[mlir][linalg][bufferize][NFC] Pass BufferizationState as const reference

[springerm]

Summary

This is mostly for documentation purposes: Passing the object as a const reference signifies that analysis decisions cannot be changed after the analysis.

Depends On D116741

[springerm]

Note: This change was needed to address requested changes in D116448. I decided to put this into a separate revision to keep the commits small.

mlir/include/mlir/Dialect/Linalg/ComprehensiveBufferize/BufferizableOpInterface.h

 public:
   BufferizationState(Operation *op, const BufferizationOptions &options);
 
   // BufferizationState should be passed as a reference.
   BufferizationState(const BufferizationState &) = delete;
 
   /// Determine which OpOperand* will alias with `result` if the op is
   /// bufferized in place. Return an empty vector if the op is not bufferizable.
-  SmallVector<OpOperand *> getAliasingOpOperand(OpResult result);
+  SmallVector<OpOperand *> getAliasingOpOperand(OpResult result) const;
 
   /// Determine which OpResult will alias with `opOperand` if the op is
   /// bufferized in place. Return an empty OpResult if the op is not
   /// bufferizable.
-  OpResult getAliasingOpResult(OpOperand &opOperand);
+  OpResult getAliasingOpResult(OpOperand &opOperand) const;
 
   /// Return true if `opOperand` bufferizes to a memory read. Return `true` if
   /// the op is not bufferizable.
-  bool bufferizesToMemoryRead(OpOperand &opOperand);
+  bool bufferizesToMemoryRead(OpOperand &opOperand) const;
 
   /// Return true if `opOperand` bufferizes to a memory write. Return true` if
   /// the op is not bufferizable.
-  bool bufferizesToMemoryWrite(OpOperand &opOperand);
+  bool bufferizesToMemoryWrite(OpOperand &opOperand) const;
 
   /// Return true if `opOperand` does neither read nor write but bufferizes to
   /// an alias. Return false if the op is not bufferizable.
-  bool bufferizesToAliasOnly(OpOperand &opOperand);
+  bool bufferizesToAliasOnly(OpOperand &opOperand) const;
 
   /// Return true if the given value is read by an op that bufferizes to a
   /// memory read. Also takes into account ops that create an alias but do not
   /// read by themselves (e.g., ExtractSliceOp).
-  bool isValueRead(Value value);
+  bool isValueRead(Value value) const;
 
   /// Starting from `value`, follow the use-def chain in reverse, always
   /// selecting the aliasing OpOperands. Find and return Values for which
   /// `condition` evaluates to true. OpOperands of such matching Values are not
   /// traversed any further.
   ///
   /// When reaching the end of a chain (BlockArgument or Value without aliasing
   /// OpOperands), also return the last Value of that chain.
   ///
   /// Example:
   ///
   ///                               8
   ///                               |
   ///   6*         7*         +-----+----+
   ///   |          |          |          |
   ///   2*         3          4*         5
   ///   |          |          |          |
   ///   +----------+----------+----------+
   ///              |
   ///              1
   ///
   /// In the above example, Values with a star satisfy the condition. When
   /// starting the traversal from Value 1, the resulting SetVector is:
   /// { 2, 7, 8, 5 }
-  llvm::SetVector<Value>
-  findValueInReverseUseDefChain(Value value,
-                                llvm::function_ref<bool(Value)> condition);
+  llvm::SetVector<Value> findValueInReverseUseDefChain(
+      Value value, llvm::function_ref<bool(Value)> condition) const;
 
   /// Find the Value of the last preceding write of a given Value.
   ///
   /// Note: Unknown ops are handled conservatively and assumed to be writes.
   /// Furthermore, BlockArguments are also assumed to be writes. There is no
   /// analysis across block boundaries.
   ///
   /// Note: When reaching an end of the reverse SSA use-def chain, that value
   /// is returned regardless of whether it is a memory write or not.
-  Value findLastPrecedingWrite(Value value);
+  Value findLastPrecedingWrite(Value value) const;
 
   /// Creates a memref allocation.
   Optional<Value> createAlloc(OpBuilder &b, Location loc, MemRefType type,
-                              ArrayRef<Value> dynShape);
+                              ArrayRef<Value> dynShape) const;
 
   /// Creates an alloc-dealloc pair. This function may perform additional
   /// optimizations such as buffer allocation hoisting.
   Value createAllocDeallocPair(OpBuilder &builder, Location loc,
-                               Value shapedValue);
+                               Value shapedValue) const;
 
   /// Creates a memref deallocation. The given memref buffer must have been
   /// allocated using `createAlloc`.
-  void createDealloc(OpBuilder &b, Location loc, Value allocatedBuffer);
+  void createDealloc(OpBuilder &b, Location loc, Value allocatedBuffer) const;
 
   /// Creates a memcpy between two given buffers.
-  void createMemCpy(OpBuilder &b, Location loc, Value from, Value to);
+  void createMemCpy(OpBuilder &b, Location loc, Value from, Value to) const;
 
   /// Replace an op with replacement values. The op is deleted. Tensor OpResults
   /// must be replaced with memref values.
-  void replaceOp(RewriterBase &rewriter, Operation *op, ValueRange values);
+  void replaceOp(RewriterBase &rewriter, Operation *op,
+                 ValueRange values) const;
 
   /// Replace an op with a new op. Tensor OpResults must be replaced with memref
   /// values.
   template <typename OpTy, typename... Args>
   OpTy replaceOpWithNewOp(RewriterBase &rewriter, Operation *op,
-                          Args &&...args) {
+                          Args &&...args) const {
     Operation *newOp =
         rewriter.create<OpTy>(op->getLoc(), std::forward<Args>(args)...);
     replaceOp(rewriter, op, newOp->getResults());
     return cast<OpTy>(newOp);
   }
 
   /// Lookup the memref buffer that is associated to the given tensor value.
   /// Asserts if no buffer is associated.
-  Value lookupBuffer(RewriterBase &rewriter, Value tensor);
+  Value lookupBuffer(RewriterBase &rewriter, Value tensor) const;
 
   /// Return `true` if the given OpResult has been decided to bufferize inplace.
   bool isInPlace(OpResult opResult) const;
 
   /// Return the result buffer (memref) for a given OpResult (tensor). Allocate
   /// a new buffer and copy over data from the existing buffer if out-of-place
   /// bufferization is necessary.
-  Value getResultBuffer(RewriterBase &rewriter, OpResult result);
+  Value getResultBuffer(RewriterBase &rewriter, OpResult result) const;
 
   /// Return dialect-specific bufferization state.
-  template <typename StateT> StateT &getDialectState(StringRef name) {
+  template <typename StateT>
+  Optional<const StateT *> getDialectState(StringRef name) const {
+    auto it = dialectState.find(name);
+    if (it == dialectState.end())
+      return None;
+    return static_cast<const StateT *>(it->getSecond().get());
+  }
+
+  /// Return dialect-specific bufferization state or create one if none exists.
+  template <typename StateT>
+  StateT &getOrCreateDialectState(StringRef name) {
     // Create state if it does not exist yet.
     if (!dialectState.count(name))
       dialectState[name] = std::make_unique<StateT>();
     return static_cast<StateT &>(*dialectState[name]);
   }
 
   /// Return a reference to the BufferizationOptions.
   const BufferizationOptions &getOptions() const { return options; }
 
-private:
-  friend LogicalResult
-  runComprehensiveBufferize(Operation *op, const BufferizationOptions &options,
-                            BufferizationState &state);
-
-  friend LogicalResult
-  runComprehensiveBufferize(ModuleOp moduleOp,
-                            std::unique_ptr<BufferizationOptions> options);
+  /// Return a reference to the BufferizationAliasInfo.
+  BufferizationAliasInfo &getAliasInfo() { return aliasInfo; }
 
+private:
   /// `aliasInfo` keeps track of aliasing and equivalent values. Only internal
   /// functions and `runComprehensiveBufferize` may access this object.
   BufferizationAliasInfo aliasInfo;
 
   /// Dialect-specific bufferization state.
   DenseMap<StringRef, std::unique_ptr<DialectBufferizationState>> dialectState;
 
   /// A reference to current bufferization options.
   const BufferizationOptions &options;
 };
 
 /// Bufferize all ops in the given region.
 LogicalResult bufferize(RewriterBase &rewriter, Region *region,
-                        BufferizationState &state);
+                        const BufferizationState &state);
 
 /// Bufferize all ops in the given block.
 LogicalResult bufferize(RewriterBase &rewriter, Block *block,
-                        BufferizationState &state);
+                        const BufferizationState &state);
 
 /// Bufferize the given op. If the op has no tensor OpOperands/OpResults, this
 /// function returns immediately. Otherwise, it calls the `bufferize` interface
 /// method of `BufferizableOpInterface`.
 LogicalResult bufferize(RewriterBase &rewriter, Operation *op,
-                        BufferizationState &state);
+                        const BufferizationState &state);
 
 /// Return a contiguous MemRefType (i.e. with canonical/empty layout map)
 /// with the same shape as `shapedType` and specified `layout` and
 /// `addressSpace`.
 MemRefType getContiguousMemRefType(ShapedType shapedType,
                                    MemRefLayoutAttrInterface layout = {},
                                    Attribute memorySpace = {});
 

