]: memref<24x25xf32, strided<[?, ?], offset: ?>>,
-func.func @pad_to_memory_space(%arg0: tensor<24x12xf32>,
-                               %arg1: tensor<12x25xf32>,
-                               %arg2: tensor<24x25xf32>,
-                               %iv0 : index, %iv1 : index,
-                               %iv2 : index) -> tensor<24x25xf32> {
-  %0 = affine.min #map()[%iv2]
-
-  // CHECK: %[[s0:.*]] = memref.subview %[[arg0]]
-  %1 = tensor.extract_slice %arg0[%iv0, %iv2] [4, %0] [1, 1] : tensor<24x12xf32> to tensor<4x?xf32>
-  // CHECK: %[[s1:.*]] = memref.subview %[[arg1]]
-  %2 = tensor.extract_slice %arg1[%iv2, %iv1] [%0, 5] [1, 1] : tensor<12x25xf32> to tensor<?x5xf32>
-  // CHECK: %[[s2:.*]] = memref.subview %[[arg2]]
-  %3 = tensor.extract_slice %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<24x25xf32> to tensor<4x5xf32>
-
-  // CHECK: %[[alloc0:.*]] = memref.alloc() : memref<4x7xf32, 3>
-  // CHECK: linalg.fill {{.*}} outs(%[[alloc0]]
-  // CHECK: %[[alloc0_view:.*]] = memref.subview %[[alloc0]][0, 0] [4, %{{.*}}] [1, 1]
-  // CHECK: memref.copy %[[s0]], %[[alloc0_view]]
-
-  // CHECK: %[[alloc1:.*]] = memref.alloc() : memref<7x5xf32, 3>
-  // CHECK: linalg.fill {{.*}} outs(%[[alloc1]]
-  // CHECK: %[[alloc1_view:.*]] = memref.subview %[[alloc1]][0, 0] [%{{.*}}, 5] [1, 1]
-  // CHECK: memref.copy %[[s1]], %[[alloc1_view]]
-
-  // CHECK: %[[alloc2:.*]] = memref.alloc() : memref<4x5xf32, 3>
-  // CHECK-NOT: linalg.fill {{.*}} outs(%[[alloc2]]
-  // No subview because there is 0 padding
-  // CHECK: memref.copy %[[s2]], %[[alloc2]]
-
-  // CHECK: linalg.matmul ins(%[[alloc0]], %[[alloc1]] : {{.*}}) outs(%[[alloc2]] : {{.*}})
-  // Copy back result.
-  // CHECK: memref.copy %[[alloc2]], %[[s2]]
-  %4 = linalg.matmul ins(%1, %2 : tensor<4x?xf32>, tensor<?x5xf32>) outs(%3 : tensor<4x5xf32>) -> tensor<4x5xf32>
-
-  // insert_slice bufferizes to a no-op.
-  %5 = tensor.insert_slice %4 into %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<4x5xf32> into tensor<24x25xf32>
-  func.return %5 : tensor<24x25xf32>
+module @payload attributes { transform.target_tag = "payload" } {
+  func.func @pad_to_memory_space(%arg0: tensor<24x12xf32>,
+                                 %arg1: tensor<12x25xf32>,
+                                 %arg2: tensor<24x25xf32>,
+                                 %iv0 : index, %iv1 : index,
+                                 %iv2 : index) -> tensor<24x25xf32> {
+    %0 = affine.min #map()[%iv2]
+
+    // CHECK: %[[s0:.*]] = memref.subview %[[arg0]]
+    %1 = tensor.extract_slice %arg0[%iv0, %iv2] [4, %0] [1, 1] : tensor<24x12xf32> to tensor<4x?xf32>
+    // CHECK: %[[s1:.*]] = memref.subview %[[arg1]]
+    %2 = tensor.extract_slice %arg1[%iv2, %iv1] [%0, 5] [1, 1] : tensor<12x25xf32> to tensor<?x5xf32>
+    // CHECK: %[[s2:.*]] = memref.subview %[[arg2]]
+    %3 = tensor.extract_slice %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<24x25xf32> to tensor<4x5xf32>
+
+    // CHECK: %[[alloc0:.*]] = memref.alloc() : memref<4x7xf32, 3>
+    // CHECK: linalg.fill {{.*}} outs(%[[alloc0]]
+    // CHECK: %[[alloc0_view:.*]] = memref.subview %[[alloc0]][0, 0] [4, %{{.*}}] [1, 1]
+    // CHECK: memref.copy %[[s0]], %[[alloc0_view]]
+
+    // CHECK: %[[alloc1:.*]] = memref.alloc() : memref<7x5xf32, 3>
+    // CHECK: linalg.fill {{.*}} outs(%[[alloc1]]
+    // CHECK: %[[alloc1_view:.*]] = memref.subview %[[alloc1]][0, 0] [%{{.*}}, 5] [1, 1]
+    // CHECK: memref.copy %[[s1]], %[[alloc1_view]]
+
+    // CHECK: %[[alloc2:.*]] = memref.alloc() : memref<4x5xf32, 3>
+    // CHECK-NOT: linalg.fill {{.*}} outs(%[[alloc2]]
+    // No subview because there is 0 padding
+    // CHECK: memref.copy %[[s2]], %[[alloc2]]
+
+    // CHECK: linalg.matmul ins(%[[alloc0]], %[[alloc1]] : {{.*}}) outs(%[[alloc2]] : {{.*}})
+    // Copy back result.
+    // CHECK: memref.copy %[[alloc2]], %[[s2]]
+    %4 = linalg.matmul ins(%1, %2 : tensor<4x?xf32>, tensor<?x5xf32>) outs(%3 : tensor<4x5xf32>) -> tensor<4x5xf32>
+
+    // insert_slice bufferizes to a no-op.
+    %5 = tensor.insert_slice %4 into %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<4x5xf32> into tensor<24x25xf32>
+    func.return %5 : tensor<24x25xf32>
+  }
 }
 
