[build] Update llvm tag to a3f2751 (#2397)

This commit updates the `llvm-project` and `mlir-hlo` submodules to commits: llvm-project: a3f2751 mlir-hlo: 97c7e4b4506c3a2441c923e592833f45da439009 Changes made: - Rename `getSuccessorEntryOperands` with `getEntrySuccessorOperands` and remove `operands` from `getSuccessorRegions` (https://reviews.llvm.org/D157506) - Make `TypeConverter` a `const` (https://reviews.llvm.org/D157601)
llvm · Aug 15, 2023 · 41bafe1 · 41bafe1
1 parent 94f7593
commit 41bafe1
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Showing 15 changed files with 43 additions and 48 deletions.
diff --git a/externals/llvm-project b/externals/llvm-project
diff --git a/externals/mlir-hlo b/externals/mlir-hlo
diff --git a/include/torch-mlir/Conversion/Utils/Utils.h b/include/torch-mlir/Conversion/Utils/Utils.h
@@ -76,7 +76,7 @@ SmallVector<Value> getAsConstantIndexValues(OpBuilder &b, Location loc,
 // convert their elements to valid target type.
 // TODO: remove this when list gets full support.
 SmallVector<Value> getTypeConvertedValues(OpBuilder &b, Location loc,
-                                          TypeConverter *converter,
+                                          const TypeConverter *converter,
                                           SmallVectorImpl<Value> &vs);
 
 mlir::RankedTensorType GetTypeFromTensorShape(llvm::ArrayRef<int64_t> shape,

diff --git a/include/torch-mlir/Dialect/Torch/IR/TorchOps.td b/include/torch-mlir/Dialect/Torch/IR/TorchOps.td
@@ -507,7 +507,7 @@ def Torch_PrimCallMethodOp : Torch_Op<"prim.CallMethod", []> {
 }
 
 def Torch_PrimLoopOp : Torch_Op<"prim.Loop", [
-  DeclareOpInterfaceMethods<RegionBranchOpInterface, ["getSuccessorEntryOperands"]>]> {
+  DeclareOpInterfaceMethods<RegionBranchOpInterface, ["getEntrySuccessorOperands"]>]> {
   let summary = "TorchScript prim::Loop op";
   let description = [{
     This op (together with prim.Loop.condition) define a looping construct

diff --git a/lib/Conversion/TorchToLinalg/DataMovement.cpp b/lib/Conversion/TorchToLinalg/DataMovement.cpp
@@ -328,7 +328,7 @@ class ConvertAtenViewOp : public OpConversionPattern<AtenViewOp> {
     SmallVector<int64_t> inputShape =
         makeShapeTorchCompatible(inputType.getShape());
     int64_t inputRank = inputType.getRank();
-    TypeConverter *typeConverter = getTypeConverter();
+    const TypeConverter *typeConverter = getTypeConverter();
     auto resultType =
         typeConverter->convertType(op.getType()).cast<RankedTensorType>();
     int64_t resultRank = resultType.getRank();
@@ -695,7 +695,7 @@ class ConvertAtenSqueezeOp : public OpConversionPattern<AtenSqueezeOp> {
     Value input = adaptor.getSelf();
     auto inputType = input.getType().cast<RankedTensorType>();
     int64_t inputRank = inputType.getRank();
-    TypeConverter *typeConverter = getTypeConverter();
+    const TypeConverter *typeConverter = getTypeConverter();
     auto resultType =
         typeConverter->convertType(op.getType()).cast<RankedTensorType>();
     int64_t resultRank = resultType.getRank();
@@ -804,7 +804,7 @@ class ConvertAtenSqueezeDimOp : public OpConversionPattern<AtenSqueezeDimOp> {
           op, "unimplemented: dim(th) dimension is not expected to be dynamic");
     }
 
-    TypeConverter *typeConverter = getTypeConverter();
+    const TypeConverter *typeConverter = getTypeConverter();
     auto resultType =
         typeConverter->convertType(op.getType()).cast<RankedTensorType>();
     int64_t resultRank = resultType.getRank();
@@ -1046,7 +1046,7 @@ class ConvertAtenSliceTensorOp : public OpConversionPattern<AtenSliceTensorOp> {
       return failure();
 
