[mlir] Add DecomposeCallGraphTypes pass.

[silvas]

Summary

This replaces the old type decomposition logic that was previously mixed
into bufferization, and makes it easily accessible.

This also deletes TestFinalizingBufferize, because after we remove the type
decomposition, it doesn't do anything that is not already provided by
func-bufferize.

[rriddle]

mlir/lib/Transforms/DecomposeCallGraphTypes.cpp

1

(Side nit, but this pass isn't general so I'd prefer to put it in Standard/Transforms instead of here.)

[silvas]

Move to Standard/Transforms

[bondhugula]

This appears nice to me. Some superficial comments to start with.

mlir/include/mlir/Dialect/StandardOps/Transforms/DecomposeCallGraphTypes.h

25–29 ↗

(On Diff #303549)

This description is a bit cryptic - could you please rephrase if possible?

mlir/lib/Dialect/StandardOps/Transforms/DecomposeCallGraphTypes.cpp

96 ↗

(On Diff #303549)

Doc comment here please.

118 ↗

(On Diff #303549)

Likewise.

141 ↗

(On Diff #303549)

Nit: int -> unsigned?

mlir/test/Transforms/decompose-call-graph-types.mlir

18

Do you think it's useful to also test cases where the arguments are a mix of tuples and non-tuple types? (and similarly return values).

mlir/test/lib/Transforms/TestDecomposeCallGraphTypes.cpp

20

Is this a TODO?

[silvas]

Address feedback.

mlir/include/mlir/Dialect/StandardOps/Transforms/DecomposeCallGraphTypes.h

25–29 ↗

(On Diff #303549)

Tried to limit the jargon to a note.

mlir/lib/Dialect/StandardOps/Transforms/DecomposeCallGraphTypes.cpp

141 ↗

(On Diff #303549)

Switched to use unsigned consistently in the file.

mlir/test/Transforms/decompose-call-graph-types.mlir

18

Good catch! Done!

mlir/test/lib/Transforms/TestDecomposeCallGraphTypes.cpp

20

Sorry, remnant of something that I should have done before uploading. (for me, "XXX" = "TODO before uploading")

Fixed.

[bondhugula]

Is "call graph types" the best term here? This doesn't have to do with call graphs but with function signatures.(?) The older naming along the lines of FunctionSignatureConversion or DecomposeFuncSignature or UnpackFuncSignature looks clearer than anything with "CallGraph".

@dfki-mako or other contributors to previous buffer conversion are probably the best to review details here.

[dfki-mako]

Awesome work 🚀 I don't see any problems to land this CL after addressing the nits 🤓

mlir/lib/Dialect/StandardOps/Transforms/DecomposeCallGraphTypes.cpp

108 ↗

(On Diff #303961)

nit: auto -> Value

135 ↗

(On Diff #303961)

nit: auto -> Value

146 ↗

(On Diff #303961)

nit: auto -> OpResult

[silvas]

In D90899#2390356, @bondhugula wrote:

Is "call graph types" the best term here? This doesn't have to do with call graphs but with function signatures.(?) The older naming along the lines of FunctionSignatureConversion or DecomposeFuncSignature or UnpackFuncSignature looks clearer than anything with "CallGraph".

I'm using the term "call graph types" as a stand-in for "function signatures, call signatures, and return signatures". That is, types that flow along call graph edges.

I was also keeping an eye towards eventually generalizing this beyond std.func/call/return. In that more general setting, I think "call graph" is much clearer, because different dialects might have objects in their call graphs that are not called "function". For example, the concept of a first class callable object has many different names: lambdas, closures, delegates. Using the name "function" doesn't sit well with me in that more general setting. In all cases though, the set of patterns we need are the same: one on CallOpInterface, one on CallableOpInterface, and one on something that represents returning control flow from a CallableOpInterface. Once we have extended those interfaces appropriately, the patterns in this pass would be updated to MatchAnyOpTypeTag patterns written over those interfaces and everything would just work.

I guess it depends on how one looks at the pass: is the pass fundamentally updating function signatures / callable signatures (and everything else is just changing to match), or is the pass to be viewed as decomposing types along call graph edges. I was thinking about the pass as the latter. What do you think?

[silvas]

Address comments.

[bondhugula]

LGTM - thanks! Just some minor comments (please see if the "mixed" test case could be improved for coverage).

mlir/lib/Dialect/StandardOps/Transforms/DecomposeCallGraphTypes.cpp

33–34 ↗

(On Diff #304909)

Triple /// comment.

77 ↗

(On Diff #304909)

A comment here perhaps.

mlir/test/Transforms/decompose-call-graph-types.mlir

103

Would it be useful to have a test case where you have a non-trivial tuple (at least one item in the tuple) mixed with a non-tuple?

mlir/test/lib/Transforms/TestDecomposeCallGraphTypes.cpp

32

auto -> ModuleOp

[silvas]

mlir/test/Transforms/decompose-call-graph-types.mlir

103

Good catch! I updated it to exercise the 1:0 1:1 and 1:N cases.

[bondhugula]

In D90899#2391979, @silvas wrote:

In D90899#2390356, @bondhugula wrote:

Is "call graph types" the best term here? This doesn't have to do with call graphs but with function signatures.(?) The older naming along the lines of FunctionSignatureConversion or DecomposeFuncSignature or UnpackFuncSignature looks clearer than anything with "CallGraph".

I'm using the term "call graph types" as a stand-in for "function signatures, call signatures, and return signatures". That is, types that flow along call graph edges.

I was also keeping an eye towards eventually generalizing this beyond std.func/call/return. In that more general setting, I think "call graph" is much clearer, because different dialects might have objects in their call graphs that are not called "function". For example, the concept of a first class callable object has many different names: lambdas, closures, delegates. Using the name "function" doesn't sit well with me in that more general setting. In all cases though, the set of patterns we need are the same: one on CallOpInterface, one on CallableOpInterface, and one on something that represents returning control flow from a CallableOpInterface. Once we have extended those interfaces appropriately, the patterns in this pass would be updated to MatchAnyOpTypeTag patterns written over those interfaces and everything would just work.

I guess it depends on how one looks at the pass: is the pass fundamentally updating function signatures / callable signatures (and everything else is just changing to match), or is the pass to be viewed as decomposing types along call graph edges. I was thinking about the pass as the latter. What do you think?

1. I guess "function signature" is the key thing - callable signatures and return signatures can be viewed as dependent/anciliary.
2. Reg. the naming reflecting a future generalization: this is a frequent slippery slope. I've seen it happen multiple times when the thing is named based on what the plan is - making it harder to find and not reflect the current functionality. The other things you refer to for the future are probably also FunctionLike or at least callable? The 'call graph types' doesn't really bring out "types along call graph edges". DecomposeCallableSignature perhaps?

Anyway, all of this isn't really a major concern.

mlir/include/mlir/Transforms/Bufferize.h

 // memref types.
 //
 // Generally speaking, for each op that operates on tensor types, a conversion
 // pattern needs to be written. The infrastructure in this file assists in
 // defining these conversion patterns in a composable way.
 //
 // Bufferization conversion patterns should generally use the ordinary
 // conversion pattern classes (e.g. OpConversionPattern). A TypeConverter
-// (accessible with getTypeConverter()) available on such patterns is sufficient
-// for most cases (if needed at all).
-//
-// But some patterns require access to the extra functions on
-// BufferizeTypeConverter that don't exist on the base TypeConverter class. For
-// those cases, BufferizeConversionPattern and its related classes should be
-// used, which provide access to a BufferizeTypeConverter directly.
+// (accessible with getTypeConverter()) is available if needed for converting
+// types.
 //
 //===----------------------------------------------------------------------===//
 