 module attributes {transform.with_named_sequence} {
@@ -69,40 +71,42 @@ module attributes {transform.with_named_sequence} {
 //  CHECK-SAME:     %[[arg0:.*]]: memref<24x12xf32, strided<[?, ?], offset: ?>>,
 //  CHECK-SAME:     %[[arg1:.*]]: memref<12x25xf32, strided<[?, ?], offset: ?>>,
 //  CHECK-SAME:     %[[arg2:.*]]: memref<24x25xf32, strided<[?, ?], offset: ?>>,
-func.func @vectorize_and_bufferize_pad(%arg0: tensor<24x12xf32>,
-                                       %arg1: tensor<12x25xf32>,
-                                       %arg2: tensor<24x25xf32>,
-                                       %iv0 : index, %iv1 : index,
-                                       %iv2 : index) -> tensor<24x25xf32> {
-  %0 = affine.min #map()[%iv2]
-
-  // CHECK: %[[s0:.*]] = memref.subview %[[arg0]]
-  %1 = tensor.extract_slice %arg0[%iv0, %iv2] [4, %0] [1, 1] : tensor<24x12xf32> to tensor<4x?xf32>
-  // CHECK: %[[s1:.*]] = memref.subview %[[arg1]]
-  %2 = tensor.extract_slice %arg1[%iv2, %iv1] [%0, 5] [1, 1] : tensor<12x25xf32> to tensor<?x5xf32>
-  // CHECK: %[[s2:.*]] = memref.subview %[[arg2]]
-  %3 = tensor.extract_slice %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<24x25xf32> to tensor<4x5xf32>
-
-  // CHECK: %[[v0:.*]] = vector.mask {{.*}} { vector.transfer_read %[[s0]]
-  // CHECK: %[[alloc0:.*]] = memref.alloc() : memref<4x7xf32, 3>
-  // CHECK: vector.mask {{.*}} { vector.transfer_write %[[v0]], %[[alloc0]]
-
-  // CHECK: %[[v1:.*]] = vector.mask {{.*}} { vector.transfer_read %[[s1]]
-  // CHECK: %[[alloc1:.*]] = memref.alloc() : memref<7x5xf32, 3>
-  // CHECK: vector.mask {{.*}} { vector.transfer_write %[[v1]], %[[alloc1]]
-
-  // CHECK: %[[v2:.*]] = vector.mask {{.*}} { vector.transfer_read %[[s2]]
-  // CHECK: %[[alloc2:.*]] = memref.alloc() : memref<4x5xf32, 3>
-  // CHECK: vector.mask {{.*}} { vector.transfer_write %[[v2]], %[[alloc0]]
-
-  // CHECK: linalg.matmul ins(%[[alloc0]], %[[alloc1]] : {{.*}}) outs(%[[alloc2]] : {{.*}})
-  // Copy back result.
-  // CHECK: memref.copy %[[alloc2]], %[[s2]]
-  %4 = linalg.matmul ins(%1, %2 : tensor<4x?xf32>, tensor<?x5xf32>) outs(%3 : tensor<4x5xf32>) -> tensor<4x5xf32>
-
-  // insert_slice bufferizes to a no-op.
-  %5 = tensor.insert_slice %4 into %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<4x5xf32> into tensor<24x25xf32>
-  func.return %5 : tensor<24x25xf32>
+module @payload attributes { transform.target_tag = "payload" } {
+  func.func @vectorize_and_bufferize_pad(%arg0: tensor<24x12xf32>,
+                                         %arg1: tensor<12x25xf32>,
+                                         %arg2: tensor<24x25xf32>,
+                                         %iv0 : index, %iv1 : index,
+                                         %iv2 : index) -> tensor<24x25xf32> {
+    %0 = affine.min #map()[%iv2]
+
+    // CHECK: %[[s0:.*]] = memref.subview %[[arg0]]
+    %1 = tensor.extract_slice %arg0[%iv0, %iv2] [4, %0] [1, 1] : tensor<24x12xf32> to tensor<4x?xf32>
+    // CHECK: %[[s1:.*]] = memref.subview %[[arg1]]
+    %2 = tensor.extract_slice %arg1[%iv2, %iv1] [%0, 5] [1, 1] : tensor<12x25xf32> to tensor<?x5xf32>
+    // CHECK: %[[s2:.*]] = memref.subview %[[arg2]]
+    %3 = tensor.extract_slice %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<24x25xf32> to tensor<4x5xf32>
+
+    // CHECK: %[[v0:.*]] = vector.mask {{.*}} { vector.transfer_read %[[s0]]
+    // CHECK: %[[alloc0:.*]] = memref.alloc() : memref<4x7xf32, 3>
+    // CHECK: vector.mask {{.*}} { vector.transfer_write %[[v0]], %[[alloc0]]
+
+    // CHECK: %[[v1:.*]] = vector.mask {{.*}} { vector.transfer_read %[[s1]]
+    // CHECK: %[[alloc1:.*]] = memref.alloc() : memref<7x5xf32, 3>
+    // CHECK: vector.mask {{.*}} { vector.transfer_write %[[v1]], %[[alloc1]]
+
+    // CHECK: %[[v2:.*]] = vector.mask {{.*}} { vector.transfer_read %[[s2]]
+    // CHECK: %[[alloc2:.*]] = memref.alloc() : memref<4x5xf32, 3>
+    // CHECK: vector.mask {{.*}} { vector.transfer_write %[[v2]], %[[alloc0]]
+
+    // CHECK: linalg.matmul ins(%[[alloc0]], %[[alloc1]] : {{.*}}) outs(%[[alloc2]] : {{.*}})
+    // Copy back result.
+    // CHECK: memref.copy %[[alloc2]], %[[s2]]
+    %4 = linalg.matmul ins(%1, %2 : tensor<4x?xf32>, tensor<?x5xf32>) outs(%3 : tensor<4x5xf32>) -> tensor<4x5xf32>
+
+    // insert_slice bufferizes to a no-op.
+    %5 = tensor.insert_slice %4 into %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<4x5xf32> into tensor<24x25xf32>
+    func.return %5 : tensor<24x25xf32>
+  }
 }
 