     Location loc = op.getLoc();
-    TypeConverter *typeConverter = getTypeConverter();
+    const TypeConverter *typeConverter = getTypeConverter();
 
     auto input = adaptor.getSelf();
     RankedTensorType resultType =
@@ -1081,7 +1081,7 @@ class ConvertAtenCatOp : public OpConversionPattern<AtenCatOp> {
     if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
       return failure();
     Location loc = op.getLoc();
-    TypeConverter *typeConverter = getTypeConverter();
+    const TypeConverter *typeConverter = getTypeConverter();
 
     // Collect all the tensors to be concatenated.
     auto tensorList = op.getTensors();
@@ -1312,7 +1312,7 @@ class ConvertAtenSliceScatterOp
       return failure();
 
     Location loc = op.getLoc();
-    TypeConverter *typeConverter = getTypeConverter();
+    const TypeConverter *typeConverter = getTypeConverter();
 
     auto input = adaptor.getSelf();
 
@@ -1361,7 +1361,7 @@ class ConvertAtenViewAsComplexOp
       return failure();
 
     Location loc = op.getLoc();
-    TypeConverter *typeConverter = getTypeConverter();
+    const TypeConverter *typeConverter = getTypeConverter();
     MLIRContext *context = rewriter.getContext();
 
     auto input = adaptor.getSelf();

diff --git a/lib/Conversion/TorchToLinalg/Pooling.cpp b/lib/Conversion/TorchToLinalg/Pooling.cpp
@@ -32,7 +32,7 @@ using namespace mlir::torch::Torch;
 template <typename OpTy>
 static LogicalResult
 checkAndGetPoolingParameters(OpTy op, ConversionPatternRewriter &rewriter,
-                             TypeConverter *typeConverter, bool &ceilMode,
+                             const TypeConverter *typeConverter, bool &ceilMode,
                              SmallVectorImpl<Value> &kernelSizeIntValues,
                              SmallVectorImpl<int64_t> &strideInts,
                              SmallVectorImpl<int64_t> &paddingInts) {
@@ -72,7 +72,6 @@ checkAndGetPoolingParameters(OpTy op, ConversionPatternRewriter &rewriter,
   return success();
 }
 
-
 // Creates a pooling operation based on the type specified by `OpTy` and
 // arguments passed.
 template <typename OpTy>
@@ -153,7 +152,7 @@ class ConvertAtenMaxPool2dOp : public OpConversionPattern<AtenMaxPool2dOp> {
     if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
       return failure();
 
-    TypeConverter *typeConverter = getTypeConverter();
+    const TypeConverter *typeConverter = getTypeConverter();
     Value self = adaptor.getSelf();
     int64_t selfRank = self.getType().cast<RankedTensorType>().getRank();
     // TODO: Add support for 3D inputs.
@@ -225,7 +224,7 @@ class ConvertAtenMaxPool2dWithIndicesOp
     if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
       return failure();
     Location loc = op->getLoc();
-    TypeConverter *typeConverter = getTypeConverter();
+    const TypeConverter *typeConverter = getTypeConverter();
     Value self = adaptor.getSelf();
     RankedTensorType selfType = self.getType().cast<RankedTensorType>();
     Type elementType = selfType.getElementType();
@@ -386,7 +385,7 @@ class ConvertAtenAvgPoolOp : public OpConversionPattern<OpTy> {
       return failure();
 
     Location loc = op->getLoc();
-    TypeConverter *typeConverter = this->getTypeConverter();
+    const TypeConverter *typeConverter = this->getTypeConverter();
     Value self = adaptor.getSelf();
 
     Type inputElementType =

diff --git a/lib/Conversion/TorchToLinalg/TensorConstructors.cpp b/lib/Conversion/TorchToLinalg/TensorConstructors.cpp
@@ -106,7 +106,7 @@ class ConvertConstantTensorAllocOp : public OpConversionPattern<OpTy> {
     }
 
     Location loc = op.getLoc();
-    TypeConverter *typeConverter = this->getTypeConverter();
+    const TypeConverter *typeConverter = this->getTypeConverter();
     SmallVector<Value> resultSizeTorchInt, resultSize, resultSizeIndex;
     if (!getListConstructElements(op.getSize(), resultSizeTorchInt)) {
       return rewriter.notifyMatchFailure(
@@ -211,7 +211,7 @@ class ConvertAtenEmptyMemoryFormatOp
     }
 