 #ifndef MLIR_TRANSFORMS_BUFFERIZE_H
 #define MLIR_TRANSFORMS_BUFFERIZE_H
 
 #include "mlir/Analysis/BufferAliasAnalysis.h"
 #include "mlir/Analysis/Liveness.h"
 #include "mlir/Dialect/StandardOps/IR/Ops.h"
 #include "mlir/IR/Builders.h"
 #include "mlir/IR/Dominance.h"
 #include "mlir/IR/Function.h"
 #include "mlir/IR/Operation.h"
 #include "mlir/Transforms/DialectConversion.h"
 
 namespace mlir {
 
-/// A helper type converter class for using inside Buffer Assignment operation
-/// conversion patterns. The default constructor keeps all the types intact
-/// except for the ranked-tensor types which is converted to memref types.
+/// A helper type converter class that automatically populates the relevant
+/// materializations and type conversions for bufferization.
 class BufferizeTypeConverter : public TypeConverter {
 public:
   BufferizeTypeConverter();
-
-  /// This method tries to decompose a value of a certain type using provided
-  /// decompose callback functions. If it is unable to do so, the original value
-  /// is returned.
-  void tryDecomposeValue(OpBuilder &, Location, Type, Value,
-                         SmallVectorImpl<Value> &);
-
-  /// This method tries to decompose a type using provided decompose callback
-  /// functions. If it is unable to do so, the original type is returned.
-  void tryDecomposeType(Type, SmallVectorImpl<Type> &);
-
-  /// This method registers a callback function that will be called to decompose
-  /// a value of a certain type into several values.
-  template <typename FnT,
-            typename T = typename llvm::function_traits<FnT>::template arg_t<2>>
-  void addDecomposeValueConversion(FnT &&callback) {
-    decomposeValueConversions.emplace_back(
-        wrapDecomposeValueConversionCallback<T>(std::forward<FnT>(callback)));
-  }
-
-  /// This method registers a callback function that will be called to decompose
-  /// a type into several types.
-  template <typename FnT,
-            typename T = typename llvm::function_traits<FnT>::template arg_t<0>>
-  void addDecomposeTypeConversion(FnT &&callback) {
-    auto wrapper =
-        wrapDecomposeTypeConversionCallback<T>(std::forward<FnT>(callback));
-    decomposeTypeConversions.emplace_back(wrapper);
-    addConversion(std::forward<FnT>(callback));
-  }
-
-private:
-  using DecomposeValueConversionCallFn = std::function<Optional<LogicalResult>(
-      OpBuilder &, Location, Type, Value, SmallVectorImpl<Value> &)>;
-
-  using DecomposeTypeConversionCallFn =
-      std::function<Optional<LogicalResult>(Type, SmallVectorImpl<Type> &)>;
-
-  /// Generate a wrapper for the given decompose value conversion callback.
-  template <typename T, typename FnT>
-  DecomposeValueConversionCallFn
-  wrapDecomposeValueConversionCallback(FnT &&callback) {
-    return [callback = std::forward<FnT>(callback)](
-               OpBuilder &builder, Location loc, Type type, Value value,
-               SmallVectorImpl<Value> &newValues) -> Optional<LogicalResult> {
-      if (T derivedType = type.dyn_cast<T>())
-        return callback(builder, loc, derivedType, value, newValues);
-      return llvm::None;
-    };
-  }
-
-  /// Generate a wrapper for the given decompose type conversion callback.
-  template <typename T, typename FnT>
-  DecomposeTypeConversionCallFn
-  wrapDecomposeTypeConversionCallback(FnT &&callback) {
-    return [callback = std::forward<FnT>(callback)](
-               Type type,
-               SmallVectorImpl<Type> &results) -> Optional<LogicalResult> {
-      T derivedType = type.dyn_cast<T>();
-      if (!derivedType)
-        return llvm::None;
-      return callback(derivedType, results);
-    };
-  }
-
-  SmallVector<DecomposeValueConversionCallFn, 2> decomposeValueConversions;
-  SmallVector<DecomposeTypeConversionCallFn, 2> decomposeTypeConversions;
 };
 
 /// Marks ops used by bufferization for type conversion materializations as
 /// "legal" in the given ConversionTarget.
 ///
 /// This function should be called by all bufferization passes using
 /// BufferizeTypeConverter so that materializations work proprely. One exception
 /// is bufferization passes doing "full" conversions, where it can be desirable
 /// for even the materializations to remain illegal so that they are eliminated,
 /// such as via the patterns in
 /// populateEliminateBufferizeMaterializationsPatterns.
 void populateBufferizeMaterializationLegality(ConversionTarget &target);
 
 /// Populate patterns to eliminate bufferize materializations.
 ///
 /// In particular, these are the tensor_load/tensor_to_memref ops.
 void populateEliminateBufferizeMaterializationsPatterns(
     MLIRContext *context, BufferizeTypeConverter &typeConverter,
     OwningRewritePatternList &patterns);
 
-/// Helper conversion pattern that encapsulates a BufferizeTypeConverter
-/// instance.
-template <typename SourceOp>
-class BufferizeOpConversionPattern : public OpConversionPattern<SourceOp> {
-public:
-  explicit BufferizeOpConversionPattern(MLIRContext *context,
-                                        BufferizeTypeConverter &converter,
-                                        PatternBenefit benefit = 1)
-      : OpConversionPattern<SourceOp>(context, benefit), converter(converter) {}
-
-protected:
-  BufferizeTypeConverter &converter;
-};
-
-/// Helper conversion pattern that encapsulates a BufferizeTypeConverter
-/// instance and that operates on Operation* to be compatible with OpInterfaces.
-/// This allows avoiding to instantiate N patterns for ops that can be subsumed
-/// by a single op interface (e.g. Linalg named ops).
-class BufferizeConversionPattern : public ConversionPattern {
-public:
-  explicit BufferizeConversionPattern(MLIRContext *context,
-                                      BufferizeTypeConverter &converter,
-                                      PatternBenefit benefit = 1)
-      : ConversionPattern(benefit, converter, MatchAnyOpTypeTag()),
-        converter(converter) {}
-
-protected:
-  BufferizeTypeConverter &converter;
-};
-
-/// Converts the signature of the function using BufferizeTypeConverter.
-/// Each result type of the function is kept as a function result or appended to
-/// the function arguments list based on ResultConversionKind for the converted
-/// result type.
-class BufferizeFuncOpConverter : public BufferizeOpConversionPattern<FuncOp> {
-public:
-  using BufferizeOpConversionPattern<FuncOp>::BufferizeOpConversionPattern;
-
-  /// Performs the actual signature rewriting step.
-  LogicalResult matchAndRewrite(mlir::FuncOp, ArrayRef<Value>,
-                                ConversionPatternRewriter &) const override;
-};
-
-/// Rewrites the `ReturnOp` to conform with the changed function signature.
-/// Operands that correspond to return values and their types have been set to
-/// AppendToArgumentsList are dropped. In their place, a corresponding copy
-/// operation from the operand to the target function argument is inserted.
-template <typename ReturnOpSourceTy, typename ReturnOpTargetTy,
-          typename CopyOpTy>
-class BufferizeReturnOpConverter
-    : public BufferizeOpConversionPattern<ReturnOpSourceTy> {
-public:
-  using BufferizeOpConversionPattern<
-      ReturnOpSourceTy>::BufferizeOpConversionPattern;
-
-  /// Performs the actual return-op conversion step.
-  LogicalResult
-  matchAndRewrite(ReturnOpSourceTy returnOp, ArrayRef<Value> operands,
-                  ConversionPatternRewriter &rewriter) const final {
-    SmallVector<Value, 2> newOperands;
-    for (auto operand : operands)
-      this->converter.tryDecomposeValue(
-          rewriter, returnOp.getLoc(), operand.getType(), operand, newOperands);
-    rewriter.replaceOpWithNewOp<ReturnOpTargetTy>(returnOp, newOperands);
-    return success();
-  }
-};
-
-/// Rewrites the `CallOp` to match its operands and results with the signature
-/// of the callee after rewriting the callee with
-/// BufferizeFuncOpConverter.
-class BufferizeCallOpConverter : public BufferizeOpConversionPattern<CallOp> {
-public:
-  using BufferizeOpConversionPattern<CallOp>::BufferizeOpConversionPattern;
-
-  /// Performs the actual rewriting step.
-  LogicalResult matchAndRewrite(CallOp, ArrayRef<Value>,
-                                ConversionPatternRewriter &) const override;
-};
-
-/// Populates `patterns` with the conversion patterns of buffer
-/// assignment.
-template <typename ReturnOpSourceTy, typename ReturnOpTargetTy,
-          typename CopyOpTy>
-static void
-populateWithBufferizeOpConversionPatterns(MLIRContext *context,
-                                          BufferizeTypeConverter &converter,
-                                          OwningRewritePatternList &patterns) {
-  // clang-format off
-  patterns.insert<
-    BufferizeCallOpConverter,
-    BufferizeFuncOpConverter,
-    BufferizeReturnOpConverter
-      <ReturnOpSourceTy, ReturnOpTargetTy, CopyOpTy>
-  >(context, converter);
-  // clang-format on
-}
-
 /// A simple analysis that detects allocation operations.
 class BufferPlacementAllocs {
 public:
   /// Represents a tuple of allocValue and deallocOperation.
   using AllocEntry = std::tuple<Value, Operation *>;
 