 /// BufferizableOpInterface for ops that are (not yet) bufferizable, but are
 /// known to be allocation hoisting barriers. All interface methods (except for
 /// `isAllocationHoistingBarrier`) are implemented conservatively.
 template <typename OpTy>
 struct AllocationHoistingBarrierOnly
     : public BufferizableOpInterface::ExternalModel<
           AllocationHoistingBarrierOnly<OpTy>, OpTy> {
   bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
-                              BufferizationState &state) const {
+                              const BufferizationState &state) const {
     return true;
   }
 
   bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     return false;
   }
 
   SmallVector<OpOperand *>
   getAliasingOpOperand(Operation *op, OpResult opResult,
-                       BufferizationState &state) const {
+                       const BufferizationState &state) const {
     return {};
   }
 
   OpResult getAliasingOpResult(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     return OpResult();
   }
 
   BufferRelation bufferRelation(Operation *op, OpResult opResult,
                                 const BufferizationAliasInfo &aliasInfo,
-                                BufferizationState &state) const {
+                                const BufferizationState &state) const {
     return BufferRelation::None;
   }
 
-  bool isWritable(Operation *op, Value value, BufferizationState &state) const {
+  bool isWritable(Operation *op, Value value,
+                  const BufferizationState &state) const {
     return false;
   }
 
   LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
-                          BufferizationState &state) const {
+                          const BufferizationState &state) const {
     auto isaTensor = [](Type t) { return t.isa<TensorType>(); };
     if (any_of(op->getOperandTypes(), isaTensor) ||
         any_of(op->getResultTypes(), isaTensor))
       if (!state.getOptions().allowUnknownOps)
         return op->emitError() << "unsupported op with tensors";
 
     for (Region &region : op->getRegions())
       if (failed(comprehensive_bufferize::bufferize(rewriter, &region, state)))

mlir/include/mlir/Dialect/Linalg/ComprehensiveBufferize/BufferizableOpInterface.td

let methods = [
           even if it does actually not read; however, this can introduce
           unnecessary out-of-place bufferization decisions. The analysis of
           Comprehensive Bufferize considers OpOperands of unknown ops (that do
           not implement this interface) as reading OpOperands.
         }],
         /*retType=*/"bool",
         /*methodName=*/"bufferizesToMemoryRead",
         /*args=*/(ins "OpOperand &":$opOperand,
-                      "BufferizationState &":$state),
+                      "const BufferizationState &":$state),
         /*methodBody=*/"",
         /*defaultImplementation=*/[{
           // Does not have to be implemented for ops without tensor OpOperands.
           llvm_unreachable("bufferizesToMemoryRead not implemented");
          }]
       >,
       InterfaceMethod<
         /*desc=*/[{
let methods = [
           even if it does actually not write; however, this can introduce
           unnecessary out-of-place bufferization decisions. The analysis of
           Comprehensive Bufferize considers OpOperands of unknown ops (that do
           not implement this interface) as writing OpOperands.
         }],
         /*retType=*/"bool",
         /*methodName=*/"bufferizesToMemoryWrite",
         /*args=*/(ins "OpOperand &":$opOperand,
-                      "BufferizationState &":$state),
+                      "const BufferizationState &":$state),
         /*methodBody=*/"",
         /*defaultImplementation=*/[{
           // Does not have to be implemented for ops without tensor OpOperands.
           // Does not have to be implemented for OpOperands that do not have an
           // aliasing OpResult.
           llvm_unreachable("bufferizesToMemoryWrite not implemented");
          }]
       >,
       InterfaceMethod<
           /*desc=*/[{
             Return `true` if the given OpResult is a memory write. This is the
             case if in the following cases:
 
             * The corresponding aliasing OpOperand bufferizes to a memory write.
             * Or: There is no corresponding aliasing OpOperand.
 
             If the OpResult has multiple aliasing OpOperands, this method
             returns `true` if at least one of them bufferizes to a memory write.
           }],
           /*retType=*/"bool",
           /*methodName=*/"isMemoryWrite",
           /*args=*/(ins "OpResult":$opResult,
-                        "BufferizationState &":$state),
+                        "const BufferizationState &":$state),
           /*methodBody=*/"",
           /*defaultImplementation=*/[{
             auto bufferizableOp =
                 cast<BufferizableOpInterface>($_op.getOperation());
             SmallVector<OpOperand*> opOperands =
               bufferizableOp.getAliasingOpOperand(opResult, state);
             if (opOperands.empty())
               return true;
let methods = [
           have a tensor type.
 
           Note: This method may not return `true` if the given OpResult does not
           have an aliasing OpOperand.
         }],
         /*retType=*/"bool",
         /*methodName=*/"mustBufferizeInPlace",
         /*args=*/(ins "OpResult":$opResult,
-                      "BufferizationState &":$state),
+                      "const BufferizationState &":$state),
         /*methodBody=*/"",
         /*defaultImplementation=*/[{
           return false;
         }]
       >,
       InterfaceMethod<
         /*desc=*/[{
           Return the OpResult that aliases with a given OpOperand when
           bufferized in-place. This method will never be called on OpOperands
           that do not have a tensor type.
         }],
         /*retType=*/"OpResult",
         /*methodName=*/"getAliasingOpResult",
         /*args=*/(ins "OpOperand &":$opOperand,
-                      "BufferizationState &":$state),
+                      "const BufferizationState &":$state),
         /*methodBody=*/"",
         /*defaultImplementation=*/[{
           // Does not have to be implemented for ops without tensor OpOperands.
           llvm_unreachable("getAliasingOpResult not implemented");
         }]
       >,
       InterfaceMethod<
         /*desc=*/[{
           Return the OpOperands that alias with a given OpResult when
           bufferized in-place. This method will never be called on OpResults
           that do not have a tensor type.
 
           By default, this method is the inverse of `getAliasingOpResult`. Ops
           with a region that yield values may want to override this method to
           return the OpOperands that are yielded by the terminator.
 
           Note: This method can return multiple OpOperands, indicating that the
           given OpResult may at runtime alias with any of the OpOperands. This
           is useful for branches and for ops such as `std.select`.
         }],
         /*retType=*/"SmallVector<OpOperand *>",
         /*methodName=*/"getAliasingOpOperand",
         /*args=*/(ins "OpResult":$opResult,
-                      "BufferizationState &":$state),
+                      "const BufferizationState &":$state),
         /*methodBody=*/"",
         /*defaultImplementation=*/[{
           assert(opResult.getType().isa<TensorType>() &&
                  "expected OpResult with tensor type");
           SmallVector<OpOperand *> result;
           auto bufferizableOp =
               cast<BufferizableOpInterface>($_op.getOperation());
           for (OpOperand &opOperand : $_op.getOperation()->getOpOperands()) {
let methods = [
 
           TODO: Support other relations such as "OpOperand is included in
           OpResult".
         }],
         /*retType=*/"BufferRelation",
         /*methodName=*/"bufferRelation",
         /*args=*/(ins "OpResult":$opResult,
                       "const BufferizationAliasInfo &":$aliasInfo,
-                      "BufferizationState &":$state),
+                      "const BufferizationState &":$state),
         /*methodBody=*/"",
         /*defaultImplementation=*/[{
           // Does not have to be implemented for ops without tensor OpResults
           // that have an aliasing OpOperand.
           llvm_unreachable("bufferRelation not implemented");
         }]
       >,
       InterfaceMethod<
         /*desc=*/[{
           Bufferize this op, i.e., rewrite it into a memref-based equivalent.
           Tensor values should be mapped to buffer values using `state`.
 
           Implementations are required to required to bufferize nested ops
           before returning. Otherwise, nested ops will not be bufferized.
 
           This method will never be called on ops that do not have at least one
           tensor operand/result or a region.
         }],
         /*retType=*/"LogicalResult",
         /*methodName=*/"bufferize",
         /*args=*/(ins "RewriterBase &":$rewriter,
-                      "BufferizationState &":$state),
+                      "const BufferizationState &":$state),
         /*methodBody=*/"",
         /*defaultImplementation=*/[{
           llvm_unreachable("bufferize not implemented");
           return failure();
         }]
       >,
       InterfaceMethod<
         /*desc=*/[{
let methods = [
 