 module attributes {transform.with_named_sequence} {
diff --git a/mlir/test/Dialect/Vector/transform-vector.mlir b/mlir/test/Dialect/Vector/transform-vector.mlir
index 4b38db79bff3e..0439844dc66ca 100644
--- a/mlir/test/Dialect/Vector/transform-vector.mlir
+++ b/mlir/test/Dialect/Vector/transform-vector.mlir
@@ -1,16 +1,18 @@
-// RUN: mlir-opt %s --transform-interpreter --split-input-file | FileCheck %s
+// RUN: mlir-opt --transform-interpreter="debug-payload-root-tag=payload" %s --split-input-file | FileCheck %s
 
 // CHECK-LABEL: func @matmul_tensors
-func.func @matmul_tensors(
-  %arg0: tensor<8x16xf32>, %arg1: tensor<16x32xf32>, %arg2: tensor<8x32xf32>)
-    -> tensor<8x32xf32> {
-// CHECK-NOT: linalg
-// CHECK: vector.extract {{.*}} : vector<4xf32> from vector<8x4xf32>
-// CHECK: vector.store {{.*}} : memref<8x32xf32>, vector<4xf32>
-  %0 = linalg.matmul  ins(%arg0, %arg1: tensor<8x16xf32>, tensor<16x32xf32>)
-                     outs(%arg2: tensor<8x32xf32>)
-    -> tensor<8x32xf32>
-  return %0 : tensor<8x32xf32>
+module @payload attributes { transform.target_tag = "payload" } {
+  func.func @matmul_tensors(
+    %arg0: tensor<8x16xf32>, %arg1: tensor<16x32xf32>, %arg2: tensor<8x32xf32>)
+      -> tensor<8x32xf32> {
+  // CHECK-NOT: linalg
+  // CHECK: vector.extract {{.*}} : vector<4xf32> from vector<8x4xf32>
+  // CHECK: vector.store {{.*}} : memref<8x32xf32>, vector<4xf32>
+    %0 = linalg.matmul  ins(%arg0, %arg1: tensor<8x16xf32>, tensor<16x32xf32>)
+                       outs(%arg2: tensor<8x32xf32>)
+      -> tensor<8x32xf32>
+    return %0 : tensor<8x32xf32>
+  }
 }
 