     Location loc = op.getLoc();
-    TypeConverter *typeConverter = this->getTypeConverter();
+    const TypeConverter *typeConverter = this->getTypeConverter();
     SmallVector<Value> resultSizeTorchInt, resultSize, resultSizeIndex;
     if (!getListConstructElements(op.getSize(), resultSizeTorchInt)) {
       return rewriter.notifyMatchFailure(
@@ -282,7 +282,7 @@ class ConvertAtenArangeStartStepOp
     }
 
     Location loc = op.getLoc();
-    TypeConverter *typeConverter = this->getTypeConverter();
+    const TypeConverter *typeConverter = this->getTypeConverter();
     RankedTensorType resultType =
         typeConverter->convertType(op->getResult(0).getType())
             .cast<RankedTensorType>();

diff --git a/lib/Conversion/TorchToLinalg/Uncategorized.cpp b/lib/Conversion/TorchToLinalg/Uncategorized.cpp
@@ -127,8 +127,10 @@ static Value buildUnitNormalCdf(OpBuilder &b, Location &loc, Value x) {
 }
 
 template <typename MathOpTy>
-static Value createCalculationForMathOpWithDtypeConversion(
-    OpBuilder &b, TypeConverter *converter, Value payloadArg, Operation *op) {
+static Value
+createCalculationForMathOpWithDtypeConversion(OpBuilder &b,
+                                              const TypeConverter *converter,
+                                              Value payloadArg, Operation *op) {
   Type dtype = converter->convertType(op->getResult(0).getType())
                    .template cast<RankedTensorType>()
                    .getElementType();
@@ -207,7 +209,7 @@ createTriangularMatrix(OpBuilder &b, Location loc, ValueRange payloadArgs,
 }
 
 static Value createLinalgPayloadCalculationForElementwiseOp(
-    OpBuilder &b, Location loc, TypeConverter *converter,
+    OpBuilder &b, Location loc, const TypeConverter *converter,
     ValueRange payloadArgs, Operation *op, ArrayRef<Value> operands) {
   if (isa<AtenFloorOp>(op))
     return b.create<math::FloorOp>(loc, payloadArgs[0]);

diff --git a/lib/Conversion/TorchToSCF/TorchToSCF.cpp b/lib/Conversion/TorchToSCF/TorchToSCF.cpp
@@ -77,7 +77,7 @@ class ConvertTorchPrimLoopWhileLikeOp : public OpConversionPattern<PrimLoopOp> {
     if (op.isForLike())
       return failure();
 
-    TypeConverter *typeConverter = getTypeConverter();
+    const TypeConverter *typeConverter = getTypeConverter();
     SmallVector<Type, 1> newResultTypes;
     if (failed(
             typeConverter->convertTypes(op.getResultTypes(), newResultTypes)))
@@ -217,7 +217,7 @@ class ConvertTorchPrimLoopForLikeOp : public OpConversionPattern<PrimLoopOp> {
     if (!op.isForLike())
       return failure();
 
-    TypeConverter *typeConverter = getTypeConverter();
+    const TypeConverter *typeConverter = getTypeConverter();
     SmallVector<Type, 1> newResultTypes;
     if (failed(
             typeConverter->convertTypes(op.getResultTypes(), newResultTypes)))

diff --git a/lib/Conversion/TorchToStablehlo/Basic.cpp b/lib/Conversion/TorchToStablehlo/Basic.cpp
@@ -1555,7 +1555,7 @@ LogicalResult ConvertAtenOp<AtenEmptyMemoryFormatOp>::matchAndRewrite(
   }
 
   Location loc = op.getLoc();
-  TypeConverter *typeConverter = this->getTypeConverter();
+  const TypeConverter *typeConverter = this->getTypeConverter();
   SmallVector<Value> resultSizeTorchInt, resultSize, resultSizeIndex;
   if (!getListConstructElements(op.getSize(), resultSizeTorchInt)) {
     return rewriter.notifyMatchFailure(

diff --git a/lib/Conversion/TorchToStablehlo/GatherScatter.cpp b/lib/Conversion/TorchToStablehlo/GatherScatter.cpp
@@ -342,7 +342,7 @@ LogicalResult ConvertAtenOp<AtenSliceScatterOp>::matchAndRewrite(
     return failure();
 