           Whether a BlockArgument can be written to or not depends on the
           operation. This method conservatively returns `false`. This method
           will never be called on BlockArguments that do not have a tensor type.
         }],
         /*retType=*/"bool",
         /*methodName=*/"isWritable",
         /*args=*/(ins "Value":$value,
-                      "BufferizationState &":$state),
+                      "const BufferizationState &":$state),
         /*methodBody=*/"",
         /*defaultImplementation=*/[{
           return value.isa<OpResult>();
         }]
       >,
       InterfaceMethod<
         /*desc=*/[{
           Return `true` if the op is an allocation hoisting barrier. Buffer
let methods = [
           If this method returns `true` the given OpOperands are not considered
           to be conflicting and do not force out-of-place bufferization. (There
           may still be other conflicts that do.)
         }],
         /*retType=*/"bool",
         /*methodName=*/"isNotConflicting",
         /*args=*/(ins "OpOperand *":$uRead,
                       "OpOperand *":$uWrite,
-                      "BufferizationState &":$state,
+                      "const BufferizationState &":$state,
                       "const BufferizationAliasInfo &":$aliasInfo),
         /*methodBody=*/"",
         /*defaultImplementation=*/[{
           return false;
         }]
       >
   ];
 
   let extraClassDeclaration = [{
     /// Return `true` if the given OpOperand creates an alias but does neither
     /// read nor write. This implies that `bufferizesToMemoryRead` and
     /// `bufferizesToMemoryWrite` must return `false`. This method will never
     /// be called on OpOperands that do not have a tensor type.
     ///
     /// Examples of such ops are `tensor.extract_slice` and `tensor.cast`.
     bool bufferizesToAliasOnly(OpOperand &opOperand,
-                               BufferizationState &state) {
+                               const BufferizationState &state) {
       auto bufferizableOp =
           cast<BufferizableOpInterface>(getOperation());
       return !bufferizableOp.bufferizesToMemoryRead(opOperand, state)
           && !bufferizableOp.bufferizesToMemoryWrite(opOperand, state)
           && static_cast<bool>(
               bufferizableOp.getAliasingOpResult(opOperand, state));
     }
 

mlir/lib/Dialect/Linalg/ComprehensiveBufferize/ArithInterfaceImpl.cpp

 namespace linalg {
 namespace comprehensive_bufferize {
 namespace arith_ext {
 
 struct ConstantOpInterface
     : public BufferizableOpInterface::ExternalModel<ConstantOpInterface,
                                                     arith::ConstantOp> {
   LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
-                          BufferizationState &state) const {
+                          const BufferizationState &state) const {
     auto constantOp = cast<arith::ConstantOp>(op);
     assert(constantOp.getType().dyn_cast<RankedTensorType>() &&
            "not a constant ranked tensor");
     auto moduleOp = constantOp->getParentOfType<ModuleOp>();
     if (!moduleOp)
       return constantOp.emitError(
           "cannot bufferize constants not within builtin.module op");
 
     GlobalCreator globalCreator(moduleOp);
     auto globalMemref = globalCreator.getGlobalFor(constantOp);
     state.replaceOpWithNewOp<memref::GetGlobalOp>(
         rewriter, op, globalMemref.type(), globalMemref.getName());
     return success();
   }
 
-  bool isWritable(Operation *op, Value value, BufferizationState &state) const {
+  bool isWritable(Operation *op, Value value,
+                  const BufferizationState &state) const {
     // Memory locations returned by memref::GetGlobalOp may not be written to.
     assert(value.isa<OpResult>());
     return false;
   }
 };
 
 } // namespace arith_ext
 } // namespace comprehensive_bufferize
 } // namespace linalg
 } // namespace mlir
 
 void mlir::linalg::comprehensive_bufferize::arith_ext::
     registerBufferizableOpInterfaceExternalModels(DialectRegistry &registry) {
   registry.addOpInterface<arith::ConstantOp, arith_ext::ConstantOpInterface>();
 }

mlir/lib/Dialect/Linalg/ComprehensiveBufferize/BufferizableOpInterface.cpp

if (isOpAllowed(bufferizableOp.getOperation()))
       return bufferizableOp;
   return nullptr;
 }
 
 /// Determine which OpOperand* will alias with `result` if the op is bufferized
 /// in place. Return an empty vector if the op is not bufferizable.
 SmallVector<OpOperand *>
 mlir::linalg::comprehensive_bufferize::BufferizationState::getAliasingOpOperand(
-    OpResult result) {
+    OpResult result) const {
   if (Operation *op = result.getDefiningOp())
     if (auto bufferizableOp = dyn_cast<BufferizableOpInterface>(op))
       return bufferizableOp.getAliasingOpOperand(result, *this);
   return {};
 }
 
 /// Determine which OpResult will alias with `opOperand` if the op is bufferized
 /// in place. Return an empty OpResult if the op is not bufferizable.
 OpResult
 mlir::linalg::comprehensive_bufferize::BufferizationState::getAliasingOpResult(
-    OpOperand &opOperand) {
+    OpOperand &opOperand) const {
   if (auto bufferizableOp =
           dyn_cast<BufferizableOpInterface>(opOperand.getOwner()))
     return bufferizableOp.getAliasingOpResult(opOperand, *this);
   return OpResult();
 }
 
 /// Return true if `opOperand` bufferizes to a memory read. Return `true` if the
 /// op is not bufferizable.
 bool mlir::linalg::comprehensive_bufferize::BufferizationState::
-    bufferizesToMemoryRead(OpOperand &opOperand) {
+    bufferizesToMemoryRead(OpOperand &opOperand) const {
   if (auto bufferizableOp =
           dyn_cast<BufferizableOpInterface>(opOperand.getOwner()))
     return bufferizableOp.bufferizesToMemoryRead(opOperand, *this);
 
   // Unknown op that returns a tensor. The inplace analysis does not support it.
   // Conservatively return true.
   return true;
 }
 
 /// Return true if `opOperand` bufferizes to a memory write. Return
 /// `true` if the op is not bufferizable.
 bool mlir::linalg::comprehensive_bufferize::BufferizationState::
-    bufferizesToMemoryWrite(OpOperand &opOperand) {
+    bufferizesToMemoryWrite(OpOperand &opOperand) const {
   if (auto bufferizableOp =
           dyn_cast<BufferizableOpInterface>(opOperand.getOwner()))
     return bufferizableOp.bufferizesToMemoryWrite(opOperand, *this);
 
   // Unknown op that returns a tensor. The inplace analysis does not support it.
   // Conservatively return true.
   return true;
 }
 
 /// Return true if `opOperand` does neither read nor write but bufferizes to an
 /// alias. Return false if the op is not bufferizable.
 bool mlir::linalg::comprehensive_bufferize::BufferizationState::
-    bufferizesToAliasOnly(OpOperand &opOperand) {
+    bufferizesToAliasOnly(OpOperand &opOperand) const {
   if (auto bufferizableOp =
           dyn_cast<BufferizableOpInterface>(opOperand.getOwner()))
     return bufferizableOp.bufferizesToAliasOnly(opOperand, *this);
 
   // Unknown op that returns a tensor. The inplace analysis does not support it.
   // Conservatively return false.
   return false;
 }
 
 /// Return true if the given value is read by an op that bufferizes to a memory
 /// read. Also takes into account ops that create an alias but do not read by
 /// themselves (e.g., ExtractSliceOp).
 bool mlir::linalg::comprehensive_bufferize::BufferizationState::isValueRead(
-    Value value) {
+    Value value) const {
   SmallVector<OpOperand *> workingSet;
   for (OpOperand &use : value.getUses())
     workingSet.push_back(&use);
 
   while (!workingSet.empty()) {
     OpOperand *uMaybeReading = workingSet.pop_back_val();
     // Skip over all ops that neither read nor write (but create an alias).
     if (bufferizesToAliasOnly(*uMaybeReading))
       for (OpOperand &use : getAliasingOpResult(*uMaybeReading).getUses())
         workingSet.push_back(&use);
     if (bufferizesToMemoryRead(*uMaybeReading))
       return true;
   }
 
   return false;
 }
 
 // Starting from `value`, follow the use-def chain in reverse, always selecting
 // the aliasing OpOperands. Find and return Values for which `condition`
 // evaluates to true. OpOperands of such matching Values are not traversed any
 // further.
-llvm::SetVector<Value>
-mlir::linalg::comprehensive_bufferize::BufferizationState::
-    findValueInReverseUseDefChain(Value value,
-                                  llvm::function_ref<bool(Value)> condition) {
+llvm::SetVector<Value> mlir::linalg::comprehensive_bufferize::
+    BufferizationState::findValueInReverseUseDefChain(
+        Value value, llvm::function_ref<bool(Value)> condition) const {
   llvm::SetVector<Value> result, workingSet;
   workingSet.insert(value);
 
   while (!workingSet.empty()) {
     Value value = workingSet.pop_back_val();
     if (condition(value) || value.isa<BlockArgument>()) {
       result.insert(value);
       continue;
for (OpOperand *o : opOperands)
       workingSet.insert(o->get());
   }
 
   return result;
 }
 
 // Find the Value of the last preceding write of a given Value.
 Value mlir::linalg::comprehensive_bufferize::BufferizationState::
-    findLastPrecedingWrite(Value value) {
+    findLastPrecedingWrite(Value value) const {
   SetVector<Value> result =
       findValueInReverseUseDefChain(value, [&](Value value) {
         Operation *op = value.getDefiningOp();
         if (!op)
           return true;
         auto bufferizableOp = options.dynCastBufferizableOp(op);
         if (!bufferizableOp)
           return true;
op->walk([&](BufferizableOpInterface bufferizableOp) {
     return WalkResult::advance();
   });
 }
 
 /// Return the result buffer (memref) for a given OpResult (tensor). Allocate
 /// a new buffer and copy over data from the existing buffer if out-of-place
 /// bufferization is necessary.
 Value mlir::linalg::comprehensive_bufferize::BufferizationState::
-    getResultBuffer(RewriterBase &rewriter, OpResult result) {
+    getResultBuffer(RewriterBase &rewriter, OpResult result) const {
   OpBuilder::InsertionGuard guard(rewriter);
   Operation *op = result.getOwner();
   SmallVector<OpOperand *> aliasingOperands = getAliasingOpOperand(result);
   assert(!aliasingOperands.empty() && "could not get aliasing OpOperand");
   OpOperand *opOperand = aliasingOperands.front();
   Value operand = opOperand->get();
   Value operandBuffer = lookupBuffer(rewriter, operand);
   // Make sure that all OpOperands are the same buffer. If this is not the case,
if (!aliasInfo.isInPlace(result)) {
     return resultBuffer;
   }
 