 module attributes {transform.with_named_sequence} {
@@ -76,11 +78,13 @@ module attributes {transform.with_named_sequence} {
 //  CHECK-SAME:   iterator_types = ["parallel", "parallel", "reduction"], kind = #vector.kind<add>}
 //  CHECK-SAME:   %[[ARG0]], %[[ARG1]], %[[ARG2]] : vector<64x64xf16>, vector<64x64xf16> into vector<64x64xf32>
 //  CHECK-NEXT:   return %[[R]] : vector<64x64xf32>
-func.func @fold_arith_extf_into_contract(%arg0: vector<64x64xf16>, %arg1: vector<64x64xf16>, %arg2: vector<64x64xf32>) -> vector<64x64xf32> {
-    %lhs_f32 = arith.extf %arg0 : vector<64x64xf16> to vector<64x64xf32>
-    %rhs_f32 = arith.extf %arg1 : vector<64x64xf16> to vector<64x64xf32>
-    %result = vector.contract {indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>], iterator_types = ["parallel", "parallel", "reduction"], kind = #vector.kind<add>} %lhs_f32, %rhs_f32, %arg2 : vector<64x64xf32>, vector<64x64xf32> into vector<64x64xf32>
-    return %result : vector<64x64xf32>
+module @payload attributes { transform.target_tag = "payload" } {
+  func.func @fold_arith_extf_into_contract(%arg0: vector<64x64xf16>, %arg1: vector<64x64xf16>, %arg2: vector<64x64xf32>) -> vector<64x64xf32> {
+      %lhs_f32 = arith.extf %arg0 : vector<64x64xf16> to vector<64x64xf32>
+      %rhs_f32 = arith.extf %arg1 : vector<64x64xf16> to vector<64x64xf32>
+      %result = vector.contract {indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>], iterator_types = ["parallel", "parallel", "reduction"], kind = #vector.kind<add>} %lhs_f32, %rhs_f32, %arg2 : vector<64x64xf32>, vector<64x64xf32> into vector<64x64xf32>
+      return %result : vector<64x64xf32>
+  }
 }
 
 module attributes {transform.with_named_sequence} {
@@ -95,30 +99,32 @@ module attributes {transform.with_named_sequence} {
 
 // -----
 
-// CHECK-LABEL: func.func @arith_to_outerproduct_scalable_i32
-//  CHECK-SAME:   %[[LHS:.*]]: vector<[4]xi32>,
-//  CHECK-SAME:   %[[RHS:.*]]: vector<[4]xi32>) -> vector<[4]x[4]xi32> {
-//       CHECK:     %[[RES:.*]] = vector.outerproduct %[[LHS]], %[[RHS]] : vector<[4]xi32>, vector<[4]xi32>
-//       CHECK:     return %[[RES]] : vector<[4]x[4]xi32>
-func.func @arith_to_outerproduct_scalable_i32(%lhs: vector<[4]xi32>, %rhs: vector<[4]xi32>) -> vector<[4]x[4]xi32> {
-  %lhsBcast = vector.broadcast %lhs : vector<[4]xi32> to vector<[4]x[4]xi32>
-  %lhsT = vector.transpose %lhsBcast, [1, 0] : vector<[4]x[4]xi32> to vector<[4]x[4]xi32>
-  %rhsBcast = vector.broadcast %rhs : vector<[4]xi32> to vector<[4]x[4]xi32>
-  %mul = arith.muli %lhsT, %rhsBcast : vector<[4]x[4]xi32>
-  return %mul: vector<[4]x[4]xi32>
-}
+module @payload attributes { transform.target_tag = "payload" } {
+  // CHECK-LABEL: func.func @arith_to_outerproduct_scalable_i32
+  //  CHECK-SAME:   %[[LHS:.*]]: vector<[4]xi32>,
+  //  CHECK-SAME:   %[[RHS:.*]]: vector<[4]xi32>) -> vector<[4]x[4]xi32> {
+  //       CHECK:     %[[RES:.*]] = vector.outerproduct %[[LHS]], %[[RHS]] : vector<[4]xi32>, vector<[4]xi32>
+  //       CHECK:     return %[[RES]] : vector<[4]x[4]xi32>
+  func.func @arith_to_outerproduct_scalable_i32(%lhs: vector<[4]xi32>, %rhs: vector<[4]xi32>) -> vector<[4]x[4]xi32> {
+    %lhsBcast = vector.broadcast %lhs : vector<[4]xi32> to vector<[4]x[4]xi32>
+    %lhsT = vector.transpose %lhsBcast, [1, 0] : vector<[4]x[4]xi32> to vector<[4]x[4]xi32>
+    %rhsBcast = vector.broadcast %rhs : vector<[4]xi32> to vector<[4]x[4]xi32>
+    %mul = arith.muli %lhsT, %rhsBcast : vector<[4]x[4]xi32>
+    return %mul: vector<[4]x[4]xi32>
+  }
 