   /// Represents a list containing all alloc entries.
   using AllocEntryList = SmallVector<AllocEntry, 8>;

mlir/include/mlir/Transforms/DecomposeCallGraphTypes.h

+//===- DecomposeCallGraphTypes.h - CG type decompositions -------*- C++ -*-===//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+//
+// Conversion patterns for decomposing types along call graph edges. That is,
+// decomposing types for calls, returns, and function args.
+//
+// TODO: Make this handle dialect-defined functions, calls, and returns.
+// Currently, the generic interfaces aren't sophisticated enough for the
+// types of mutations that we are doing here.
+//
+//===----------------------------------------------------------------------===//
+
+#ifndef MLIR_TRANSFORMS_DECOMPOSECALLGRAPHTYPES_H
+#define MLIR_TRANSFORMS_DECOMPOSECALLGRAPHTYPES_H
+
+#include "mlir/Transforms/DialectConversion.h"
+
+namespace mlir {
+
+/// This class provides a hook, registratable similar to TypeConverter
+/// materialization hooks, that effectively perfoms the inverse of a
+/// source/argument materialization.
+///
+/// The reason we need the inverse is easily understood by looking at what we
+/// need to do for decomposing types for a return op. When converting a return
+/// op, the dialect conversion framework will give the list of converted
+/// operands, and will ensure that each converted operand, even if it expanded
+/// into multiple types, is materialized as a single result. We then need to
+/// undo that materialization to a single result, which we do with the
+/// decomposeValue hooks registered on this object.
+///
+/// TODO: Eventually, the type conversion infra should have this hook built-in.
+/// See
+/// https://llvm.discourse.group/t/extending-type-conversion-infrastructure/779/2
+class ValueDecomposer {
+public:
+  /// This method tries to decompose a value of a certain type using provided
+  /// decompose callback functions. If it is unable to do so, the original value
+  /// is returned.
+  void decomposeValue(OpBuilder &, Location, Type, Value,
+                      SmallVectorImpl<Value> &);
+
+  /// This method registers a callback function that will be called to decompose
+  /// a value of a certain type into 0, 1, or multiple values.
+  template <typename FnT,
+            typename T = typename llvm::function_traits<FnT>::template arg_t<2>>
+  void addDecomposeValueConversion(FnT &&callback) {
+    decomposeValueConversions.emplace_back(
+        wrapDecomposeValueConversionCallback<T>(std::forward<FnT>(callback)));
+  }
+
+private:
+  using DecomposeValueConversionCallFn = std::function<Optional<LogicalResult>(
+      OpBuilder &, Location, Type, Value, SmallVectorImpl<Value> &)>;
+
+  /// Generate a wrapper for the given decompose value conversion callback.
+  template <typename T, typename FnT>
+  DecomposeValueConversionCallFn
+  wrapDecomposeValueConversionCallback(FnT &&callback) {
+    return [callback = std::forward<FnT>(callback)](
+               OpBuilder &builder, Location loc, Type type, Value value,
+               SmallVectorImpl<Value> &newValues) -> Optional<LogicalResult> {
+      if (T derivedType = type.dyn_cast<T>())
+        return callback(builder, loc, derivedType, value, newValues);
+      return llvm::None;
+    };
+  }
+
+  SmallVector<DecomposeValueConversionCallFn, 2> decomposeValueConversions;
+};
+
+/// Populates the patterns needed to drive the conversion process for
+/// decomposing call graph types with the given `ValueDecomposer`.
+void populateDecomposeCallGraphTypesPatterns(
+    MLIRContext *context, TypeConverter &typeConverter,
+    ValueDecomposer &decomposer, OwningRewritePatternList &patterns);
+
+} // end namespace mlir
+
+#endif // MLIR_TRANSFORMS_DECOMPOSECALLGRAPHTYPES_H

mlir/lib/Transforms/Bufferize.cpp

BufferizeTypeConverter::BufferizeTypeConverter() {
   addTargetMaterialization([](OpBuilder &builder, BaseMemRefType type,
                               ValueRange inputs, Location loc) -> Value {
     assert(inputs.size() == 1);
     assert(inputs[0].getType().isa<TensorType>());
     return builder.create<TensorToMemrefOp>(loc, type, inputs[0]);
   });
 }
 
-/// This method tries to decompose a value of a certain type using provided
-/// decompose callback functions. If it is unable to do so, the original value
-/// is returned.
-void BufferizeTypeConverter::tryDecomposeValue(
-    OpBuilder &builder, Location loc, Type type, Value value,
-    SmallVectorImpl<Value> &results) {
-  for (auto &conversion : decomposeValueConversions)
-    if (conversion(builder, loc, type, value, results))
-      return;
-  results.push_back(value);
-}
-
-/// This method tries to decompose a type using provided decompose callback
-/// functions. If it is unable to do so, the original type is returned.
-void BufferizeTypeConverter::tryDecomposeType(Type type,
-                                              SmallVectorImpl<Type> &types) {
-  for (auto &conversion : decomposeTypeConversions)
-    if (conversion(type, types))
-      return;
-  types.push_back(type);
-}
-
 void mlir::populateBufferizeMaterializationLegality(ConversionTarget &target) {
   target.addLegalOp<TensorLoadOp, TensorToMemrefOp>();
 }
 
 namespace {
 // In a finalizing bufferize conversion, we know that all tensors have been
 // converted to memrefs, thus, this op becomes an identity.
 class BufferizeTensorLoadOp : public OpConversionPattern<TensorLoadOp> {

 } // namespace
 
 void mlir::populateEliminateBufferizeMaterializationsPatterns(
     MLIRContext *context, BufferizeTypeConverter &typeConverter,
     OwningRewritePatternList &patterns) {
   patterns.insert<BufferizeTensorLoadOp, BufferizeTensorToMemrefOp>(
       typeConverter, context);
 }
-
-//===----------------------------------------------------------------------===//
-// BufferizeFuncOpConverter
-//===----------------------------------------------------------------------===//
-
-/// Performs the actual function signature rewriting step.
-LogicalResult BufferizeFuncOpConverter::matchAndRewrite(
-    mlir::FuncOp funcOp, ArrayRef<Value> operands,
-    ConversionPatternRewriter &rewriter) const {
-  auto funcType = funcOp.getType();
-
-  // Convert function arguments using the provided TypeConverter.
-  TypeConverter::SignatureConversion conversion(funcType.getNumInputs());
-  for (auto argType : llvm::enumerate(funcType.getInputs())) {
-    SmallVector<Type, 2> decomposedTypes, convertedTypes;
-    converter.tryDecomposeType(argType.value(), decomposedTypes);
-    converter.convertTypes(decomposedTypes, convertedTypes);
-    conversion.addInputs(argType.index(), convertedTypes);
-  }
-
-  // Convert the result types of the function.
-  SmallVector<Type, 2> newResultTypes;
-  newResultTypes.reserve(funcOp.getNumResults());
-  for (Type resultType : funcType.getResults()) {
-    SmallVector<Type, 2> originTypes;
-    converter.tryDecomposeType(resultType, originTypes);
-    for (auto origin : originTypes)
-      newResultTypes.push_back(converter.convertType(origin));
-  }
-
-  if (failed(rewriter.convertRegionTypes(&funcOp.getBody(), converter,
-                                         &conversion)))
-    return failure();
-
-  // Update the signature of the function.
-  rewriter.updateRootInPlace(funcOp, [&] {
-    funcOp.setType(rewriter.getFunctionType(conversion.getConvertedTypes(),
-                                            newResultTypes));
-  });
-  return success();
-}
-
-//===----------------------------------------------------------------------===//
-// BufferizeCallOpConverter
-//===----------------------------------------------------------------------===//
-
-/// Performs the actual rewriting step.
-LogicalResult BufferizeCallOpConverter::matchAndRewrite(
-    CallOp callOp, ArrayRef<Value> operands,
-    ConversionPatternRewriter &rewriter) const {
-
-  Location loc = callOp.getLoc();
-  SmallVector<Value, 2> newOperands;
-
-  // TODO: if the CallOp references a FuncOp that only has a declaration (e.g.
-  // to an externally defined symbol like an external library calls), only
-  // convert if some special attribute is set.
-  // This will allow more control of interop across ABI boundaries.
-
-  // Create the operands list of the new `CallOp`. It unpacks the decomposable
-  // values if a decompose callback function has been provided by the user.
-  for (auto operand : operands)
-    converter.tryDecomposeValue(rewriter, loc, operand.getType(), operand,
-                                newOperands);
-
-  // Create the new result types for the new `CallOp` and track the indices in
-  // the new call op's results that correspond to the old call op's results.
-  SmallVector<Type, 2> newResultTypes;
-  SmallVector<SmallVector<int, 2>, 4> expandedResultIndices;
-  expandedResultIndices.resize(callOp.getNumResults());
-  for (auto result : llvm::enumerate(callOp.getResults())) {
-    SmallVector<Type, 2> originTypes;
-    converter.tryDecomposeType(result.value().getType(), originTypes);
-    auto &resultMapping = expandedResultIndices[result.index()];
-    for (Type origin : originTypes) {
-      Type converted = converter.convertType(origin);
-      newResultTypes.push_back(converted);
-      // The result value is not yet available. Its index is kept and it is
-      // replaced with the actual value of the new `CallOp` later.
-      resultMapping.push_back(newResultTypes.size() - 1);
-    }
-  }
-
-  CallOp newCallOp = rewriter.create<CallOp>(loc, callOp.getCallee(),
-                                             newResultTypes, newOperands);
-
-  // Build a replacing value for each result to replace its uses. If a result
-  // has multiple mapping values, it needs to be packed to a single value.
-  SmallVector<Value, 2> replacedValues;
-  replacedValues.reserve(callOp.getNumResults());
-  for (unsigned i = 0, e = callOp.getNumResults(); i < e; ++i) {
-    auto valuesToPack = llvm::to_vector<6>(
-        llvm::map_range(expandedResultIndices[i],
-                        [&](int i) { return newCallOp.getResult(i); }));
-    if (valuesToPack.empty()) {
-      // No replacement is required.
-      replacedValues.push_back(nullptr);
-    } else if (valuesToPack.size() == 1) {
-      replacedValues.push_back(valuesToPack.front());
-    } else {
-      // Values need to be packed using callback function. The same callback
-      // that is used for materializeArgumentConversion is used for packing.
-      Value packed = converter.materializeArgumentConversion(
-          rewriter, loc, callOp.getType(i), valuesToPack);
-      replacedValues.push_back(packed);
-    }
-  }
-  rewriter.replaceOp(callOp, replacedValues);
-  return success();
-}