   // Bufferizing in-place. No need to allocate a new buffer.
   return operandBuffer;
 }
 
 void mlir::linalg::comprehensive_bufferize::BufferizationState::replaceOp(
-    RewriterBase &rewriter, Operation *op, ValueRange values) {
+    RewriterBase &rewriter, Operation *op, ValueRange values) const {
   OpBuilder::InsertionGuard g(rewriter);
 
   // Replace all OpResults with the given values.
   for (OpResult opResult : op->getOpResults()) {
     // Skip OpResult if it has no uses.
     if (opResult.getUses().empty())
       continue;
 
for (OpResult opResult : op->getOpResults()) {
     }
     opResult.replaceAllUsesWith(replacement);
   }
 
   rewriter.eraseOp(op);
 }
 
 LogicalResult mlir::linalg::comprehensive_bufferize::bufferize(
-    RewriterBase &rewriter, Region *region, BufferizationState &state) {
+    RewriterBase &rewriter, Region *region, const BufferizationState &state) {
   for (Block &block : *region)
     if (failed(bufferize(rewriter, &block, state)))
       return failure();
   return success();
 }
 
 LogicalResult mlir::linalg::comprehensive_bufferize::bufferize(
-    RewriterBase &rewriter, Block *block, BufferizationState &state) {
+    RewriterBase &rewriter, Block *block, const BufferizationState &state) {
   // Ops may get deleted during the traversal, so do not iterate over `block`
   // directly.
   SmallVector<Operation *> ops;
   ops.reserve(block->getOperations().size());
   for (Operation &op : *block)
     ops.push_back(&op);
   for (Operation *op : ops)
     if (failed(bufferize(rewriter, op, state)))
       return failure();
   return success();
 }
 
 LogicalResult mlir::linalg::comprehensive_bufferize::bufferize(
-    RewriterBase &rewriter, Operation *op, BufferizationState &state) {
+    RewriterBase &rewriter, Operation *op, const BufferizationState &state) {
   // Check if op has tensor results or operands.
   auto isaTensor = [](Type t) { return t.isa<TensorType>(); };
   bool hasTensorResult = any_of(op->getResultTypes(), isaTensor);
   bool hasTensorOperand = any_of(op->getOperandTypes(), isaTensor);
   bool hasRegions = !op->getRegions().empty();
 
   // No tensor results/operands or regions. We are done.
   if (!hasTensorResult && !hasTensorOperand && !hasRegions)
static MemRefType getAllocationTypeAndShape(OpBuilder &b, Location loc,
 
   return allocMemRefType;
 }
 
 /// Create an Allocop/DeAllocOp pair, where the AllocOp is after
 /// `shapedValue.getDefiningOp` (or at the top of the block in case of a
 /// bbArg) and the DeallocOp is at the end of the block.
 Value mlir::linalg::comprehensive_bufferize::BufferizationState::
-    createAllocDeallocPair(OpBuilder &b, Location loc, Value shapedValue) {
+    createAllocDeallocPair(OpBuilder &b, Location loc,
+                           Value shapedValue) const {
   // Take a guard before anything else.
   OpBuilder::InsertionGuard g(b);
 
   // 1. Create memory allocation.
   assert(shapedValue.getType().isa<ShapedType>());
   MemRefType memRefType = shapedValue.getType().dyn_cast<MemRefType>();
   SmallVector<Value> dynShape;
   // Note: getAllocationTypeAndShape also sets the insertion point.
Value mlir::linalg::comprehensive_bufferize::BufferizationState::
   b.setInsertionPoint(allocated.getValue().getParentBlock()->getTerminator());
   createDealloc(b, loc, allocated.getValue());
   return casted;
 }
 
 /// Create a memref allocation.
 Optional<Value>
 mlir::linalg::comprehensive_bufferize::BufferizationState::createAlloc(
-    OpBuilder &b, Location loc, MemRefType type, ArrayRef<Value> dynShape) {
+    OpBuilder &b, Location loc, MemRefType type,
+    ArrayRef<Value> dynShape) const {
   return options.allocationFns->allocationFn(b, loc, type, dynShape);
 }
 
 /// Create a memref deallocation.
 void mlir::linalg::comprehensive_bufferize::BufferizationState::createDealloc(
-    OpBuilder &b, Location loc, Value allocatedBuffer) {
+    OpBuilder &b, Location loc, Value allocatedBuffer) const {
   return options.allocationFns->deallocationFn(b, loc, allocatedBuffer);
 }
 
 /// Create a memory copy between two memref buffers.
 void mlir::linalg::comprehensive_bufferize::BufferizationState::createMemCpy(
-    OpBuilder &b, Location loc, Value from, Value to) {
+    OpBuilder &b, Location loc, Value from, Value to) const {
   return options.allocationFns->memCpyFn(b, loc, from, to);
 }
 
 //===----------------------------------------------------------------------===//
 // Bufferization-specific BlockAndValueMapping support with debugging.
 //===----------------------------------------------------------------------===//
 
 bool mlir::linalg::comprehensive_bufferize::isFunctionArgument(Value value) {
   auto bbArg = value.dyn_cast<BlockArgument>();
   if (!bbArg)
     return false;
   return isa<FuncOp>(bbArg.getOwner()->getParentOp());
 }
 
 Value mlir::linalg::comprehensive_bufferize::BufferizationState::lookupBuffer(
-    RewriterBase &rewriter, Value tensor) {
+    RewriterBase &rewriter, Value tensor) const {
   assert(tensor.getType().isa<TensorType>() && "unexpected non-tensor type");
 
   // Replace "%t = to_tensor %m" with %m.
   if (auto toTensorOp = tensor.getDefiningOp<bufferization::ToTensorOp>())
     return toTensorOp.memref();
 
   if (!isFunctionArgument(tensor)) {
     if (static_cast<bool>(options.dynCastBufferizableOp(tensor))) {

mlir/lib/Dialect/Linalg/ComprehensiveBufferize/BufferizationInterfaceImpl.cpp

 /// bufferization boundary. When bufferization is complete, there should be no
 /// such ops left over. If `allowUnknownOps`, such ops may be part of the
 /// resulting IR, but such IR may no longer be bufferizable by Comprehensive
 /// Bufferize.
 struct ToMemrefOpInterface
     : public BufferizableOpInterface::ExternalModel<ToMemrefOpInterface,
                                                     bufferization::ToMemrefOp> {
   bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
-                              BufferizationState &state) const {
+                              const BufferizationState &state) const {
     // It is unknown whether the resulting MemRef will be read or not.
     return true;
   }
 
   OpResult getAliasingOpResult(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     return OpResult();
   }
 
   LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
-                          BufferizationState &state) const {
+                          const BufferizationState &state) const {
     auto toMemrefOp = cast<bufferization::ToMemrefOp>(op);
 
     // Fold to_memref(to_tensor(x)) to x.
     if (auto toTensorOp =
             toMemrefOp.tensor().getDefiningOp<bufferization::ToTensorOp>()) {
       rewriter.replaceOp(toMemrefOp, toTensorOp.memref());
       return success();
     }

 /// potentially alias with any other bufferized tensor. Since ToTensorOp and
 /// ToMemrefOp have no aliasing OpOperand/OpResult pairs, this cannot be encoded
 /// directly in the analysis. However, declaring ToTensorOp results as not
 /// writable also enforces a buffer copy and has the same effect.
 struct ToTensorOpInterface
     : public BufferizableOpInterface::ExternalModel<ToTensorOpInterface,
                                                     bufferization::ToTensorOp> {
   LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
-                          BufferizationState &state) const {
+                          const BufferizationState &state) const {
     return success();
   }
 
-  bool isWritable(Operation *op, Value value, BufferizationState &state) const {
+  bool isWritable(Operation *op, Value value,
+                  const BufferizationState &state) const {
     // It is unknown whether the MemRef operand is writable or not.
     return false;
   }
 };
 
 } // namespace bufferization_ext
 } // namespace comprehensive_bufferize
 } // namespace linalg

mlir/lib/Dialect/Linalg/ComprehensiveBufferize/ComprehensiveBufferize.cpp

 }
 
 LogicalResult mlir::linalg::comprehensive_bufferize::runComprehensiveBufferize(
     Operation *op, const BufferizationOptions &options,
     BufferizationState &state) {
 
   IRRewriter rewriter(op->getContext());
   DominanceInfo domInfo(op);
-  BufferizationAliasInfo &aliasInfo = state.aliasInfo;
+  BufferizationAliasInfo &aliasInfo = state.getAliasInfo();
 
   if (failed(checkAliasInfoConsistency(op, domInfo, state, aliasInfo)))
     return failure();
 
   // If the analysis fails, just return.
   if (failed(inPlaceAnalysis(op, aliasInfo, state, domInfo,
                              options.analysisFuzzerSeed)))
     return failure();

mlir/lib/Dialect/Linalg/ComprehensiveBufferize/LinalgInterfaceImpl.cpp

 using namespace comprehensive_bufferize;
 
 namespace {
 
 // TODO: Ops in the linalg dialect can directly implement this interface.
 