-// CHECK-LABEL: func.func @arith_to_outerproduct_trans_rhs_f32
-//  CHECK-SAME:   %[[LHS:.*]]: vector<16xf32>,
-//  CHECK-SAME:   %[[RHS:.*]]: vector<8xf32>) -> vector<8x16xf32> {
-//       CHECK:     %[[RES:.*]] = vector.outerproduct %[[RHS]], %[[LHS]] : vector<8xf32>, vector<16xf32>
-//       CHECK:     return %[[RES]] : vector<8x16xf32>
-func.func @arith_to_outerproduct_trans_rhs_f32(%lhs: vector<16xf32>, %rhs: vector<8xf32>) -> vector<8x16xf32> {
-  %rhsBcast = vector.broadcast %rhs : vector<8xf32> to vector<16x8xf32>
-  %rhsT = vector.transpose %rhsBcast, [1, 0] : vector<16x8xf32> to vector<8x16xf32>
-  %lhsBcast = vector.broadcast %lhs : vector<16xf32> to vector<8x16xf32>
-  %mul = arith.mulf %lhsBcast, %rhsT : vector<8x16xf32>
-  return %mul: vector<8x16xf32>
+  // CHECK-LABEL: func.func @arith_to_outerproduct_trans_rhs_f32
+  //  CHECK-SAME:   %[[LHS:.*]]: vector<16xf32>,
+  //  CHECK-SAME:   %[[RHS:.*]]: vector<8xf32>) -> vector<8x16xf32> {
+  //       CHECK:     %[[RES:.*]] = vector.outerproduct %[[RHS]], %[[LHS]] : vector<8xf32>, vector<16xf32>
+  //       CHECK:     return %[[RES]] : vector<8x16xf32>
+  func.func @arith_to_outerproduct_trans_rhs_f32(%lhs: vector<16xf32>, %rhs: vector<8xf32>) -> vector<8x16xf32> {
+    %rhsBcast = vector.broadcast %rhs : vector<8xf32> to vector<16x8xf32>
+    %rhsT = vector.transpose %rhsBcast, [1, 0] : vector<16x8xf32> to vector<8x16xf32>
+    %lhsBcast = vector.broadcast %lhs : vector<16xf32> to vector<8x16xf32>
+    %mul = arith.mulf %lhsBcast, %rhsT : vector<8x16xf32>
+    return %mul: vector<8x16xf32>
+  }
 }
 
 module attributes {transform.with_named_sequence} {

From aca0e78354db91fa51fa3239d8d82b586adc7a77 Mon Sep 17 00:00:00 2001
From: Tzung-Han Juang <tzunghan.juang@gmail.com>
Date: Wed, 11 Sep 2024 15:51:49 -0400
Subject: [PATCH 10/17] Add transform.target_tag to CHH/full.mlir

---
 mlir/test/Examples/transform/ChH/full.mlir | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/mlir/test/Examples/transform/ChH/full.mlir b/mlir/test/Examples/transform/ChH/full.mlir
index 259475ebdbf49..005ac5a9ba8ec 100644
--- a/mlir/test/Examples/transform/ChH/full.mlir
+++ b/mlir/test/Examples/transform/ChH/full.mlir
@@ -1,4 +1,4 @@
-// RUN: mlir-opt %s --transform-interpreter \
+// RUN: mlir-opt %s --transform-interpreter="debug-payload-root-tag=payload" \
 // RUN:             --test-transform-dialect-erase-schedule \
 // RUN:             --math-uplift-to-fma \
 // RUN:             --convert-bufferization-to-memref \
@@ -19,6 +19,7 @@
 // tensors annotated with attributes from the `bufferization` dialect. These
 // attributes hint the bufferization pass to assume buffers can be directly
 // used for these tensors without reshaping.
+module @payload attributes { transform.target_tag = "payload" } {
 func.func @conv(
     %input: !tinput {bufferization.writable = false,
                      bufferization.access = "read",
@@ -84,7 +85,7 @@ func.func @conv(
 
   return %relued : !toutput
 }
-
+}
 // Module containing the transformation script to be applied. The attribute
 // is required to correctly verify the use of named (macro-like) sequences.
 module attributes { transform.with_named_sequence } {

From 913ab762c527cc16509605b4d8f27ea8bd6bd157 Mon Sep 17 00:00:00 2001
From: Tzung-Han Juang <tzunghan.juang@gmail.com>
Date: Mon, 16 Sep 2024 15:43:22 -0400
Subject: [PATCH 11/17] Create a pass pipeline on nested modules in
 mlir/test/Examples/transform/ChH/full.mlir

---
 mlir/test/Examples/transform/ChH/full.mlir | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/mlir/test/Examples/transform/ChH/full.mlir b/mlir/test/Examples/transform/ChH/full.mlir
index 005ac5a9ba8ec..85dbf67023323 100644
--- a/mlir/test/Examples/transform/ChH/full.mlir
+++ b/mlir/test/Examples/transform/ChH/full.mlir
@@ -1,8 +1,6 @@
 // RUN: mlir-opt %s --transform-interpreter="debug-payload-root-tag=payload" \
-// RUN:             --test-transform-dialect-erase-schedule \
-// RUN:             --math-uplift-to-fma \
-// RUN:             --convert-bufferization-to-memref \
-// RUN:             --test-lower-to-llvm |\
+// RUN:             --test-transform-dialect-erase-schedule |\
+// RUN: mlir-opt -pass-pipeline='builtin.module(builtin.module(math-uplift-to-fma,convert-bufferization-to-memref,test-lower-to-llvm))' - |\
 // RUN: FileCheck %s
 
 // Fixed-size tensor types to be used in convolution.