mlir/lib/Transforms/CMakeLists.txt

 add_subdirectory(Utils)
 
 add_mlir_library(MLIRTransforms
   BufferDeallocation.cpp
   BufferOptimizations.cpp
   BufferResultsToOutParams.cpp
   Bufferize.cpp
   Canonicalizer.cpp
   CopyRemoval.cpp
   CSE.cpp
+  DecomposeCallGraphTypes.cpp
   Inliner.cpp
   LocationSnapshot.cpp
   LoopCoalescing.cpp
   LoopFusion.cpp
   LoopInvariantCodeMotion.cpp
   MemRefDataFlowOpt.cpp
   NormalizeMemRefs.cpp
   OpStats.cpp

mlir/lib/Transforms/DecomposeCallGraphTypes.cpp

+//===- DecomposeCallGraphTypes.cpp - CG type decomposition ----------------===//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+
+#include "mlir/Transforms/DecomposeCallGraphTypes.h"
+#include "mlir/Dialect/StandardOps/IR/Ops.h"
+#include "mlir/IR/Function.h"
+
+using namespace mlir;
+
+//===----------------------------------------------------------------------===//
+// ValueDecomposer
+//===----------------------------------------------------------------------===//
+
+void ValueDecomposer::decomposeValue(OpBuilder &builder, Location loc,
+                                     Type type, Value value,
+                                     SmallVectorImpl<Value> &results) {
+  for (auto &conversion : decomposeValueConversions)
+    if (conversion(builder, loc, type, value, results))
+      return;
+  results.push_back(value);
+}
+
+//===----------------------------------------------------------------------===//
+// DecomposeCallGraphTypesOpConversionPattern
+//===----------------------------------------------------------------------===//
+
+namespace {
+// Base OpConversionPattern class to make a ValueDecomposer available to
+// inherited patterns.
+template <typename SourceOp>
+class DecomposeCallGraphTypesOpConversionPattern
+    : public OpConversionPattern<SourceOp> {
+public:
+  DecomposeCallGraphTypesOpConversionPattern(TypeConverter &typeConverter,
+                                             MLIRContext *context,
+                                             ValueDecomposer &decomposer,
+                                             PatternBenefit benefit = 1)
+      : OpConversionPattern<SourceOp>(typeConverter, context, benefit),
+        decomposer(decomposer) {}
+
+protected:
+  ValueDecomposer &decomposer;
+};
+} // namespace
+
+//===----------------------------------------------------------------------===//
+// DecomposeCallGraphTypesForFuncArgs
+//===----------------------------------------------------------------------===//
+
+namespace {
+struct DecomposeCallGraphTypesForFuncArgs
+    : public DecomposeCallGraphTypesOpConversionPattern<FuncOp> {
+  using DecomposeCallGraphTypesOpConversionPattern::
+      DecomposeCallGraphTypesOpConversionPattern;
+
+  LogicalResult
+  matchAndRewrite(FuncOp op, ArrayRef<Value> operands,
+                  ConversionPatternRewriter &rewriter) const final {
+    auto functionType = op.getType();
+
+    // Convert function arguments using the provided TypeConverter.
+    TypeConverter::SignatureConversion conversion(functionType.getNumInputs());
+    for (auto argType : llvm::enumerate(functionType.getInputs())) {
+      SmallVector<Type, 2> decomposedTypes;
+      getTypeConverter()->convertType(argType.value(), decomposedTypes);
+      if (!decomposedTypes.empty())
+        conversion.addInputs(argType.index(), decomposedTypes);
+    }
+
+    if (failed(rewriter.convertRegionTypes(&op.getBody(), *getTypeConverter(),
+                                           &conversion)))
+      return failure();
+
+    // Update the signature of the function.
+    SmallVector<Type, 2> newResultTypes;
+    getTypeConverter()->convertTypes(functionType.getResults(), newResultTypes);
+    rewriter.updateRootInPlace(op, [&] {
+      op.setType(rewriter.getFunctionType(conversion.getConvertedTypes(),
+                                          newResultTypes));
+    });
+    return success();
+  }
+};
+} // namespace
+
+//===----------------------------------------------------------------------===//
+// DecomposeCallGraphTypesForReturnOp
+//===----------------------------------------------------------------------===//
+
+namespace {
+struct DecomposeCallGraphTypesForReturnOp
+    : public DecomposeCallGraphTypesOpConversionPattern<ReturnOp> {
+  using DecomposeCallGraphTypesOpConversionPattern::
+      DecomposeCallGraphTypesOpConversionPattern;
+  LogicalResult
+  matchAndRewrite(ReturnOp op, ArrayRef<Value> operands,
+                  ConversionPatternRewriter &rewriter) const final {
+    SmallVector<Value, 2> newOperands;
+    for (auto operand : operands)
+      decomposer.decomposeValue(rewriter, op.getLoc(), operand.getType(),
+                                operand, newOperands);
+    rewriter.replaceOpWithNewOp<ReturnOp>(op, newOperands);
+    return success();
+  }
+};
+} // namespace
+
+//===----------------------------------------------------------------------===//
+// DecomposeCallGraphTypesForCallOp
+//===----------------------------------------------------------------------===//
+
+namespace {
+struct DecomposeCallGraphTypesForCallOp
+    : public DecomposeCallGraphTypesOpConversionPattern<CallOp> {
+  using DecomposeCallGraphTypesOpConversionPattern::
+      DecomposeCallGraphTypesOpConversionPattern;
+
+  LogicalResult
+  matchAndRewrite(CallOp op, ArrayRef<Value> operands,
+                  ConversionPatternRewriter &rewriter) const final {
+
+    // Create the operands list of the new `CallOp`.
+    SmallVector<Value, 2> newOperands;
+    for (auto operand : operands)
+      decomposer.decomposeValue(rewriter, op.getLoc(), operand.getType(),
+                                operand, newOperands);
+
+    // Create the new result types for the new `CallOp` and track the indices in
+    // the new call op's results that correspond to the old call op's results.
+    //
+    // expandedResultIndices[i] = "list of new result indices that old result i
+    // expanded to"
+    SmallVector<Type, 2> newResultTypes;
+    SmallVector<SmallVector<int, 2>, 4> expandedResultIndices;
+    for (auto result : op.getResults()) {
+      int oldSize = newResultTypes.size();
+      getTypeConverter()->convertType(result.getType(), newResultTypes);
+      auto &resultMapping = expandedResultIndices.emplace_back();
+      for (int i = oldSize, e = newResultTypes.size(); i < e; i++)
+        resultMapping.push_back(i);
+    }
+
+    CallOp newCallOp = rewriter.create<CallOp>(op.getLoc(), op.getCallee(),
+                                               newResultTypes, newOperands);
+
+    // Build a replacement value for each result to replace its uses. If a
+    // result has multiple mapping values, it needs to be materialized as a
+    // single value.
+    SmallVector<Value, 2> replacedValues;
+    replacedValues.reserve(op.getNumResults());
+    for (unsigned i = 0, e = op.getNumResults(); i < e; ++i) {
+      auto decomposedValues = llvm::to_vector<6>(
+          llvm::map_range(expandedResultIndices[i],
+                          [&](int i) { return newCallOp.getResult(i); }));
+      if (decomposedValues.empty()) {
+        // No replacement is required.
+        replacedValues.push_back(nullptr);
+      } else if (decomposedValues.size() == 1) {
+        replacedValues.push_back(decomposedValues.front());
+      } else {
+        // Materialize a single Value to replace the original Value.
+        Value materialized = getTypeConverter()->materializeArgumentConversion(
+            rewriter, op.getLoc(), op.getType(i), decomposedValues);
+        replacedValues.push_back(materialized);
+      }
+    }
+    rewriter.replaceOp(op, replacedValues);
+    return success();
+  }
+};
+} // namespace
+
+void mlir::populateDecomposeCallGraphTypesPatterns(
+    MLIRContext *context, TypeConverter &typeConverter,
+    ValueDecomposer &decomposer, OwningRewritePatternList &patterns) {
+  patterns.insert<DecomposeCallGraphTypesForCallOp,
+                  DecomposeCallGraphTypesForFuncArgs,
+                  DecomposeCallGraphTypesForReturnOp>(typeConverter, context,
+                                                      decomposer);
+}