   Location loc = op.getLoc();
-  TypeConverter *typeConverter = getTypeConverter();
+  const TypeConverter *typeConverter = getTypeConverter();
 
   auto input = adaptor.getSelf();
 

diff --git a/lib/Conversion/TorchToTMTensor/TorchToTMTensor.cpp b/lib/Conversion/TorchToTMTensor/TorchToTMTensor.cpp
@@ -309,7 +309,7 @@ class ConvertAtenScatterSrcOp : public OpConversionPattern<AtenScatterSrcOp> {
     if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
       return failure();
     Location loc = op.getLoc();
-    TypeConverter *typeConverter = getTypeConverter();
+    const TypeConverter *typeConverter = getTypeConverter();
     Value self = adaptor.getSelf();
     Value index = adaptor.getIndex();
     Value src = adaptor.getSrc();
@@ -361,7 +361,7 @@ class ConvertAtenBincountOp : public OpConversionPattern<AtenBincountOp> {
       return failure();
     Location loc = op.getLoc();
     MLIRContext *context = op->getContext();
-    TypeConverter *typeConverter = getTypeConverter();
+    const TypeConverter *typeConverter = getTypeConverter();
     Value input = adaptor.getSelf();
     Value torchTypeInput = op.getSelf();
     Value minlength = adaptor.getMinlength();

diff --git a/lib/Conversion/TorchToTosa/TorchToTosa.cpp b/lib/Conversion/TorchToTosa/TorchToTosa.cpp
@@ -2121,7 +2121,7 @@ LogicalResult ConvertAtenOp<AtenBatchNormOp>::matchAndRewrite(
   // reshaped so it sits on the same dim as 'C'.
   auto reshapeToNormInputDim = [&](Operation *op,
                                    ConversionPatternRewriter &rewriter,
-                                   TypeConverter *converter, Type outType,
+                                   const TypeConverter *converter, Type outType,
                                    const Value toBcast, Value &result) {
     RankedTensorType toBcastType =
         toBcast.getType().dyn_cast<RankedTensorType>();
@@ -3809,7 +3809,7 @@ LogicalResult ConvertAtenOp<AtenArangeStartStepOp>::matchAndRewrite(
     AtenArangeStartStepOp op, OpAdaptor adaptor,
     ConversionPatternRewriter &rewriter) const {
 
-  TypeConverter *typeConverter = this->getTypeConverter();
+  const TypeConverter *typeConverter = this->getTypeConverter();
   RankedTensorType resultType =
       typeConverter->convertType(op->getResult(0).getType())
           .cast<RankedTensorType>();
@@ -3859,7 +3859,7 @@ LogicalResult ConvertAtenOp<PrimNumToTensorScalarOp>::matchAndRewrite(
     PrimNumToTensorScalarOp op, OpAdaptor adaptor,
     ConversionPatternRewriter &rewriter) const {
 
-  TypeConverter *typeConverter = this->getTypeConverter();
+  const TypeConverter *typeConverter = this->getTypeConverter();
   RankedTensorType resultType =
       typeConverter->convertType(op->getResult(0).getType())
           .cast<RankedTensorType>();
@@ -4673,7 +4673,7 @@ template <>
 LogicalResult ConvertAtenOp<AtenCatOp>::matchAndRewrite(
     AtenCatOp op, OpAdaptor adaptor,
     ConversionPatternRewriter &rewriter) const {
-  TypeConverter *typeConverter = this->getTypeConverter();
+  const TypeConverter *typeConverter = this->getTypeConverter();
   auto outType =
       typeConverter->convertType(op.getType()).cast<RankedTensorType>();
   int64_t rank = outType.getRank();