 /// Generic conversion for any LinalgOp on tensors.
 static LogicalResult bufferizeLinalgOp(RewriterBase &rewriter, LinalgOp op,
-                                       BufferizationState &state) {
+                                       const BufferizationState &state) {
   // Take a guard before anything else.
   OpBuilder::InsertionGuard g(rewriter);
   rewriter.setInsertionPoint(op);
 
   // Nothing to do. This op is already bufferized.
   if (op.hasBufferSemantics())
     return success();
 
static DenseMap<OpOperand *, OpResult> computeAliasingPairs(LinalgOp op) {
   return mapping;
 }
 
 template <typename OpTy>
 struct LinalgOpInterface
     : public BufferizableOpInterface::ExternalModel<LinalgOpInterface<OpTy>,
                                                     OpTy> {
   bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
-                              BufferizationState &state) const {
+                              const BufferizationState &state) const {
     auto genericOp = cast<linalg::LinalgOp>(op);
     return genericOp.payloadUsesValueFromOperand(&opOperand);
   }
 
   bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     auto bufferizableOp = cast<BufferizableOpInterface>(op);
     return static_cast<bool>(
         bufferizableOp.getAliasingOpResult(opOperand, state));
   }
 
   SmallVector<OpOperand *>
   getAliasingOpOperand(Operation *op, OpResult opResult,
-                       BufferizationState &state) const {
+                       const BufferizationState &state) const {
     auto genericOp = cast<linalg::LinalgOp>(op);
     DenseMap<OpOperand *, OpResult> pairs = computeAliasingPairs(genericOp);
     for (OpOperand *opOperand : genericOp.getInputAndOutputOperands())
       if (pairs[opOperand] == opResult)
         return {opOperand};
     return {};
   }
 
   OpResult getAliasingOpResult(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     auto genericOp = cast<linalg::LinalgOp>(op);
     DenseMap<OpOperand *, OpResult> pairs = computeAliasingPairs(genericOp);
     return pairs[&opOperand];
   }
 
   BufferRelation bufferRelation(Operation *op, OpResult opResult,
                                 const BufferizationAliasInfo &aliasInfo,
-                                BufferizationState &state) const {
+                                const BufferizationState &state) const {
     return BufferRelation::Equivalent;
   }
 
   LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
-                          BufferizationState &state) const {
+                          const BufferizationState &state) const {
     return bufferizeLinalgOp(rewriter, cast<LinalgOp>(op), state);
   }
 };
 
 struct InitTensorOpInterface
     : public BufferizableOpInterface::ExternalModel<InitTensorOpInterface,
                                                     linalg::InitTensorOp> {
   bool isMemoryWrite(Operation *op, OpResult opResult,
-                     BufferizationState &state) const {
+                     const BufferizationState &state) const {
     // InitTensorOps allocate but do not write.
     return false;
   }
 
   LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
-                          BufferizationState &state) const {
+                          const BufferizationState &state) const {
     auto initTensorOp = cast<linalg::InitTensorOp>(op);
 
     // The InitTensorOp may have been eliminated.
     if (initTensorOp->getUses().empty())
       return success();
 
     Value alloc = state.createAllocDeallocPair(rewriter, initTensorOp->getLoc(),
                                                initTensorOp.result());
     state.replaceOp(rewriter, op, alloc);
     return success();
   }
 };
 
 struct TiledLoopOpInterface
     : public BufferizableOpInterface::ExternalModel<TiledLoopOpInterface,
                                                     linalg::TiledLoopOp> {
   bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
-                              BufferizationState &state) const {
+                              const BufferizationState &state) const {
     // TiledLoop alone doesn't bufferize to a memory read, one of the uses of
     // its matching bbArg may.
     auto tiledLoopOp = cast<linalg::TiledLoopOp>(op);
     return state.isValueRead(tiledLoopOp.getTiedBlockArgument(opOperand));
   }
 
   bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     // TiledLoop alone doesn't bufferize to a memory write, one of the uses of
     // its matching bbArg may.
     auto bufferizableOp = cast<BufferizableOpInterface>(op);
     return static_cast<bool>(
         bufferizableOp.getAliasingOpResult(opOperand, state));
   }
 
   OpResult getAliasingOpResult(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     auto tiledLoopOp = cast<linalg::TiledLoopOp>(op);
     return tiledLoopOp.getTiedOpResult(opOperand);
   }
 
   BufferRelation bufferRelation(Operation *op, OpResult opResult,
                                 const BufferizationAliasInfo &aliasInfo,
-                                BufferizationState &state) const {
+                                const BufferizationState &state) const {
     return BufferRelation::Equivalent;
   }
 
-  bool isWritable(Operation *op, Value value, BufferizationState &state) const {
+  bool isWritable(Operation *op, Value value,
+                  const BufferizationState &state) const {
     // Interestingly, linalg::TiledLoopOp's bbArg can **always** be viewed
     // inplace from the perspective of ops nested under:
     //   1. Either the matching iter operand is not bufferized inplace and an
     //      alloc + optional copy makes the bbArg itself inplaceable.
     //   2. Or the matching iter operand is bufferized inplace and bbArg just
     //      bufferizes to that too.
     return true;
   }
 
   bool isAllocationHoistingBarrier(Operation *op) const { return true; }
 
   LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
-                          BufferizationState &state) const {
+                          const BufferizationState &state) const {
     auto tiledLoopOp = cast<linalg::TiledLoopOp>(op);
 
     // Compute new inputs, outputs and results.
     SmallVector<Value> newInputs, newOutputs, newResults;
     for (Value value : tiledLoopOp.inputs()) {
       if (value.getType().isa<TensorType>()) {
         newInputs.push_back(state.lookupBuffer(rewriter, value));
       } else {
return comprehensive_bufferize::bufferize(rewriter,
                                               newTiledLoopOp.getBody(), state);
   }
 };
 
 struct YieldOpInterface
     : public BufferizableOpInterface::ExternalModel<YieldOpInterface,
                                                     linalg::YieldOp> {
   bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
-                              BufferizationState &state) const {
+                              const BufferizationState &state) const {
     return true;
   }
 
   bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     return false;
   }
 
   OpResult getAliasingOpResult(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     return OpResult();
   }
 
   LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
-                          BufferizationState &state) const {
+                          const BufferizationState &state) const {
     auto yieldOp = cast<linalg::YieldOp>(op);
 
     if (!yieldOp->getParentOfType<TiledLoopOp>())
       return yieldOp->emitError(
           "expected that linalg.yield terminates a tiled_loop");
 
     assert(yieldOp->getOpOperands().empty() &&
            "expected that linalg.yield was bufferized together with"

mlir/lib/Dialect/Linalg/ComprehensiveBufferize/ModuleBufferization.cpp

struct ModuleBufferizationState : public DialectBufferizationState {
   DenseMap<FuncOp, DenseMap<int64_t, int64_t>> equivalentFuncArgs;
 
   SmallVector<FuncOp> orderedFuncOps;
 
   DenseMap<FuncOp, DenseSet<Operation *>> callerMap;
 };
 } // namespace
 
+/// Get ModuleBufferizationState.
+static const ModuleBufferizationState &
+getModuleBufferizationState(const BufferizationState &state) {
+  Optional<const ModuleBufferizationState *> maybeState =
+      state.getDialectState<ModuleBufferizationState>(
+          StandardOpsDialect::getDialectNamespace());
+  assert(maybeState.hasValue() && "ModuleBufferizationState does not exist");
+  return **maybeState;
+}
+
+/// Get or create ModuleBufferizationState.
 static ModuleBufferizationState &
 getModuleBufferizationState(BufferizationState &state) {
-  return state.getDialectState<ModuleBufferizationState>(
+  return state.getOrCreateDialectState<ModuleBufferizationState>(
       StandardOpsDialect::getDialectNamespace());
 }
 
 /// Return the unique ReturnOp that terminates `funcOp`.
 /// Return nullptr if there is no such unique ReturnOp.
 static ReturnOp getAssumedUniqueReturnOp(FuncOp funcOp) {
   ReturnOp returnOp;
   for (Block &b : funcOp.body()) {

 namespace mlir {
 namespace linalg {
 namespace comprehensive_bufferize {
 namespace std_ext {
 
 /// Return the index of the bbArg in the given FuncOp that is equivalent to the
 /// specified return value (if any).
 static Optional<int64_t>
-getEquivalentFuncArgIdx(FuncOp funcOp, ModuleBufferizationState &state,
+getEquivalentFuncArgIdx(FuncOp funcOp, const ModuleBufferizationState &state,
                         int64_t returnValIdx) {
-  if (!state.equivalentFuncArgs[funcOp].count(returnValIdx))
+  if (!state.equivalentFuncArgs.count(funcOp))
+    // No equivalence info stores for funcOp.
+    return None;
+
+  const DenseMap<int64_t, int64_t> &equivFuncArgs =
+      state.equivalentFuncArgs.lookup(funcOp);
+  if (!equivFuncArgs.count(returnValIdx))
     // Return value has no equivalent bbArg.
     return None;
 
-  return state.equivalentFuncArgs[funcOp][returnValIdx];
+  return equivFuncArgs.lookup(returnValIdx);
 }
 
 struct CallOpInterface
     : public BufferizableOpInterface::ExternalModel<CallOpInterface, CallOp> {
   bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
-                              BufferizationState &state) const {
+                              const BufferizationState &state) const {
     // CallOpInterface alone doesn't bufferize to a memory read, one of the uses
     // of the matching bbArg may. It is the responsibility of the caller to
     // inspect bbArgs. In the absence of a BufferizationAliasInfo, we need to be
     // conservative.
     return true;
   }
 
