Integrate LLVM at f427890a1dcd7759240568f189df8fbbcf23669c (#4152)

Update LLVM to
llvm/llvm-project@f427890

1. Remove argument materialization in accordance with
https://discourse.llvm.org/t/rfc-merging-1-1-and-1-n-dialect-conversions/82513.
2. Make changes to `GreedyRewriteConfig` methods in accordance with
llvm/llvm-project@0c61b24.
3. Update tests affected by
llvm/llvm-project@f010725.

---------

Signed-off-by: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>

Author: vivekkhandelwal1

externals/llvm-project

@@ -916,8 +916,8 @@ Value PoolSizeCalculator<NumOfDims>::getPoolSize(
     // change, these variables used "height" and "width" (or "h" and "w")
     // in these intermediate variables instead of "Dim".
     Value IndexODim =
-        b.createOrFold<linalg::IndexOp>(location,
-                                        /*value=*/DimSizeFromSumPoolType[i]);
+        b.create<linalg::IndexOp>(location,
+                                  /*value=*/DimSizeFromSumPoolType[i]);
     Value ODim = castIndexToInt64(b, location, IndexODim);
     Value DDim = b.createOrFold<arith::ConstantOp>(
         location, b.getI64IntegerAttr(strideInts[i]));

lib/Conversion/TorchToLinalg/Pooling.cpp

@@ -154,7 +154,6 @@ struct TMTensorBufferizePass
     typeConverter.addConversion([](UnrankedTensorType type) -> Type {
       return UnrankedMemRefType::get(type.getElementType(), 0);
     });
-    typeConverter.addArgumentMaterialization(materializeToTensor);
     typeConverter.addSourceMaterialization(materializeToTensor);
     typeConverter.addTargetMaterialization([](OpBuilder &builder,
                                               BaseMemRefType type,

lib/Dialect/TMTensor/Transforms/Bufferize.cpp

@@ -231,7 +231,6 @@ static LogicalResult adjustCallingConventions(func::FuncOp func,
         return success();
       });
 
-  typeConverter.addArgumentMaterialization(materializeAsCopyTensorToType);
   typeConverter.addSourceMaterialization(materializeAsCopyTensorToType);
   typeConverter.addTargetMaterialization(materializeAsCopyTensorToType);
   patterns.add<AdjustCallingConventionForFunc>(typeConverter, context);

lib/Dialect/Torch/Transforms/AdjustCallingConventions.cpp

@@ -12115,8 +12115,8 @@ class DecomposeComplexOpsPass
     addPatternIfTargetOpIsIllegal<DecomposeAten_AssertScalarOp>(patterns);
 
     GreedyRewriteConfig config;
-    config.useTopDownTraversal = true;
-    config.maxIterations = GreedyRewriteConfig::kNoLimit;
+    config.setUseTopDownTraversal(true);
+    config.setMaxIterations(GreedyRewriteConfig::kNoLimit);
 
     if (failed(applyPatternsGreedily(getOperation(), std::move(patterns),
                                      config))) {

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -820,8 +820,8 @@ class RecomposeComplexOpsPass
     patterns.add<RecomposeMeshgridIndexingListUnpack>(context);
 
     GreedyRewriteConfig config;
-    config.useTopDownTraversal = true;
-    config.maxIterations = GreedyRewriteConfig::kNoLimit;
+    config.setUseTopDownTraversal(true);
+    config.setMaxIterations(GreedyRewriteConfig::kNoLimit);
 
     if (failed(applyPatternsGreedily(getOperation(), std::move(patterns),
                                      config))) {

lib/Dialect/Torch/Transforms/RecomposeComplexOps.cpp

@@ -261,8 +261,8 @@ class RestructureNonConstantAxesPass
     // TODO: Debug visitation order to make this more efficient.
     // A single linear scan should suffice.
     GreedyRewriteConfig config;
-    config.useTopDownTraversal = true;
-    config.maxIterations = GreedyRewriteConfig::kNoLimit;
+    config.setUseTopDownTraversal(true);
+    config.setMaxIterations(GreedyRewriteConfig::kNoLimit);
     if (failed(applyPatternsGreedily(getOperation(), std::move(patterns),
                                      config))) {
       return signalPassFailure();

lib/Dialect/Torch/Transforms/RestructureNonConstantAxes.cpp

@@ -1601,7 +1601,7 @@ class ScalarizeShapesPass : public ScalarizeShapesBase<ScalarizeShapesPass> {
     // When propagating, we need to go back and clean up aten.Tensor ops that
     // have been futher propagated. It is also necessary to add newly created
     // ops for custom folding after scalarizing a where.self op.
-    config.strictMode = GreedyRewriteStrictness::ExistingAndNewOps;
+    config.setStrictness(GreedyRewriteStrictness::ExistingAndNewOps);
     if (failed(applyOpPatternsGreedily(shapeCalculationOps.getArrayRef(),
                                        std::move(patterns), config))) {
       return signalPassFailure();

lib/Dialect/Torch/Transforms/ScalarizeShapes.cpp

@@ -211,8 +211,8 @@ class SimplifyDtypeCalculationsPass
     // TODO: Debug visitation order to make this more efficient.
     // A single linear scan should suffice.
     GreedyRewriteConfig config;
-    config.useTopDownTraversal = true;
-    config.maxIterations = GreedyRewriteConfig::kNoLimit;
+    config.setUseTopDownTraversal(true);
+    config.setMaxIterations(GreedyRewriteConfig::kNoLimit);
     if (failed(applyPatternsGreedily(getOperation(), std::move(patterns),
                                      config))) {
       return signalPassFailure();

lib/Dialect/Torch/Transforms/SimplifyDtypeCalculations.cpp

@@ -207,8 +207,8 @@ class SimplifyShapeCalculationsPass
     // TODO: Debug visitation order to make this more efficient.
     // A single linear scan should suffice.
     GreedyRewriteConfig config;
-    config.useTopDownTraversal = true;
-    config.maxIterations = GreedyRewriteConfig::kNoLimit;
+    config.setUseTopDownTraversal(true);
+    config.setMaxIterations(GreedyRewriteConfig::kNoLimit);
     if (failed(applyPatternsGreedily(getOperation(), std::move(patterns),
                                      config))) {
       return signalPassFailure();