diff --git a/lib/Conversion/Utils/Utils.cpp b/lib/Conversion/Utils/Utils.cpp
@@ -230,7 +230,7 @@ SmallVector<Value> getAsConstantIndexValues(OpBuilder &b, Location loc,
 // convert their elements to valid target type.
 // TODO: remove this when list gets full support.
 SmallVector<Value> getTypeConvertedValues(OpBuilder &b, Location loc,
-                                          TypeConverter *converter,
+                                          const TypeConverter *converter,
                                           SmallVectorImpl<Value> &vs) {
   return llvm::to_vector<4>(llvm::map_range(vs, [&](Value v) {
     return converter->materializeTargetConversion(

diff --git a/lib/Dialect/Torch/IR/TorchOps.cpp b/lib/Dialect/Torch/IR/TorchOps.cpp
@@ -302,15 +302,13 @@ LogicalResult ClassTypeOp::verify() {
 //===----------------------------------------------------------------------===//
 
 OperandRange
-PrimLoopOp::getSuccessorEntryOperands(std::optional<unsigned int> index) {
+PrimLoopOp::getEntrySuccessorOperands(std::optional<unsigned int> index) {
   assert(index.has_value() && index.value() == 0);
   return getIterArgsInit();
 }
 
 void PrimLoopOp::getSuccessorRegions(
-    std::optional<unsigned> index, ArrayRef<Attribute> operands,
-    SmallVectorImpl<RegionSuccessor> &regions) {
-  (void)operands;
+    std::optional<unsigned> index, SmallVectorImpl<RegionSuccessor> &regions) {
 
   if (!index.has_value()) {
     regions.emplace_back(&getRegion(), getRegion().getArguments().slice(1));
@@ -381,7 +379,6 @@ void PrimIfOp::print(OpAsmPrinter &p) {
 }
 
 void PrimIfOp::getSuccessorRegions(std::optional<unsigned> index,
-                                   ArrayRef<Attribute> operands,
                                    SmallVectorImpl<RegionSuccessor> &regions) {
   // The `then` and the `else` region branch back to the parent operation.
   if (index.has_value()) {
@@ -390,9 +387,9 @@ void PrimIfOp::getSuccessorRegions(std::optional<unsigned> index,
   }
 
   // If the condition is constant, we can give a more precise answer.
-  if (auto condAttr = operands.front().dyn_cast_or_null<IntegerAttr>()) {
-    Region *executedRegion =
-        condAttr.getValue().isOne() ? &getThenRegion() : &getElseRegion();
+  bool condition;
+  if (matchPattern(getCondition(), m_TorchConstantBool(&condition))) {
+    Region *executedRegion = condition ? &getThenRegion() : &getElseRegion();
     regions.push_back(RegionSuccessor(executedRegion));
     return;
   }
@@ -2720,7 +2717,6 @@ OpFoldResult PrimMinIntOp::fold(FoldAdaptor adaptor) {
 template <typename CalculateOp>
 static void
 getSuccessorRegionsForCalculateOp(CalculateOp op, std::optional<unsigned> index,
-                                  ArrayRef<Attribute> operands,
                                   SmallVectorImpl<RegionSuccessor> &regions) {
   if (!index.has_value()) {
     // First thing the op does is branch into the calculation.
@@ -2738,19 +2734,17 @@ getSuccessorRegionsForCalculateOp(CalculateOp op, std::optional<unsigned> index,
 }
 
 void ShapeCalculateOp::getSuccessorRegions(
-    std::optional<unsigned> index, ArrayRef<Attribute> operands,
-    SmallVectorImpl<RegionSuccessor> &regions) {
-  getSuccessorRegionsForCalculateOp(*this, index, operands, regions);
+    std::optional<unsigned> index, SmallVectorImpl<RegionSuccessor> &regions) {
+  getSuccessorRegionsForCalculateOp(*this, index, regions);
 }
 
 //===----------------------------------------------------------------------===//
 // DtypeCalculateOp
 //===----------------------------------------------------------------------===//
 
 void DtypeCalculateOp::getSuccessorRegions(
-    std::optional<unsigned> index, ArrayRef<Attribute> operands,
-    SmallVectorImpl<RegionSuccessor> &regions) {
-  getSuccessorRegionsForCalculateOp(*this, index, operands, regions);
+    std::optional<unsigned> index, SmallVectorImpl<RegionSuccessor> &regions) {
+  getSuccessorRegionsForCalculateOp(*this, index, regions);
 }
 
 //===----------------------------------------------------------------------===//