   OpResult getAliasingOpResult(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     // CallOpInterface is special, it needs to wait for the callee to be
     // bufferized and needs to inspect the BufferAliasInfo object. It can't
     // make a proper determination by itself and needs to be conservative.
     return OpResult();
   }
 
   /// In a first approximation, all the function arguments of a FuncOp are
   /// marked inplaceable. For now, it is the responsibility of the `callOp`
   /// bufferization to allow FuncOp that are inplaceable to write inPlace.
   LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
-                          BufferizationState &state) const {
+                          const BufferizationState &state) const {
     CallOp callOp = cast<CallOp>(op);
     unsigned numResults = callOp.getNumResults();
     unsigned numOperands = callOp->getNumOperands();
     FuncOp funcOp = getCalledFunction(callOp);
     assert(isa<CallOp>(callOp.getOperation()) && funcOp &&
            "expected CallOp to a FuncOp");
-    ModuleBufferizationState &moduleState = getModuleBufferizationState(state);
+    const ModuleBufferizationState &moduleState =
+        getModuleBufferizationState(state);
 
     // Result types of the bufferized CallOp.
     SmallVector<Type> resultTypes;
     // Replacement values for the existing CallOp. These are usually the results
     // of the bufferized CallOp, unless a tensor result folds onto an operand.
     SmallVector<Value> replacementValues(numResults, Value());
     // For non-tensor results: A mapping from return val indices of the old
     // CallOp to return val indices of the bufferized CallOp.
LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
     return success();
   }
 };
 
 struct ReturnOpInterface
     : public BufferizableOpInterface::ExternalModel<ReturnOpInterface,
                                                     ReturnOp> {
   bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
-                              BufferizationState &state) const {
+                              const BufferizationState &state) const {
     return true;
   }
 
   bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     return false;
   }
 
   OpResult getAliasingOpResult(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     return OpResult();
   }
 
   LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
-                          BufferizationState &state) const {
+                          const BufferizationState &state) const {
     auto returnOp = cast<ReturnOp>(op);
     assert(isa<FuncOp>(returnOp->getParentOp()) &&
            "only support FuncOp parent for ReturnOp");
 
     for (OpOperand &operand : returnOp->getOpOperands()) {
       auto tensorType = operand.get().getType().dyn_cast<TensorType>();
       if (!tensorType)
         continue;
       Value v = state.lookupBuffer(rewriter, operand.get());
       Value returnTensor =
           rewriter.create<bufferization::ToTensorOp>(returnOp.getLoc(), v);
       operand.set(returnTensor);
     }
     return success();
   }
 };
 
 struct FuncOpInterface
     : public BufferizableOpInterface::ExternalModel<FuncOpInterface, FuncOp> {
   LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
-                          BufferizationState &state) const {
+                          const BufferizationState &state) const {
     auto funcOp = cast<FuncOp>(op);
 
     // Bufferize function body.
     return comprehensive_bufferize::bufferize(rewriter, &funcOp.body(), state);
   }
 
   /// Return `true` if the given function argument is writable.
-  bool isWritable(Operation *op, Value value, BufferizationState &state) const {
+  bool isWritable(Operation *op, Value value,
+                  const BufferizationState &state) const {
     auto funcOp = cast<FuncOp>(op);
     BlockArgument bbArg = value.dyn_cast<BlockArgument>();
     assert(bbArg && "expected BlockArgument");
-    ModuleBufferizationState &moduleState = getModuleBufferizationState(state);
+    const ModuleBufferizationState &moduleState =
+        getModuleBufferizationState(state);
 
     // In a first approximation:
     // =========================
     // If the function is called, we can allocate on the caller side which lets
     // us force inplace arguments at function boundaries.
     // TODO: do not rely on this behavior.
     if (moduleState.callerMap.find(funcOp) != moduleState.callerMap.end())
       return true;

 static void setInPlaceFuncArgument(BlockArgument bbArg, bool inPlace) {
   auto funcOp = cast<FuncOp>(bbArg.getOwner()->getParentOp());
   funcOp.setArgAttr(bbArg.getArgNumber(),
                     BufferizableOpInterface::kInplaceableAttrName,
                     BoolAttr::get(bbArg.getContext(), inPlace));
 }
 
 /// Annotate the IR with the result of the analysis. For testing/debugging only.
-static void annotateOpsWithBufferizationMarkers(FuncOp funcOp,
-                                                BufferizationState &state) {
+static void
+annotateOpsWithBufferizationMarkers(FuncOp funcOp,
+                                    const BufferizationState &state) {
   auto bufferizableOp = cast<BufferizableOpInterface>(funcOp.getOperation());
   for (BlockArgument bbArg : funcOp.getArguments())
     if (bbArg.getType().isa<TensorType>())
       setInPlaceFuncArgument(bbArg, bufferizableOp.isWritable(bbArg, state));
 }
 
 LogicalResult mlir::linalg::comprehensive_bufferize::runComprehensiveBufferize(
     ModuleOp moduleOp, std::unique_ptr<BufferizationOptions> options) {
   IRRewriter rewriter(moduleOp.getContext());
   BufferizationState state(moduleOp, *options);
   ModuleBufferizationState &moduleState = getModuleBufferizationState(state);
-  BufferizationAliasInfo &aliasInfo = state.aliasInfo;
+  BufferizationAliasInfo &aliasInfo = state.getAliasInfo();
 
   if (failed(getFuncOpsOrderedByCalls(moduleOp, moduleState.orderedFuncOps,
                                       moduleState.callerMap)))
     return failure();
 
   // Interestingly, all function args that are not visible outside of a module
   // can be fully bufferized inplace by guaranteeing the CallOp is bufferized
   // inplace. Therefore, we just bufferize funcOp as if none of its results were

mlir/lib/Dialect/Linalg/ComprehensiveBufferize/SCFInterfaceImpl.cpp

 namespace comprehensive_bufferize {
 namespace scf_ext {
 
 struct ExecuteRegionOpInterface
     : public BufferizableOpInterface::ExternalModel<ExecuteRegionOpInterface,
                                                     scf::ExecuteRegionOp> {
   SmallVector<OpOperand *>
   getAliasingOpOperand(Operation *op, OpResult opResult,
-                       BufferizationState &state) const {
+                       const BufferizationState &state) const {
     // ExecuteRegionOps do not have tensor OpOperands. The yielded value can be
     // any SSA value that is in scope. To allow for use-def chain traversal
     // through ExecuteRegionOps in the analysis, the corresponding yield value
     // is considered to be aliasing with the result.
     auto executeRegionOp = cast<scf::ExecuteRegionOp>(op);
     size_t resultNum = std::distance(op->getOpResults().begin(),
                                      llvm::find(op->getOpResults(), opResult));
     assert(executeRegionOp.getRegion().getBlocks().size() == 1 &&
            "expected exactly 1 block");
     auto yieldOp = dyn_cast<scf::YieldOp>(
         executeRegionOp.getRegion().front().getTerminator());
     assert(yieldOp && "expected scf.yield terminator in scf.execute_region");
     return {&yieldOp->getOpOperand(resultNum)};
   }
 
   bool mustBufferizeInPlace(Operation *op, OpResult opResult,
-                            BufferizationState &state) const {
+                            const BufferizationState &state) const {
     // ExecuteRegionOp results always bufferize in-place. Since they have no
     // OpOperands, they are mostly ignored by the analysis once alias sets are
     // set up.
     return true;
   }
 
   // TODO: For better bufferization results, this could return `true` only if
   // there is a memory write in the region.
   bool isMemoryWrite(Operation *op, OpResult opResult,
-                     BufferizationState &state) const {
+                     const BufferizationState &state) const {
     // Similar to scf.if, results of this op are always considered memory writes
     // in the analysis. This is a useful pattern for all ops that have tensor
     // OpResults but no tensor OpOperands. By default, `isMemoryWrite` is
     // implemented in terms of `bufferizesToMemoryWrite`, which does not work on
     // ops without OpOperands.
     return true;
   }
 
   LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
-                          BufferizationState &state) const {
+                          const BufferizationState &state) const {
     // TODO: Add bufferization support when needed. scf.execute_region should be
     // bufferized similar to scf.if.
     auto executeRegionOp = cast<scf::ExecuteRegionOp>(op);
     bool hasTensorReturnType = any_of(
         op->getResultTypes(), [](Type t) { return t.isa<TensorType>(); });
     if (hasTensorReturnType)
       return op->emitError(
           "scf.execute_region with tensor result not supported");
     return comprehensive_bufferize::bufferize(
         rewriter, &executeRegionOp.getRegion(), state);
   }
 
   BufferRelation bufferRelation(Operation *op, OpResult opResult,
                                 const BufferizationAliasInfo &aliasInfo,
-                                BufferizationState &state) const {
+                                const BufferizationState &state) const {
     return BufferRelation::Equivalent;
   }
 };
 
 struct IfOpInterface
     : public BufferizableOpInterface::ExternalModel<IfOpInterface, scf::IfOp> {
   SmallVector<OpOperand *>
   getAliasingOpOperand(Operation *op, OpResult opResult,
-                       BufferizationState &state) const {
+                       const BufferizationState &state) const {
     // IfOps do not have tensor OpOperands. The yielded value can be any SSA
     // value that is in scope. To allow for use-def chain traversal through
     // IfOps in the analysis, both corresponding yield values from the then/else
     // branches are considered to be aliasing with the result.
     auto ifOp = cast<scf::IfOp>(op);
     size_t resultNum = std::distance(op->getOpResults().begin(),
                                      llvm::find(op->getOpResults(), opResult));
     return {&ifOp.thenYield()->getOpOperand(resultNum),
             &ifOp.elseYield()->getOpOperand(resultNum)};
   }
 
