Bump LLVM to c27444ab4976dd9ff131212f87463f9945ab28d7

.gitmodules

@@ -2,5 +2,5 @@
 
 [submodule "llvm-project"]
 	path = llvm-project
-	url = https://github.com/Xilinx/llvm-project.git
+	url = https://github.com/Xilinx/llvm-aie.git
 	branch = feature/fused-ops

lib/Conversion/TosaToXTenNN.cpp

@@ -101,8 +101,10 @@ m_ConstantFloatLog2(IntegerAttr::ValueType *bindValue) { // NOLINT
 ///\return true shape does not change from input to output
 ///\return false shape does change from input to output
 bool sameInputAndOutputShape(mlir::Operation *operation) {
-  assert(operation->getNumOperands() == 2 && operation->getNumResults() == 1 &&
-         "expected operation with 2 inputs and one output.");
+  assert(
+      (operation->getNumOperands() == 2 || operation->getNumOperands() == 3) &&
+      operation->getNumResults() == 1 &&
+      "expected operation with 2 inputs and one output.");
   return operation->getOperand(0).getType() ==
          operation->getResult(0).getType();
 }
@@ -179,7 +181,8 @@ class FoldMulsToQDQOps : public OpRewritePattern<tosa::MulOp> {
     APInt quantizeShift(32, 0, true);
     auto isQDQPattern = m_Op<amd::xten_nn::DequantizeOp>(
         m_Op<amd::xten_nn::QuantizeOp>(m_Op<tosa::MulOp>(
-            matchers::m_Any(), m_ConstantFloatLog2(&quantizeShift))));
+            matchers::m_Any(), m_ConstantFloatLog2(&quantizeShift),
+            matchers::m_Any())));
     if (!dequantizeOp || !isQDQPattern.match(dequantizeOp)) {
       return rewriter.notifyMatchFailure(dequantizeMulOp->getLoc(),
                                          "expected mul->q->dq->mul pattern.");
@@ -261,7 +264,8 @@ class MoveScalarTensorToRHSOfMul : public OpRewritePattern<tosa::MulOp> {
 
   LogicalResult matchAndRewrite(tosa::MulOp mulOp,
                                 PatternRewriter &rewriter) const override {
-    if (!m_Op<tosa::MulOp>(m_Constant(), m_Constant()).match(mulOp)) {
+    if (!m_Op<tosa::MulOp>(m_Constant(), m_Constant(), m_Constant())
+             .match(mulOp)) {
       return rewriter.notifyMatchFailure(
           mulOp.getLoc(),
           "only reorganize operands on muls with two constants");

lib/Conversion/XTenNNToTosa.cpp

@@ -66,6 +66,15 @@ APFloat convertF32AttrToFloatTy(FloatAttr attr, Type typeToConvertTo) {
                     llvm::RoundingMode::NearestTiesToEven, &losesInfo);
   return scale;
 }
+
+Value getZeroShift(PatternRewriter &rewriter, Location loc) {
+  auto shiftValueType = RankedTensorType::get({1}, rewriter.getI8Type());
+  auto shiftValueAttr = DenseElementsAttr::get(shiftValueType, {int8_t(0)});
+
+  auto shiftValue =
+      rewriter.create<tosa::ConstOp>(loc, shiftValueType, shiftValueAttr);
+  return shiftValue;
+}
 } // namespace
 
 class QuantizeOp : public OpRewritePattern<amd::xten_nn::QuantizeOp> {
@@ -96,7 +105,7 @@ class QuantizeOp : public OpRewritePattern<amd::xten_nn::QuantizeOp> {
 
     auto mulOp = rewriter.create<tosa::MulOp>(
         quantizeOp.getLoc(), inputType, quantizeOp->getOperand(0),
-        constOp->getResult(0), rewriter.getI8IntegerAttr(0));
+        constOp->getResult(0), getZeroShift(rewriter, quantizeOp.getLoc()));
 
     mlir::Value castFrom = mulOp->getResult(0);
     if (!quantizeOp.getZeroPoint().isZero()) {
@@ -189,7 +198,7 @@ class DequantizeOp : public OpRewritePattern<amd::xten_nn::DequantizeOp> {
     rewriter.replaceOpWithNewOp<tosa::MulOp>(
         dequantizeOp, dequantizeOp->getResult(0).getType(),
         zeroPointSubOp->getResult(0), constOp->getResult(0),
-        rewriter.getI8IntegerAttr(0));
+        getZeroShift(rewriter, dequantizeOp.getLoc()));
     return success();
   }
 };