mlir/test/Transforms/decompose-call-graph-types.mlir

+// RUN: mlir-opt %s -split-input-file -test-decompose-call-graph-types | FileCheck %s
+
+// Test case: Most basic case of a 1:N decomposition, an identity function.
+
+// CHECK-LABEL:   func @identity(
+// CHECK-SAME:                   %[[ARG0:.*]]: i1,
+// CHECK-SAME:                   %[[ARG1:.*]]: i32) -> (i1, i32) {
+// CHECK:           %[[ARG_MATERIALIZED:.*]] = "test.make_tuple"(%[[ARG0]], %[[ARG1]]) : (i1, i32) -> tuple<i1, i32>
+// CHECK:           %[[RET0:.*]] = "test.get_tuple_element"(%[[ARG_MATERIALIZED]]) {index = 0 : i32} : (tuple<i1, i32>) -> i1
+// CHECK:           %[[RET1:.*]] = "test.get_tuple_element"(%[[ARG_MATERIALIZED]]) {index = 1 : i32} : (tuple<i1, i32>) -> i32
+// CHECK:           return %[[RET0]], %[[RET1]] : i1, i32
+func @identity(%arg0: tuple<i1, i32>) -> tuple<i1, i32> {
+  return %arg0 : tuple<i1, i32>
+}
+
+// -----
+
+// Test case: Ensure no materializations in the case of 1:1 decomposition.
+
+// CHECK-LABEL:   func @identity_1_to_1_no_materializations(
+// CHECK-SAME:                                              %[[ARG0:.*]]: i1) -> i1 {
+// CHECK:           return %[[ARG0]] : i1
+func @identity_1_to_1_no_materializations(%arg0: tuple<i1>) -> tuple<i1> {
+  return %arg0 : tuple<i1>
+}
+
+// -----
+
+// Test case: Type that needs to be recursively decomposed.
+
+// CHECK-LABEL:   func @recursive_decomposition(
+// CHECK-SAME:                                   %[[ARG0:.*]]: i1) -> i1 {
+// CHECK:           return %[[ARG0]] : i1
+func @recursive_decomposition(%arg0: tuple<tuple<tuple<i1>>>) -> tuple<tuple<tuple<i1>>> {
+  return %arg0 : tuple<tuple<tuple<i1>>>
+}
+
+// -----
+
+// Test case: Check decomposition of calls.
+
+// CHECK-LABEL:   func @callee(i1, i32) -> (i1, i32)
+func @callee(tuple<i1, i32>) -> tuple<i1, i32>
+
+// CHECK-LABEL:   func @caller(
+// CHECK-SAME:                 %[[ARG0:.*]]: i1,
+// CHECK-SAME:                 %[[ARG1:.*]]: i32) -> (i1, i32) {
+// CHECK:           %[[ARG_MATERIALIZED:.*]] = "test.make_tuple"(%[[ARG0]], %[[ARG1]]) : (i1, i32) -> tuple<i1, i32>
+// CHECK:           %[[CALL_ARG0:.*]] = "test.get_tuple_element"(%[[ARG_MATERIALIZED]]) {index = 0 : i32} : (tuple<i1, i32>) -> i1
+// CHECK:           %[[CALL_ARG1:.*]] = "test.get_tuple_element"(%[[ARG_MATERIALIZED]]) {index = 1 : i32} : (tuple<i1, i32>) -> i32
+// CHECK:           %[[DECOMPOSED:.*]]:2 = call @callee(%[[CALL_ARG0]], %[[CALL_ARG1]]) : (i1, i32) -> (i1, i32)
+// CHECK:           %[[CALL_RESULT_RECOMPOSED:.*]] = "test.make_tuple"(%[[DECOMPOSED]]#0, %[[DECOMPOSED]]#1) : (i1, i32) -> tuple<i1, i32>
+// CHECK:           %[[RET0:.*]] = "test.get_tuple_element"(%[[CALL_RESULT_RECOMPOSED]]) {index = 0 : i32} : (tuple<i1, i32>) -> i1
+// CHECK:           %[[RET1:.*]] = "test.get_tuple_element"(%[[CALL_RESULT_RECOMPOSED]]) {index = 1 : i32} : (tuple<i1, i32>) -> i32
+// CHECK:           return %[[RET0]], %[[RET1]] : i1, i32
+func @caller(%arg0: tuple<i1, i32>) -> tuple<i1, i32> {
+  %0 = call @callee(%arg0) : (tuple<i1, i32>) -> tuple<i1, i32>
+  return %0 : tuple<i1, i32>
+}
+
+// -----
+
+// Test case: Type that decomposes to nothing (that is, a 1:0 decomposition).
+
+// CHECK-LABEL:   func @callee()
+func @callee(tuple<>) -> tuple<>
+// CHECK-LABEL:   func @caller() {
+// CHECK:           call @callee() : () -> ()
+// CHECK:           return
+func @caller(%arg0: tuple<>) -> tuple<> {
+  %0 = call @callee(%arg0) : (tuple<>) -> (tuple<>)
+  return %0 : tuple<>
+}
+
+// -----
+
+// Test case: Ensure decompositions are inserted properly around results of
+// unconverted ops.
+
+// CHECK-LABEL:   func @unconverted_op_result() -> (i1, i32) {
+// CHECK:           %[[UNCONVERTED_VALUE:.*]] = "test.source"() : () -> tuple<i1, i32>
+// CHECK:           %[[RET0:.*]] = "test.get_tuple_element"(%[[UNCONVERTED_VALUE]]) {index = 0 : i32} : (tuple<i1, i32>) -> i1
+// CHECK:           %[[RET1:.*]] = "test.get_tuple_element"(%[[UNCONVERTED_VALUE]]) {index = 1 : i32} : (tuple<i1, i32>) -> i32
+// CHECK:           return %[[RET0]], %[[RET1]] : i1, i32
+func @unconverted_op_result() -> tuple<i1, i32> {
+  %0 = "test.source"() : () -> (tuple<i1, i32>)
+  return %0 : tuple<i1, i32>
+}