   // TODO: For better bufferization results, this could return `true` only if
   // there is a memory write in one (or both) of the branches. Since this is not
   // allowed at the moment, we should never encounter scf.ifs that yield
   // unmodified tensors. Such scf.yield ops could just fold away.
   bool isMemoryWrite(Operation *op, OpResult opResult,
-                     BufferizationState &state) const {
+                     const BufferizationState &state) const {
     // IfOp results are always considered memory writes in the analysis. This
     // design decision simplifies the analysis considerably. E.g., consider the
     // following test case:
     //
     // %0 = "some_writing_op" : tensor<?xf32>
     // %r = scf.if %c -> (tensor<?xf32>) {
     //   scf.yield %0
     // } else {
bool isMemoryWrite(Operation *op, OpResult opResult,
     // * lastWrite = %0  (Note: The last write of %r would be a set: {%0, %1}.)
     // * conflictingWrite = %1
     //
     // For more details, check the "scf.IfOp" section of the design document.
     return true;
   }
 
   bool mustBufferizeInPlace(Operation *op, OpResult opResult,
-                            BufferizationState &state) const {
+                            const BufferizationState &state) const {
     // IfOp results always bufferize in-place. Since they have no OpOperands,
     // they are mostly ignored by the analysis once alias sets are set up.
     return true;
   }
 
   LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
-                          BufferizationState &state) const {
+                          const BufferizationState &state) const {
     auto ifOp = cast<scf::IfOp>(op);
 
     // Compute new types of the bufferized scf.if op.
     SmallVector<Type> newTypes;
     for (Type returnType : ifOp->getResultTypes()) {
       if (returnType.isa<TensorType>()) {
         assert(returnType.isa<RankedTensorType>() &&
                "unsupported unranked tensor");
if (failed(comprehensive_bufferize::bufferize(rewriter, newIfOp.elseBlock(),
                                                   state)))
       return failure();
 
     return success();
   }
 
   BufferRelation bufferRelation(Operation *op, OpResult opResult,
                                 const BufferizationAliasInfo &aliasInfo,
-                                BufferizationState &state) const {
+                                const BufferizationState &state) const {
     // IfOp results are equivalent to their corresponding yield values if both
     // yield values are equivalent to each other.
     auto bufferizableOp = cast<BufferizableOpInterface>(op);
     SmallVector<OpOperand *> yieldValues =
         bufferizableOp.getAliasingOpOperand(opResult, state);
     assert(yieldValues.size() == 2 && "expected 2 yield values");
     bool equivalentYields = aliasInfo.areEquivalentBufferizedValues(
         yieldValues[0]->get(), yieldValues[1]->get());
     return equivalentYields ? BufferRelation::Equivalent : BufferRelation::None;
   }
 };
 
 struct ForOpInterface
     : public BufferizableOpInterface::ExternalModel<ForOpInterface,
                                                     scf::ForOp> {
   bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
-                              BufferizationState &state) const {
+                              const BufferizationState &state) const {
     // scf::ForOp alone doesn't bufferize to a memory read, one of the uses of
     // its matching bbArg may.
     auto forOp = cast<scf::ForOp>(op);
     return state.isValueRead(forOp.getRegionIterArgForOpOperand(opOperand));
   }
 
   bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     // Tensor iter_args of scf::ForOps are always considered as a write. This is
     // to simplify the analysis.
     // TODO: Consider doing sth. like isValueWritten.
     return true;
   }
 
   OpResult getAliasingOpResult(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     auto forOp = cast<scf::ForOp>(op);
     if (!opOperand.get().getType().isa<RankedTensorType>())
       return OpResult();
     return forOp.getResultForOpOperand(opOperand);
   }
 
   BufferRelation bufferRelation(Operation *op, OpResult opResult,
                                 const BufferizationAliasInfo &aliasInfo,
-                                BufferizationState &state) const {
+                                const BufferizationState &state) const {
     // ForOp results are equivalent to their corresponding init_args if the
     // corresponding iter_args and yield values are equivalent.
     auto forOp = cast<scf::ForOp>(op);
     OpOperand &forOperand = forOp.getOpOperandForResult(opResult);
     auto bbArg = forOp.getRegionIterArgForOpOperand(forOperand);
     auto yieldOp = cast<scf::YieldOp>(&forOp.getLoopBody().front().back());
     bool equivalentYield = aliasInfo.areEquivalentBufferizedValues(
         bbArg, yieldOp->getOperand(opResult.getResultNumber()));
     return equivalentYield ? BufferRelation::Equivalent : BufferRelation::None;
   }
 
-  bool isWritable(Operation *op, Value value, BufferizationState &state) const {
+  bool isWritable(Operation *op, Value value,
+                  const BufferizationState &state) const {
     // Interestingly, scf::ForOp's bbArg can **always** be viewed
     // inplace from the perspective of ops nested under:
     //   1. Either the matching iter operand is not bufferized inplace and an
     //      alloc + optional copy makes the bbArg itself inplaceable.
     //   2. Or the matching iter operand is bufferized inplace and bbArg just
     //      bufferizes to that too.
     return true;
   }
 
   LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
-                          BufferizationState &state) const {
+                          const BufferizationState &state) const {
     auto forOp = cast<scf::ForOp>(op);
     Block *oldLoopBody = &forOp.getLoopBody().front();
 
     // Indices of all iter_args that have tensor type. These are the ones that
     // are bufferized.
     DenseSet<int64_t> indices;
     for (const auto &it : llvm::enumerate(forOp.getInitArgs()))
       if (it.value().getType().isa<TensorType>())
LogicalResult mlir::linalg::comprehensive_bufferize::scf_ext::
   });
   return status;
 }
 
 struct YieldOpInterface
     : public BufferizableOpInterface::ExternalModel<YieldOpInterface,
                                                     scf::YieldOp> {
   bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
-                              BufferizationState &state) const {
+                              const BufferizationState &state) const {
     return true;
   }
 
   bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     return false;
   }
 
   OpResult getAliasingOpResult(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     return OpResult();
   }
 
   LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
-                          BufferizationState &state) const {
+                          const BufferizationState &state) const {
     auto yieldOp = cast<scf::YieldOp>(op);
     if (!isa<scf::ExecuteRegionOp, scf::IfOp, scf::ForOp>(
             yieldOp->getParentOp()))
       return yieldOp->emitError("unsupported scf::YieldOp parent");
     return success();
   }
 };
 

mlir/lib/Dialect/Linalg/ComprehensiveBufferize/TensorInterfaceImpl.cpp

 
 using tensor::ExtractSliceOp;
 using tensor::InsertSliceOp;
 
 struct CastOpInterface
     : public BufferizableOpInterface::ExternalModel<CastOpInterface,
                                                     tensor::CastOp> {
   bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
-                              BufferizationState &state) const {
+                              const BufferizationState &state) const {
     return false;
   }
 
   bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     return false;
   }
 
   OpResult getAliasingOpResult(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     return op->getResult(0);
   }
 
   BufferRelation bufferRelation(Operation *op, OpResult opResult,
                                 const BufferizationAliasInfo &aliasInfo,
-                                BufferizationState &state) const {
+                                const BufferizationState &state) const {
     return BufferRelation::Equivalent;
   }
 
   LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
-                          BufferizationState &state) const {
+                          const BufferizationState &state) const {
     auto castOp = cast<tensor::CastOp>(op);
 
     Value resultBuffer = state.getResultBuffer(rewriter, castOp->getResult(0));
     if (!resultBuffer)
       return failure();
     Type sourceType = resultBuffer.getType();
     auto rankedMemRefType = sourceType.dyn_cast<MemRefType>();
     auto unrankedMemRefType = sourceType.dyn_cast<UnrankedMemRefType>();
LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
     return success();
   }
 };
 
 struct DimOpInterface
     : public BufferizableOpInterface::ExternalModel<DimOpInterface,
                                                     tensor::DimOp> {
   bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
-                              BufferizationState &state) const {
+                              const BufferizationState &state) const {
     return true;
   }
 
   bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     return false;
   }
 
   OpResult getAliasingOpResult(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     return OpResult();
   }
 
   LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
-                          BufferizationState &state) const {
+                          const BufferizationState &state) const {
     auto dimOp = cast<tensor::DimOp>(op);
     if (!dimOp.source().getType().isa<RankedTensorType>())
       return dimOp.emitError("unranked tensor not supported");
     Value v = state.lookupBuffer(rewriter, dimOp.source());
     state.replaceOpWithNewOp<memref::DimOp>(rewriter, op, v, dimOp.index());
     return success();
   }
 };
 
 struct ExtractSliceOpInterface
     : public BufferizableOpInterface::ExternalModel<ExtractSliceOpInterface,
                                                     tensor::ExtractSliceOp> {
   bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
-                              BufferizationState &state) const {
+                              const BufferizationState &state) const {
     return false;
   }
 
   bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     return false;
   }
 
   OpResult getAliasingOpResult(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     return &opOperand == &op->getOpOperand(0) /*source*/
                ? op->getResult(0)
                : OpResult();
   }
 