From 1811994699517d14b659405ff979b91f8e5e37a0 Mon Sep 17 00:00:00 2001
From: Tzung-Han Juang <tzunghan.juang@gmail.com>
Date: Tue, 17 Sep 2024 16:34:17 -0400
Subject: [PATCH 12/17] Make FunctionArgTypeConverterFn use FunctionOpInterface

---
 .../mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h  | 3 ++-
 .../lib/Dialect/Bufferization/IR/BufferizableOpInterface.cpp | 5 +++--
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/mlir/include/mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h b/mlir/include/mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h
index 2fda091e412ae..ba28596d1f97d 100644
--- a/mlir/include/mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h
+++ b/mlir/include/mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h
@@ -9,6 +9,7 @@
 #ifndef MLIR_DIALECT_BUFFERIZATION_IR_BUFFERIZABLEOPINTERFACE_H_
 #define MLIR_DIALECT_BUFFERIZATION_IR_BUFFERIZABLEOPINTERFACE_H_
 
+#include "mlir/Interfaces/FunctionInterfaces.h"
 #include "mlir/IR/Operation.h"
 #include "mlir/IR/PatternMatch.h"
 #include "mlir/Support/LLVM.h"
@@ -262,7 +263,7 @@ struct BufferizationOptions {
   /// Parameters: Value, memory space, func op, bufferization options
   using FunctionArgTypeConverterFn =
       std::function<BaseMemRefType(TensorType, Attribute memorySpace,
-                                   func::FuncOp, const BufferizationOptions &)>;
+                                   FunctionOpInterface, const BufferizationOptions &)>;
   /// Tensor -> MemRef type converter.
   /// Parameters: Value, memory space, bufferization options
   using UnknownTypeConverterFn = std::function<BaseMemRefType(
diff --git a/mlir/lib/Dialect/Bufferization/IR/BufferizableOpInterface.cpp b/mlir/lib/Dialect/Bufferization/IR/BufferizableOpInterface.cpp
index d51d63f243ea0..c4201698468ce 100644
--- a/mlir/lib/Dialect/Bufferization/IR/BufferizableOpInterface.cpp
+++ b/mlir/lib/Dialect/Bufferization/IR/BufferizableOpInterface.cpp
@@ -18,6 +18,7 @@
 #include "mlir/IR/TypeUtilities.h"
 #include "mlir/IR/Value.h"
 #include "mlir/Interfaces/ControlFlowInterfaces.h"
+#include "mlir/Interfaces/FunctionInterfaces.h"
 #include "llvm/ADT/ScopeExit.h"
 #include "llvm/Support/Debug.h"
 
@@ -314,7 +315,7 @@ namespace {
 /// Default function arg type converter: Use a fully dynamic layout map.
 BaseMemRefType
 defaultFunctionArgTypeConverter(TensorType type, Attribute memorySpace,
-                                func::FuncOp funcOp,
+                                FunctionOpInterface funcOp,
                                 const BufferizationOptions &options) {
   return getMemRefTypeWithFullyDynamicLayout(type, memorySpace);
 }
@@ -361,7 +362,7 @@ BufferizationOptions::dynCastBufferizableOp(Value value) const {
 void BufferizationOptions::setFunctionBoundaryTypeConversion(
     LayoutMapOption layoutMapOption) {
   functionArgTypeConverterFn = [=](TensorType tensorType, Attribute memorySpace,
-                                   func::FuncOp funcOp,
+                                   FunctionOpInterface funcOp,
                                    const BufferizationOptions &options) {
     if (layoutMapOption == LayoutMapOption::IdentityLayoutMap)
       return bufferization::getMemRefTypeWithStaticIdentityLayout(tensorType,

From 73f7e862c3853de1375b52968af7325911716af9 Mon Sep 17 00:00:00 2001
From: Tzung-Han Juang <tzunghan.juang@gmail.com>
Date: Tue, 24 Sep 2024 14:56:07 -0400
Subject: [PATCH 13/17] Fix formatting issues

---
 .../IR/BufferizableOpInterface.h              |  8 +++---
 .../Transforms/OneShotModuleBufferize.cpp     | 28 +++++++++----------
 2 files changed, 18 insertions(+), 18 deletions(-)

diff --git a/mlir/include/mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h b/mlir/include/mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h
index ba28596d1f97d..eb0df1d92d6ad 100644
--- a/mlir/include/mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h
+++ b/mlir/include/mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h
@@ -9,9 +9,9 @@
 #ifndef MLIR_DIALECT_BUFFERIZATION_IR_BUFFERIZABLEOPINTERFACE_H_
 #define MLIR_DIALECT_BUFFERIZATION_IR_BUFFERIZABLEOPINTERFACE_H_
 