mlir/test/Transforms/finalizing-bufferize.mlir

-// RUN: mlir-opt -test-finalizing-bufferize -split-input-file %s | FileCheck %s
-
-// CHECK-LABEL: func @void_function_signature_conversion
-func @void_function_signature_conversion(%arg0: tensor<4x8xf32>) {
-    return
-}
-// CHECK: ({{.*}}: memref<4x8xf32>)
-
-// -----
-
-// CHECK-LABEL: func @complex_signature_conversion
-func @complex_signature_conversion(
-  %arg0: tensor<5xf32>,
-  %arg1: memref<10xf32>,
-  %arg2: i1,
-  %arg3: f16) -> (
-    i1,
-    tensor<5xf32>,
-    memref<10xf32>,
-    memref<15xf32>,
-    f16) {
-  %0 = alloc() : memref<15xf32>
-  %1 = test.tensor_based in(%arg0 : tensor<5xf32>) -> tensor<5xf32>
-  return %arg2, %1, %arg1, %0, %arg3 :
-   i1, tensor<5xf32>, memref<10xf32>, memref<15xf32>, f16
-}
-//      CHECK: (%[[ARG0:.*]]: memref<5xf32>, %[[ARG1:.*]]: memref<10xf32>,
-// CHECK-SAME: %[[ARG2:.*]]: i1, %[[ARG3:.*]]: f16)
-// CHECK-SAME: (i1, memref<5xf32>, memref<10xf32>, memref<15xf32>, f16)
-//      CHECK: %[[FIRST_ALLOC:.*]] = alloc()
-//      CHECK: %[[TENSOR_ALLOC:.*]] = alloc()
-//      CHECK: return %[[ARG2]], %[[TENSOR_ALLOC]], %[[ARG1]], %[[FIRST_ALLOC]],
-// CHECK-SAME: %[[ARG3]]
-
-// -----
-
-// CHECK-LABEL: func @no_signature_conversion_is_needed
-func @no_signature_conversion_is_needed(%arg0: memref<4x8xf32>) {
-  return
-}
-// CHECK: ({{.*}}: memref<4x8xf32>)
-
-// -----
-
-// CHECK-LABEL: func @no_signature_conversion_is_needed
-func @no_signature_conversion_is_needed(%arg0: i1, %arg1: f16) -> (i1, f16){
-  return %arg0, %arg1 : i1, f16
-}
-// CHECK: (%[[ARG0:.*]]: i1, %[[ARG1:.*]]: f16) -> (i1, f16)
-// CHECK: return %[[ARG0]], %[[ARG1]]
-
-// -----
-
-// CHECK-LABEL: func @simple_signature_conversion
-func @simple_signature_conversion(%arg0: tensor<4x8xf32>) -> tensor<4x8xf32> {
-  return %arg0 : tensor<4x8xf32>
-}
-//      CHECK: (%[[ARG0:.*]]: [[TYPE:.*]]<[[RANK:.*]]>) -> [[TYPE]]<[[RANK]]>
-// CHECK-NEXT: return %[[ARG0]]
-
-// -----
-
-// CHECK-LABEL: func @func_with_unranked_arg_and_result
-func @func_with_unranked_arg_and_result(%arg0: tensor<*xf32>) -> tensor<*xf32> {
-  return %arg0 : tensor<*xf32>
-}
-// CHECK-SAME: ([[ARG:%.*]]: memref<*xf32>) -> memref<*xf32>
-// CHECK-NEXT: return [[ARG]] : memref<*xf32>
-
-// -----
-
-// CHECK-LABEL: func @func_and_block_signature_conversion
-func @func_and_block_signature_conversion(%arg0 : tensor<2xf32>, %cond : i1, %arg1: tensor<4x4xf32>) -> tensor<4x4xf32>{
-    cond_br %cond, ^bb1, ^bb2
-  ^bb1:
-    br ^exit(%arg0 : tensor<2xf32>)
-  ^bb2:
-    br ^exit(%arg0 : tensor<2xf32>)
-  ^exit(%arg2: tensor<2xf32>):
-    return %arg1 : tensor<4x4xf32>
-}
-//      CHECK: (%[[ARG0:.*]]: [[ARG0_TYPE:.*]], %[[COND:.*]]: i1, %[[ARG1:.*]]: [[ARG1_TYPE:.*]]) -> [[RESULT_TYPE:.*]] {
-//      CHECK: br ^[[EXIT_BLOCK:.*]](%[[ARG0]] : [[ARG0_TYPE]])
-//      CHECK: br ^[[EXIT_BLOCK]](%[[ARG0]] : [[ARG0_TYPE]])
-//      CHECK: ^[[EXIT_BLOCK]](%{{.*}}: [[ARG0_TYPE]])
-// CHECK-NEXT:  return %[[ARG1]] : [[RESULT_TYPE]]
-
-// -----
-
-// CHECK-LABEL: func @callee
-func @callee(%arg1: tensor<5xf32>) -> (tensor<5xf32>, memref<2xf32>) {
-  %buff = alloc() : memref<2xf32>
-  return %arg1, %buff : tensor<5xf32>, memref<2xf32>
-}
-// CHECK: (%[[CALLEE_ARG:.*]]: memref<5xf32>) -> (memref<5xf32>, memref<2xf32>)
-// CHECK: %[[ALLOC:.*]] = alloc()
-// CHECK: return %[[CALLEE_ARG]], %[[ALLOC]]
-
-// CHECK-LABEL: func @caller
-func @caller(%arg0: tensor<5xf32>) -> tensor<5xf32> {
-  %x:2 = call @callee(%arg0) : (tensor<5xf32>) -> (tensor<5xf32>, memref<2xf32>)
-  %y:2 = call @callee(%x#0) : (tensor<5xf32>) -> (tensor<5xf32>, memref<2xf32>)
-  return %y#0 : tensor<5xf32>
-}
-// CHECK: (%[[CALLER_ARG:.*]]: memref<5xf32>) -> memref<5xf32>
-// CHECK: %[[X:.*]]:2 = call @callee(%[[CALLER_ARG]])
-// CHECK: %[[Y:.*]]:2 = call @callee(%[[X]]#0)
-// CHECK: return %[[Y]]#0
-
-// -----
-
-// Test case: Testing BufferizeCallOpConverter to see if it matches with the
-// signature of the new signature of the callee function when there are tuple
-// typed args and results. BufferizeTypeConverter is set to flatten tuple typed
-// arguments. The tuple typed values should be decomposed and composed using
-// get_tuple_element and make_tuple operations of test dialect. Tensor types are
-// converted to Memref. Memref typed function results remain as function
-// results.
-
-// CHECK-LABEL: func @callee
-func @callee(%arg0: tuple<tensor<2xf32>,i1, tensor<5xf32>>) -> (tuple<tensor<2xf32>,i1, tensor<5xf32>>){
-  return %arg0 : tuple<tensor<2xf32>,i1, tensor<5xf32>>
-}
-// CHECK-SAME: (%[[ARG0:.*]]: memref<2xf32>, %[[ARG1:.*]]: i1, %[[ARG2:.*]]: memref<5xf32>)
-// CHECK-SAME: (memref<2xf32>, i1, memref<5xf32>)
-// CHECK-NEXT: %[[TUPLE:.*]] = "test.make_tuple"(%[[ARG0]], %[[ARG1]], %[[ARG2]])
-// CHECK-NEXT: %[[FIRST_ELEM:.*]] = "test.get_tuple_element"(%[[TUPLE]]) {index = 0 : i32}
-// CHECK-NEXT: %[[SECOND_ELEM:.*]] = "test.get_tuple_element"(%[[TUPLE]]) {index = 1 : i32}
-// CHECK-NEXT: %[[THIRD_ELEM:.*]]  = "test.get_tuple_element"(%[[TUPLE]]) {index = 2 : i32}
-// CHECK-NEXT: return %[[FIRST_ELEM]], %[[SECOND_ELEM]], %[[THIRD_ELEM]]
-
-// CHECK-LABEL: func @caller
-func @caller(%arg0: tuple<tensor<2xf32>,i1, tensor<5xf32>>) -> tuple<tensor<2xf32>,i1, tensor<5xf32>>{
-  %x0 = call @callee(%arg0) : (tuple<tensor<2xf32>,i1, tensor<5xf32>>) -> (tuple<tensor<2xf32>,i1, tensor<5xf32>>)
-  %y0 = call @callee(%x0) : (tuple<tensor<2xf32>,i1, tensor<5xf32>>) -> (tuple<tensor<2xf32>,i1, tensor<5xf32>>)
-  return %y0 : tuple<tensor<2xf32>,i1, tensor<5xf32>>
-}
-// CHECK-SAME: (%[[ARG0:.*]]: memref<2xf32>, %[[ARG1:.*]]: i1, %[[ARG2:.*]]: memref<5xf32>)
-// CHECK-SAME: (memref<2xf32>, i1, memref<5xf32>)
-// CHECK-NEXT: %[[ARG_TUPLE:.*]] = "test.make_tuple"(%[[ARG0]], %[[ARG1]], %[[ARG2]])
-// CHECK-NEXT: %[[FIRST_ELEM:.*]] = "test.get_tuple_element"(%[[ARG_TUPLE]]) {index = 0 : i32}
-// CHECK-NEXT: %[[SECOND_ELEM:.*]] = "test.get_tuple_element"(%[[ARG_TUPLE]]) {index = 1 : i32}
-// CHECK-NEXT: %[[THIRD_ELEM:.*]]  = "test.get_tuple_element"(%[[ARG_TUPLE]]) {index = 2 : i32}
-// CHECK-NEXT: %[[CALLEE_RESULTS:.*]]:3 = call @callee(%[[FIRST_ELEM]], %[[SECOND_ELEM]], %[[THIRD_ELEM]])
-// CHECK-SAME: (memref<2xf32>, i1, memref<5xf32>) -> (memref<2xf32>, i1, memref<5xf32>)
-// CHECK-NEXT: %[[RESULT_TUPLE:.*]] = "test.make_tuple"(%[[CALLEE_RESULTS]]#0, %[[CALLEE_RESULTS]]#1, %[[CALLEE_RESULTS]]#2)
-// CHECK-NEXT: %[[FIRST_ELEM:.*]] = "test.get_tuple_element"(%[[RESULT_TUPLE]]) {index = 0 : i32}
-// CHECK-NEXT: %[[SECOND_ELEM:.*]] = "test.get_tuple_element"(%[[RESULT_TUPLE]]) {index = 1 : i32}
-// CHECK-NEXT: %[[THIRD_ELEM:.*]]  = "test.get_tuple_element"(%[[RESULT_TUPLE]]) {index = 2 : i32}
-// CHECK-NEXT: %[[CALLEE_RESULTS:.*]]:3 = call @callee(%[[FIRST_ELEM]], %[[SECOND_ELEM]], %[[THIRD_ELEM]])
-// CHECK-SAME: (memref<2xf32>, i1, memref<5xf32>) -> (memref<2xf32>, i1, memref<5xf32>)
-// CHECK-NEXT: %[[RETURN_TUPLE:.*]] = "test.make_tuple"(%[[CALLEE_RESULTS]]#0, %[[CALLEE_RESULTS]]#1, %[[CALLEE_RESULTS]]#2)
-// CHECK-NEXT: %[[FIRST_ELEM:.*]] = "test.get_tuple_element"(%[[RETURN_TUPLE]]) {index = 0 : i32}
-// CHECK-NEXT: %[[SECOND_ELEM:.*]] = "test.get_tuple_element"(%[[RETURN_TUPLE]]) {index = 1 : i32}
-// CHECK-NEXT: %[[THIRD_ELEM:.*]]  = "test.get_tuple_element"(%[[RETURN_TUPLE]]) {index = 2 : i32}
-// CHECK-NEXT: return %[[FIRST_ELEM]], %[[SECOND_ELEM]], %[[THIRD_ELEM]]
-
-// -----
-
-// Test case: Testing BufferizeFuncOpConverter and
-// BufferizeReturnOpConverter to see if the return operation matches with the
-// new function signature when there are tuple typed args and results.
-// BufferizeTypeConverter is set to flatten tuple typed arguments. The tuple
-// typed values should be decomposed and composed using get_tuple_element and
-// make_tuple operations of test dialect. Tensor types are converted to Memref.
-// Memref typed function results remain as function results.
-
-// CHECK-LABEL: func @decompose_tuple_typed_function_args_and_results
-func @decompose_tuple_typed_function_args_and_results(%arg0: tuple<i1,f32>, %arg1: tensor<10xf32>, %arg2: tuple<i1, tensor<5xf32>>) -> (tuple<i1, tensor<5xf32>>, tensor<10xf32>, tuple<i1,f32>){
-  return %arg2, %arg1, %arg0 : tuple<i1, tensor<5xf32>>, tensor<10xf32>, tuple<i1,f32>
-}
-// CHECK-SAME: %[[ARG0:.*]]: i1, %[[ARG1:.*]]: f32, %[[ARG2:.*]]: memref<10xf32>, %[[ARG3:.*]]: i1, %[[ARG4:.*]]: memref<5xf32>
-// CHECK-SAME: (i1, memref<5xf32>, memref<10xf32>, i1, f32)
-// CHECK-NEXT: %[[FIRST_TUPLE:.*]] = "test.make_tuple"(%[[ARG0]], %[[ARG1]])
-// CHECK-NEXT: %[[SECOND_TUPLE:.*]] = "test.make_tuple"(%[[ARG3]], %[[ARG4]])
-// CHECK-NEXT: %[[SECOND_TUPLE_FIRST_ELEM:.*]]  = "test.get_tuple_element"(%[[SECOND_TUPLE]]) {index = 0 : i32}
-// CHECK-NEXT: %[[SECOND_TUPLE_SECOND_ELEM:.*]] = "test.get_tuple_element"(%[[SECOND_TUPLE]]) {index = 1 : i32}
-// CHECK-NEXT: %[[FIRST_TUPLE_FIRST_ELEM:.*]] = "test.get_tuple_element"(%[[FIRST_TUPLE]]) {index = 0 : i32}
-// CHECK-NEXT: %[[FIRST_TUPLE_SECOND_ELEM:.*]] = "test.get_tuple_element"(%[[FIRST_TUPLE]]) {index = 1 : i32}
-// CHECK-NEXT: return %[[SECOND_TUPLE_FIRST_ELEM]], %[[SECOND_TUPLE_SECOND_ELEM]], %[[ARG2]], %[[FIRST_TUPLE_FIRST_ELEM]], %[[FIRST_TUPLE_SECOND_ELEM]]