   BufferRelation bufferRelation(Operation *op, OpResult opResult,
                                 const BufferizationAliasInfo &aliasInfo,
-                                BufferizationState &state) const {
+                                const BufferizationState &state) const {
     return BufferRelation::None;
   }
 
   LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
-                          BufferizationState &state) const {
+                          const BufferizationState &state) const {
     auto extractSliceOp = cast<tensor::ExtractSliceOp>(op);
     Location loc = extractSliceOp.getLoc();
     Value srcMemref = state.lookupBuffer(rewriter, extractSliceOp.source());
     auto srcMemrefType = srcMemref.getType().cast<MemRefType>();
     auto dstTensorType =
         extractSliceOp.result().getType().cast<RankedTensorType>();
 
     // If not inplaceable, alloc.
LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
     return success();
   }
 };
 
 struct ExtractOpInterface
     : public BufferizableOpInterface::ExternalModel<ExtractOpInterface,
                                                     tensor::ExtractOp> {
   bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
-                              BufferizationState &state) const {
+                              const BufferizationState &state) const {
     return true;
   }
 
   bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     return false;
   }
 
   OpResult getAliasingOpResult(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     return OpResult();
   }
 
   LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
-                          BufferizationState &state) const {
+                          const BufferizationState &state) const {
     auto extractOp = cast<tensor::ExtractOp>(op);
     Value srcMemref = state.lookupBuffer(rewriter, extractOp.tensor());
     state.replaceOpWithNewOp<memref::LoadOp>(rewriter, op, srcMemref,
                                              extractOp.indices());
     return success();
   }
 };
 
 struct InsertOpInterface
     : public BufferizableOpInterface::ExternalModel<InsertOpInterface,
                                                     tensor::InsertOp> {
   bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
-                              BufferizationState &state) const {
+                              const BufferizationState &state) const {
     return true;
   }
 
   bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     return true;
   }
 
   OpResult getAliasingOpResult(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     assert(&opOperand == &op->getOpOperand(1) /*dest*/ &&
            "expected dest OpOperand");
     return op->getOpResult(0);
   }
 
   SmallVector<OpOperand *>
   getAliasingOpOperand(Operation *op, OpResult opResult,
-                       BufferizationState &state) const {
+                       const BufferizationState &state) const {
     return {&op->getOpOperand(1) /*dest*/};
   }
 
   LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
-                          BufferizationState &state) const {
+                          const BufferizationState &state) const {
     auto insertOp = cast<tensor::InsertOp>(op);
     Location loc = insertOp.getLoc();
     Value destMemref =
         state.getResultBuffer(rewriter, insertOp->getOpResult(0));
     rewriter.create<memref::StoreOp>(loc, insertOp.scalar(), destMemref,
                                      insertOp.indices());
     state.replaceOp(rewriter, op, destMemref);
     return success();
   }
 
   BufferRelation bufferRelation(Operation *op, OpResult opResult,
                                 const BufferizationAliasInfo &aliasInfo,
-                                BufferizationState &state) const {
+                                const BufferizationState &state) const {
     return BufferRelation::Equivalent;
   }
 };
 
 /// Return true if the (ExtractSliceOp, InsertSliceOp) pair match (i.e.
 /// equivalent operand / result and same offset/sizes/strides specification).
 ///
 /// This is one particular type of relationship between ops on tensors that
areEquivalentExtractSliceOps(const BufferizationAliasInfo &aliasInfo,
   if (!sameOffsetsSizesAndStrides(st, sti, isEqualConstantIntOrValue))
     return false;
   return true;
 }
 
 /// Return true if `value` is originating from an ExtractSliceOp that matches
 /// the given InsertSliceOp.
 static bool hasMatchingExtractSliceOp(const BufferizationAliasInfo &aliasInfo,
-                                      BufferizationState &state, Value value,
-                                      InsertSliceOp insertOp) {
+                                      const BufferizationState &state,
+                                      Value value, InsertSliceOp insertOp) {
   auto condition = [&](Value val) {
     if (auto extractOp = val.getDefiningOp<ExtractSliceOp>())
       if (areEquivalentExtractSliceOps(aliasInfo, extractOp, insertOp))
         return true;
     return false;
   };
 
   return llvm::all_of(state.findValueInReverseUseDefChain(value, condition),
                       condition);
 }
 
 struct InsertSliceOpInterface
     : public BufferizableOpInterface::ExternalModel<InsertSliceOpInterface,
                                                     tensor::InsertSliceOp> {
   bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
-                              BufferizationState &state) const {
+                              const BufferizationState &state) const {
     return true;
   }
 
   bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     return &opOperand == &op->getOpOperand(1) /*dest*/;
   }
 
   OpResult getAliasingOpResult(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     return &opOperand == &op->getOpOperand(1) /*dest*/
                ? op->getResult(0)
                : OpResult();
   }
 
   BufferRelation bufferRelation(Operation *op, OpResult opResult,
                                 const BufferizationAliasInfo &aliasInfo,
-                                BufferizationState &state) const {
+                                const BufferizationState &state) const {
     return BufferRelation::Equivalent;
   }
 
   bool isNotConflicting(Operation *op, OpOperand *uRead,
-                        OpOperand *uConflictingWrite, BufferizationState &state,
+                        OpOperand *uConflictingWrite,
+                        const BufferizationState &state,
                         const BufferizationAliasInfo &aliasInfo) const {
     Operation *readingOp = uRead->getOwner();
     Operation *conflictingWritingOp = uConflictingWrite->getOwner();
 
     // Special rules for matching ExtractSliceOp/InsertSliceOp pairs. If
     // uRead is an InsertSliceOp...
     if (auto insertSliceOp = dyn_cast<InsertSliceOp>(readingOp)) {
       // As an example, consider the following IR.
if (auto insertSliceOp = dyn_cast<InsertSliceOp>(conflictingWritingOp))
           hasMatchingExtractSliceOp(aliasInfo, state, insertSliceOp.source(),
                                     insertSliceOp))
         return true;
 
     return false;
   }
 
   LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
-                          BufferizationState &state) const {
+                          const BufferizationState &state) const {
     // insert_slice ops arise from tiling and bufferizing them out-of-place is
     // generally a deal breaker. When used with loops, this ends up cloning the
     // whole tensor on every single iteration and is a symptom of a
     // catastrophically bad scheduling decision.
     // TODO: be very loud about it or even consider failing the pass.
     auto insertSliceOp = cast<tensor::InsertSliceOp>(op);
     Location loc = insertSliceOp.getLoc();
 

mlir/lib/Dialect/Linalg/ComprehensiveBufferize/VectorInterfaceImpl.cpp

 namespace linalg {
 namespace comprehensive_bufferize {
 namespace vector_ext {
 
 struct TransferReadOpInterface
     : public BufferizableOpInterface::ExternalModel<TransferReadOpInterface,
                                                     vector::TransferReadOp> {
   bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
-                              BufferizationState &state) const {
+                              const BufferizationState &state) const {
     assert(opOperand.get().getType().isa<RankedTensorType>() &&
            "only tensor types expected");
     return true;
   }
 
   bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     assert(opOperand.get().getType().isa<RankedTensorType>() &&
            "only tensor types expected");
     return false;
   }
 
   OpResult getAliasingOpResult(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     return OpResult();
   }
 
   LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
-                          BufferizationState &state) const {
+                          const BufferizationState &state) const {
     auto readOp = cast<vector::TransferReadOp>(op);
     assert(readOp.getShapedType().isa<TensorType>() &&
            "only tensor types expected");
 
     // TransferReadOp always reads from the bufferized op.source().
     Value buffer = state.lookupBuffer(rewriter, readOp.source());
     Value read = rewriter.create<vector::TransferReadOp>(
         readOp.getLoc(), readOp.getVectorType(), buffer, readOp.indices(),
         readOp.permutation_map(), readOp.padding(), readOp.mask(),
         readOp.in_boundsAttr());
     state.replaceOp(rewriter, op, read);
     return success();
   }
 };
 
 struct TransferWriteOpInterface
     : public BufferizableOpInterface::ExternalModel<TransferWriteOpInterface,
                                                     vector::TransferWriteOp> {
   bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
-                              BufferizationState &state) const {
+                              const BufferizationState &state) const {
     assert(opOperand.get().getType().isa<TensorType>() &&
            "only tensor types expected");
     return true;
   }
 
   bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     assert(opOperand.get().getType().isa<TensorType>() &&
            "only tensor types expected");
     return true;
   }
 
   OpResult getAliasingOpResult(Operation *op, OpOperand &opOperand,
-                               BufferizationState &state) const {
+                               const BufferizationState &state) const {
     assert(opOperand.get().getType().isa<TensorType>() &&
            "only tensor types expected");
     return op->getOpResult(0);
   }
 
   BufferRelation bufferRelation(Operation *op, OpResult opResult,
                                 const BufferizationAliasInfo &aliasInfo,
-                                BufferizationState &state) const {
+                                const BufferizationState &state) const {
     return BufferRelation::Equivalent;
   }
 
   LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
-                          BufferizationState &state) const {
+                          const BufferizationState &state) const {
     auto writeOp = cast<vector::TransferWriteOp>(op);
     assert(writeOp.getShapedType().isa<TensorType>() &&
            "only tensor types expected");
 
     // Create a new transfer_write on buffer that doesn't have a return value.
     // Leave the previous transfer_write to dead code as it still has uses at
     // this point.
     Value resultBuffer = state.getResultBuffer(rewriter, op->getResult(0));