-#include "mlir/Interfaces/FunctionInterfaces.h"
 #include "mlir/IR/Operation.h"
 #include "mlir/IR/PatternMatch.h"
+#include "mlir/Interfaces/FunctionInterfaces.h"
 #include "mlir/Support/LLVM.h"
 #include "llvm/ADT/DenseMapInfoVariant.h"
 #include "llvm/ADT/SetVector.h"
@@ -261,9 +261,9 @@ struct BufferizationOptions {
   using AnalysisStateInitFn = std::function<void(AnalysisState &)>;
   /// Tensor -> MemRef type converter.
   /// Parameters: Value, memory space, func op, bufferization options
-  using FunctionArgTypeConverterFn =
-      std::function<BaseMemRefType(TensorType, Attribute memorySpace,
-                                   FunctionOpInterface, const BufferizationOptions &)>;
+  using FunctionArgTypeConverterFn = std::function<BaseMemRefType(
+      TensorType, Attribute memorySpace, FunctionOpInterface,
+      const BufferizationOptions &)>;
   /// Tensor -> MemRef type converter.
   /// Parameters: Value, memory space, bufferization options
   using UnknownTypeConverterFn = std::function<BaseMemRefType(
diff --git a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
index ce90d907b4ca5..f934ae88277b6 100644
--- a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
+++ b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
@@ -88,7 +88,7 @@ getOrCreateFuncAnalysisState(OneShotAnalysisState &state) {
 
 /// Return the unique ReturnOp that terminates `funcOp`.
 /// Return nullptr if there is no such unique ReturnOp.
-static Operation* getAssumedUniqueReturnOp(FunctionOpInterface funcOp) {
+static Operation *getAssumedUniqueReturnOp(FunctionOpInterface funcOp) {
   Operation *returnOp = nullptr;
   for (Block &b : funcOp.getFunctionBody()) {
     auto candidateOp = b.getTerminator();
@@ -127,7 +127,8 @@ static void annotateEquivalentReturnBbArg(OpOperand &returnVal,
 /// Store function BlockArguments that are equivalent to/aliasing a returned
 /// value in FuncAnalysisState.
 static LogicalResult
-aliasingFuncOpBBArgsAnalysis(FunctionOpInterface funcOp, OneShotAnalysisState &state,
+aliasingFuncOpBBArgsAnalysis(FunctionOpInterface funcOp,
+                             OneShotAnalysisState &state,
                              FuncAnalysisState &funcState) {
   if (funcOp.getFunctionBody().empty()) {
     // No function body available. Conservatively assume that every tensor
@@ -168,8 +169,8 @@ aliasingFuncOpBBArgsAnalysis(FunctionOpInterface funcOp, OneShotAnalysisState &s
   return success();
 }
 
-static void annotateFuncArgAccess(FunctionOpInterface funcOp, int64_t idx, bool isRead,
-                                  bool isWritten) {
+static void annotateFuncArgAccess(FunctionOpInterface funcOp, int64_t idx,
+                                  bool isRead, bool isWritten) {
   OpBuilder b(funcOp.getContext());
   Attribute accessType;
   if (isRead && isWritten) {
@@ -189,10 +190,10 @@ static void annotateFuncArgAccess(FunctionOpInterface funcOp, int64_t idx, bool
 /// function with unknown ops, we conservatively assume that such ops bufferize
 /// to a read + write.
 static LogicalResult
-funcOpBbArgReadWriteAnalysis(FunctionOpInterface funcOp, OneShotAnalysisState &state,
+funcOpBbArgReadWriteAnalysis(FunctionOpInterface funcOp,
+                             OneShotAnalysisState &state,
                              FuncAnalysisState &funcState) {
-  for (int64_t idx = 0, e = funcOp.getNumArguments(); idx < e;
-       ++idx) {
+  for (int64_t idx = 0, e = funcOp.getNumArguments(); idx < e; ++idx) {
     // Skip non-tensor arguments.
     if (!isa<TensorType>(funcOp.getArgumentTypes()[idx]))
       continue;
@@ -277,10 +278,8 @@ static void equivalenceAnalysis(FunctionOpInterface funcOp,
 
 /// Return "true" if the given function signature has tensor semantics.
 static bool hasTensorSignature(FunctionOpInterface funcOp) {
-  return llvm::any_of(funcOp.getArgumentTypes(),
-                      llvm::IsaPred<TensorType>) ||
-         llvm::any_of(funcOp.getResultTypes(),
-                      llvm::IsaPred<TensorType>);
+  return llvm::any_of(funcOp.getArgumentTypes(), llvm::IsaPred<TensorType>) ||
+         llvm::any_of(funcOp.getResultTypes(), llvm::IsaPred<TensorType>);
 }
 
 /// Store all functions of the `moduleOp` in `orderedFuncOps`, sorted by
@@ -310,7 +309,8 @@ getFuncOpsOrderedByCalls(ModuleOp moduleOp,
     numberCallOpsContainedInFuncOp[funcOp] = 0;
     return funcOp.walk([&](CallOpInterface callOp) -> WalkResult {
       FunctionOpInterface calledFunction = getCalledFunction(callOp);
-      assert(calledFunction && "could not retrieved called FunctionOpInterface");
+      assert(calledFunction &&
+             "could not retrieved called FunctionOpInterface");
       // If the called function does not have any tensors in its signature, then
       // it is not necessary to bufferize the callee before the caller.
       if (!hasTensorSignature(calledFunction))
@@ -364,8 +364,8 @@ static void foldMemRefCasts(FunctionOpInterface funcOp) {
     }
   }
 