mlir/test/lib/Transforms/CMakeLists.txt

 # Exclude tests from libMLIR.so
 add_mlir_library(MLIRTestTransforms
   TestAffineLoopParametricTiling.cpp
   TestExpandMemRefReshape.cpp
   TestExpandTanh.cpp
   TestCallGraph.cpp
+  TestDecomposeCallGraphTypes.cpp
   TestConstantFold.cpp
   TestConvVectorization.cpp
   TestConvertCallOp.cpp
   TestConvertGPUKernelToCubin.cpp
   TestConvertGPUKernelToHsaco.cpp
   TestDominance.cpp
   TestDynamicPipeline.cpp
-  TestFinalizingBufferize.cpp
   TestLoopFusion.cpp
   TestGpuMemoryPromotion.cpp
   TestGpuParallelLoopMapping.cpp
   TestGpuRewrite.cpp
   TestInlining.cpp
   TestLinalgCodegenStrategy.cpp
   TestLinalgFusionTransforms.cpp
   TestLinalgHoisting.cpp

mlir/test/lib/Transforms/TestDecomposeCallGraphTypes.cpp

+//===- TestDecomposeCallGraphTypes.cpp - Test CG type decomposition -------===//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+
+#include "TestDialect.h"
+#include "mlir/Dialect/StandardOps/IR/Ops.h"
+#include "mlir/IR/Builders.h"
+#include "mlir/Pass/Pass.h"
+#include "mlir/Transforms/DecomposeCallGraphTypes.h"
+#include "mlir/Transforms/DialectConversion.h"
+
+using namespace mlir;
+
+namespace {
+// XXX: document
+struct TestDecomposeCallGraphTypes
+    : public PassWrapper<TestDecomposeCallGraphTypes, OperationPass<ModuleOp>> {
+
+  void getDependentDialects(DialectRegistry &registry) const override {
+    registry.insert<test::TestDialect>();
+  }
+  void runOnOperation() override {
+    auto module = getOperation();
+    auto *context = &getContext();
+    TypeConverter typeConverter;
+    ConversionTarget target(*context);
+    ValueDecomposer decomposer;
+    OwningRewritePatternList patterns;
+
+    target.addLegalDialect<test::TestDialect>();
+
+    target.addDynamicallyLegalOp<ReturnOp>([&](ReturnOp op) {
+      return typeConverter.isLegal(op.getOperandTypes());
+    });
+    target.addDynamicallyLegalOp<CallOp>(
+        [&](CallOp op) { return typeConverter.isLegal(op); });
+    target.addDynamicallyLegalOp<FuncOp>([&](FuncOp op) {
+      return typeConverter.isSignatureLegal(op.getType());
+    });
+
+    typeConverter.addConversion([](Type type) { return type; });
+    typeConverter.addConversion(
+        [](TupleType tupleType, SmallVectorImpl<Type> &types) {
+          tupleType.getFlattenedTypes(types);
+          return success();
+        });
+
+    decomposer.addDecomposeValueConversion([](OpBuilder &builder, Location loc,
+                                              TupleType resultType, Value value,
+                                              SmallVectorImpl<Value> &values) {
+      for (unsigned i = 0, e = resultType.size(); i < e; ++i) {
+        Value res = builder.create<test::GetTupleElementOp>(
+            loc, resultType.getType(i), value, builder.getI32IntegerAttr(i));
+        values.push_back(res);
+      }
+      return success();
+    });
+
+    typeConverter.addArgumentMaterialization(
+        [](OpBuilder &builder, TupleType resultType, ValueRange inputs,
+           Location loc) -> Optional<Value> {
+          if (inputs.size() == 1)
+            return llvm::None;
+          TypeRange TypeRange = inputs.getTypes();
+          SmallVector<Type, 2> types(TypeRange.begin(), TypeRange.end());
+          TupleType tuple = TupleType::get(types, builder.getContext());
+          Value value = builder.create<test::MakeTupleOp>(loc, tuple, inputs);
+          return value;
+        });
+
+    populateDecomposeCallGraphTypesPatterns(context, typeConverter, decomposer,
+                                            patterns);
+
+    if (failed(applyPartialConversion(module, target, std::move(patterns))))
+      return signalPassFailure();
+  }
+};
+
+} // end anonymous namespace
+
+namespace mlir {
+namespace test {
+void registerTestDecomposeCallGraphTypes() {
+  PassRegistration<TestDecomposeCallGraphTypes> pass(
+      "test-decompose-call-graph-types",
+      "Decomposes types at call graph boundaries.");
+}
+} // namespace test
+} // namespace mlir

mlir/test/lib/Transforms/TestFinalizingBufferize.cpp

-//===- TestFinalizingBufferize.cpp - Finalizing bufferization ---*- C++ -*-===//
-//
-// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
-// See https://llvm.org/LICENSE.txt for license information.
-// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
-//
-//===----------------------------------------------------------------------===//
-//
-// This file implements a pass that exercises the functionality of finalizing
-// bufferizations.
-//
-//===----------------------------------------------------------------------===//
-
-#include "TestDialect.h"
-#include "mlir/Conversion/StandardToLLVM/ConvertStandardToLLVM.h"
-#include "mlir/IR/BlockAndValueMapping.h"
-#include "mlir/IR/Function.h"
-#include "mlir/IR/Operation.h"
-#include "mlir/Pass/Pass.h"
-#include "mlir/Pass/PassManager.h"
-#include "mlir/Transforms/Bufferize.h"
-
-using namespace mlir;
-
-namespace {
-/// This pass is a test for "finalizing" bufferize conversions.
-///
-/// A "finalizing" bufferize conversion is one that performs a "full" conversion
-/// and expects all tensors to be gone from the program. This in particular
-/// involves rewriting funcs (including block arguments of the contained
-/// region), calls, and returns. The unique property of finalizing bufferization
-/// passes is that they cannot be done via a local transformation with suitable
-/// materializations to ensure composability (as other bufferization passes do).
-/// For example, if a call is rewritten, the callee needs to be rewritten
-/// otherwise the IR will end up invalid. Thus, finalizing bufferization passes
-/// require an atomic change to the entire program (e.g. the whole module).
-///
-/// TODO: Split out BufferizeFinalizationPolicy from BufferizeTypeConverter.
-struct TestFinalizingBufferizePass
-    : mlir::PassWrapper<TestFinalizingBufferizePass, OperationPass<ModuleOp>> {
-
-  /// Converts tensor based test operations to buffer based ones using
-  /// bufferize.
-  class TensorBasedOpConverter
-      : public BufferizeOpConversionPattern<test::TensorBasedOp> {
-  public:
-    using BufferizeOpConversionPattern<
-        test::TensorBasedOp>::BufferizeOpConversionPattern;
-
-    LogicalResult
-    matchAndRewrite(test::TensorBasedOp op, ArrayRef<Value> operands,
-                    ConversionPatternRewriter &rewriter) const final {
-      mlir::test::TensorBasedOpAdaptor adaptor(
-          operands, op.getOperation()->getAttrDictionary());
-
-      // The input needs to be turned into a buffer first. Until then, bail out.
-      if (!adaptor.input().getType().isa<MemRefType>())
-        return failure();
-
-      Location loc = op.getLoc();
-
-      // Update the result type to a memref type.
-      auto type = op.getResult().getType().cast<ShapedType>();
-      if (!type.hasStaticShape())
-        return rewriter.notifyMatchFailure(
-            op, "dynamic shapes not currently supported");
-      auto memrefType = MemRefType::get(type.getShape(), type.getElementType());
-      Value newOutputBuffer = rewriter.create<AllocOp>(loc, memrefType);
-
-      // Generate a new test operation that works on buffers.
-      rewriter.create<mlir::test::BufferBasedOp>(loc,
-                                                 /*input=*/adaptor.input(),
-                                                 /*output=*/newOutputBuffer);
-
-      // Replace the results of the old op with the new output buffers.
-      rewriter.replaceOp(op, newOutputBuffer);
-      return success();
-    }
-  };
-
-  void getDependentDialects(DialectRegistry &registry) const override {
-    registry.insert<test::TestDialect>();
-  }
-
-  void runOnOperation() override {
-    MLIRContext &context = this->getContext();
-    ConversionTarget target(context);
-    BufferizeTypeConverter converter;
-
-    // Mark all Standard operations legal.
-    target.addLegalDialect<StandardOpsDialect>();
-    target.addLegalOp<test::MakeTupleOp>();
-    target.addLegalOp<test::GetTupleElementOp>();
-    target.addLegalOp<ModuleOp>();
-    target.addLegalOp<ModuleTerminatorOp>();
-
-    // Mark all Test operations illegal as long as they work on tensors.
-    auto isLegalOperation = [&](Operation *op) {
-      return converter.isLegal(op);
-    };
-    target.addDynamicallyLegalDialect<test::TestDialect>(isLegalOperation);
-
-    // Mark Standard Return operations illegal as long as one operand is tensor.
-    target.addDynamicallyLegalOp<mlir::ReturnOp>([&](mlir::ReturnOp returnOp) {
-      return converter.isLegal(returnOp.getOperandTypes());
-    });
-
-    // Mark Standard Call Operation illegal as long as it operates on tensor.
-    target.addDynamicallyLegalOp<mlir::CallOp>(
-        [&](mlir::CallOp callOp) { return converter.isLegal(callOp); });
-
-    // Mark the function whose arguments are in tensor-type illegal.
-    target.addDynamicallyLegalOp<FuncOp>([&](FuncOp funcOp) {
-      return converter.isSignatureLegal(funcOp.getType()) &&
-             converter.isLegal(&funcOp.getBody());
-    });
-
-    converter.addDecomposeTypeConversion(
-        [](TupleType tupleType, SmallVectorImpl<Type> &types) {
-          tupleType.getFlattenedTypes(types);
-          return success();
-        });
-
-    converter.addArgumentMaterialization(
-        [](OpBuilder &builder, TupleType resultType, ValueRange inputs,
-           Location loc) -> Optional<Value> {
-          if (inputs.size() == 1)
-            return llvm::None;
-          TypeRange TypeRange = inputs.getTypes();
-          SmallVector<Type, 2> types(TypeRange.begin(), TypeRange.end());
-          TupleType tuple = TupleType::get(types, builder.getContext());
-          mlir::Value value =
-              builder.create<test::MakeTupleOp>(loc, tuple, inputs);
-          return value;
-        });
-
-    converter.addDecomposeValueConversion([](OpBuilder &builder, Location loc,
-                                             TupleType resultType, Value value,
-                                             SmallVectorImpl<Value> &values) {
-      for (unsigned i = 0, e = resultType.size(); i < e; ++i) {
-        Value res = builder.create<test::GetTupleElementOp>(
-            loc, resultType.getType(i), value, builder.getI32IntegerAttr(i));
-        values.push_back(res);
-      }
-      return success();
-    });
-
-    OwningRewritePatternList patterns;
-    populateWithBufferizeOpConversionPatterns<ReturnOp, ReturnOp, test::CopyOp>(
-        &context, converter, patterns);
-    patterns.insert<TensorBasedOpConverter>(&context, converter);
-
-    if (failed(applyFullConversion(this->getOperation(), target,
-                                   std::move(patterns))))
-      this->signalPassFailure();
-  };
-};
-} // end anonymous namespace
-
-namespace mlir {
-namespace test {
-void registerTestFinalizingBufferizePass() {
-  PassRegistration<TestFinalizingBufferizePass>(
-      "test-finalizing-bufferize", "Tests finalizing bufferize conversions");
-}
-} // namespace test
-} // namespace mlir

mlir/tools/mlir-opt/mlir-opt.cpp

 void registerPatternsTestPass();
 void registerSimpleParametricTilingPass();
 void registerTestAffineLoopParametricTilingPass();
 void registerTestCallGraphPass();
 void registerTestConstantFold();
 void registerTestConvVectorization();
 void registerTestConvertGPUKernelToCubinPass();
 void registerTestConvertGPUKernelToHsacoPass();
+void registerTestDecomposeCallGraphTypes();
 void registerTestDialect(DialectRegistry &);
 void registerTestDominancePass();
 void registerTestDynamicPipelinePass();
 void registerTestExpandMemRefReshapePass();
 void registerTestExpandTanhPass();
-void registerTestFinalizingBufferizePass();
 void registerTestGpuParallelLoopMappingPass();
 void registerTestInterfaces();
 void registerTestLinalgCodegenStrategy();
 void registerTestLinalgFusionTransforms();
 void registerTestLinalgGreedyFusion();
 void registerTestLinalgHoisting();
 void registerTestLinalgTransforms();
 void registerTestLivenessPass();
void registerTestPasses() {
   test::registerTestConstantFold();
 #if MLIR_CUDA_CONVERSIONS_ENABLED
   test::registerTestConvertGPUKernelToCubinPass();
 #endif
 #if MLIR_ROCM_CONVERSIONS_ENABLED
   test::registerTestConvertGPUKernelToHsacoPass();
 #endif
   test::registerTestConvVectorization();
+  test::registerTestDecomposeCallGraphTypes();
   test::registerTestDominancePass();
   test::registerTestDynamicPipelinePass();
   test::registerTestExpandMemRefReshapePass();
   test::registerTestExpandTanhPass();
-  test::registerTestFinalizingBufferizePass();
   test::registerTestGpuParallelLoopMappingPass();
   test::registerTestInterfaces();
   test::registerTestLinalgCodegenStrategy();
   test::registerTestLinalgFusionTransforms();
   test::registerTestLinalgGreedyFusion();
   test::registerTestLinalgHoisting();
   test::registerTestLinalgTransforms();
   test::registerTestLivenessPass();