-  auto newFuncType = FunctionType::get(
-      funcOp.getContext(), funcOp.getArgumentTypes(), resultTypes);
+  auto newFuncType = FunctionType::get(funcOp.getContext(),
+                                       funcOp.getArgumentTypes(), resultTypes);
   funcOp.setType(newFuncType);
 }
 

From 8d780007625108a7f34e40efb8604b858e04c60c Mon Sep 17 00:00:00 2001
From: Tzung-Han Juang <tzunghan.juang@gmail.com>
Date: Fri, 27 Sep 2024 15:46:11 -0400
Subject: [PATCH 14/17] Skip function call analysis for CallIndirectOp

---
 .../Bufferization/Transforms/OneShotModuleBufferize.cpp     | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
index f934ae88277b6..36499ed2ad74a 100644
--- a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
+++ b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
@@ -255,6 +255,9 @@ static void equivalenceAnalysis(FunctionOpInterface funcOp,
                                 OneShotAnalysisState &state,
                                 FuncAnalysisState &funcState) {
   funcOp->walk([&](CallOpInterface callOp) {
+    if (isa<func::CallIndirectOp>(callOp))
+      return WalkResult::skip();
+
     FunctionOpInterface calledFunction = getCalledFunction(callOp);
     assert(calledFunction && "could not retrieved called FunctionOpInterface");
 
@@ -308,6 +311,9 @@ getFuncOpsOrderedByCalls(ModuleOp moduleOp,
     // Collect function calls and populate the caller map.
     numberCallOpsContainedInFuncOp[funcOp] = 0;
     return funcOp.walk([&](CallOpInterface callOp) -> WalkResult {
+      if (isa<func::CallIndirectOp>(callOp))
+        return WalkResult::skip();
+
       FunctionOpInterface calledFunction = getCalledFunction(callOp);
       assert(calledFunction &&
              "could not retrieved called FunctionOpInterface");

From f3b55a1dea1954b6a8908378f292b939425db976 Mon Sep 17 00:00:00 2001
From: Tzung-Han Juang <tzunghan.juang@xanadu.ai>
Date: Mon, 30 Sep 2024 09:50:34 -0400
Subject: [PATCH 15/17] Update
 mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp

Co-authored-by: erick-xanadu <110487834+erick-xanadu@users.noreply.github.com>
---
 .../Bufferization/Transforms/OneShotModuleBufferize.cpp     | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
index 36499ed2ad74a..465679c1fa120 100644
--- a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
+++ b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
@@ -255,11 +255,9 @@ static void equivalenceAnalysis(FunctionOpInterface funcOp,
                                 OneShotAnalysisState &state,
                                 FuncAnalysisState &funcState) {
   funcOp->walk([&](CallOpInterface callOp) {
-    if (isa<func::CallIndirectOp>(callOp))
-      return WalkResult::skip();
-
     FunctionOpInterface calledFunction = getCalledFunction(callOp);
-    assert(calledFunction && "could not retrieved called FunctionOpInterface");
+    if (!calledFunction)
+      return WalkResult::skip();
 
     // No equivalence info available for the called function.
     if (!funcState.equivalentFuncArgs.count(calledFunction))

From 6c6f88984e86b89acb28f4c97d58a458ca7d6ef6 Mon Sep 17 00:00:00 2001
From: Tzung-Han Juang <tzunghan.juang@gmail.com>
Date: Mon, 30 Sep 2024 09:57:35 -0400
Subject: [PATCH 16/17] Patch another function call assertion

---
 .../Bufferization/Transforms/OneShotModuleBufferize.cpp       | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
index 465679c1fa120..bec5a95883452 100644
--- a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
+++ b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
@@ -313,8 +313,8 @@ getFuncOpsOrderedByCalls(ModuleOp moduleOp,
         return WalkResult::skip();
 
       FunctionOpInterface calledFunction = getCalledFunction(callOp);
-      assert(calledFunction &&
-             "could not retrieved called FunctionOpInterface");
+      if (!calledFunction)
+        return WalkResult::skip();
       // If the called function does not have any tensors in its signature, then
       // it is not necessary to bufferize the callee before the caller.
       if (!hasTensorSignature(calledFunction))

From 4a60687b1f744f3444855b39ae457c8efe6ae1bb Mon Sep 17 00:00:00 2001
From: Tzung-Han Juang <tzunghan.juang@gmail.com>
Date: Mon, 30 Sep 2024 14:35:28 -0400
Subject: [PATCH 17/17] Remove CallIndirectOp skip

---
 .../Bufferization/Transforms/OneShotModuleBufferize.cpp        | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
index bec5a95883452..a0e5c7fff7690 100644
--- a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
+++ b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
@@ -309,9 +309,6 @@ getFuncOpsOrderedByCalls(ModuleOp moduleOp,
     // Collect function calls and populate the caller map.
     numberCallOpsContainedInFuncOp[funcOp] = 0;
     return funcOp.walk([&](CallOpInterface callOp) -> WalkResult {
-      if (isa<func::CallIndirectOp>(callOp))
-        return WalkResult::skip();
-
       FunctionOpInterface calledFunction = getCalledFunction(callOp);
       if (!calledFunction)
         return WalkResult::skip();