Xilinx · mgehre-amd · Nov 7, 2025 · Nov 11, 2025 · Nov 11, 2025
diff --git a/externals/llvm-project b/externals/llvm-project
diff --git a/include/torch-mlir/Conversion/TorchToTosa/TosaLegalizeUtils.h b/include/torch-mlir/Conversion/TorchToTosa/TosaLegalizeUtils.h
@@ -49,6 +49,11 @@ bool isScale32(mlir::quant::UniformQuantizedType output_element_type);
 Value getTosaConstTensorSingleF32(PatternRewriter &rewriter, Operation *op,
                                   float val);
 
+// Create an int8_t const tosa.mul shift tensor from an int when required for
+// the given result type. Returns a null Value when no shift operand is needed.
+Value getTosaMulShiftConstTensor(PatternRewriter &rewriter, Operation *op,
+                                 Type resultType, int32_t shift);
+
 // Create a zero constant tensor of the desired type and shape.
 std::optional<Value> getZerosLikeTensor(PatternRewriter &rewriter,
                                         Operation *op, Type type);

diff --git a/lib/Conversion/TorchToTosa/TorchToTosa.cpp b/lib/Conversion/TorchToTosa/TorchToTosa.cpp
diff --git a/lib/Conversion/TorchToTosa/TosaLegalizeCommon.cpp b/lib/Conversion/TorchToTosa/TosaLegalizeCommon.cpp
@@ -119,8 +119,10 @@ tosa::MulOp createMulOpAndCast(PatternRewriter &rewriter, Operation *op,
                                int32_t shift) {
   lhs = promoteType(rewriter, lhs, outType);
   rhs = promoteType(rewriter, rhs, outType);
+  auto constShift =
+      tosa::getTosaMulShiftConstTensor(rewriter, op, outType, shift);
   return tosa::CreateOpAndInfer<tosa::MulOp>(rewriter, op->getLoc(), outType,
-                                             lhs, rhs, shift);
+                                             lhs, rhs, constShift);
 }
 
 template <>
@@ -386,10 +388,13 @@ std::optional<Value> convertGatherNdOp(PatternRewriter &rewriter, Operation *op,
   // Multiply the coefficients by the coordinates
   // %5 = "tosa.mul"(%3, %4) {shift = 0 : i32} : (tensor<8x3xi32>,
   // tensor<3xi32>) -> tensor<8x3xi32>
+  auto flattenedMulType =
+      GetTypeFromTensorShape(indicesMatrixShape, indicesType.getElementType());
+  auto mulShift =
+      tosa::getTosaMulShiftConstTensor(rewriter, op, flattenedMulType, 0);
   auto flattenedIndicesMulOp = tosa::CreateOpAndInfer<tosa::MulOp>(
-      rewriter, op->getLoc(),
-      GetTypeFromTensorShape(indicesMatrixShape, indicesType.getElementType()),
-      indicesMatrixReshapeOp, flattenedCoeffValue.value(), 0);
+      rewriter, op->getLoc(), flattenedMulType, indicesMatrixReshapeOp,
+      flattenedCoeffValue.value(), mulShift);
 
   // Sum up the products of the coefficients and coordinates
   // %6 = "tosa.reduce_sum"(%5) {axis = 1 : i64} : (tensor<8x3xi32>) ->
@@ -657,10 +662,13 @@ std::optional<Value> convertScatterNdOp(PatternRewriter &rewriter,
   // [[0, 1], [0, 2],  [0, 3]] X [4, 1] -> [[4*0, 1*1], [4*0, 1*2], [4*0, 1*3]]
   // %13 = "tosa.mul"(%11, %12) {shift = 0 : i32} : (tensor<3x2xi32>,
   // tensor<2xi32>) -> tensor<3x2xi32>
+  auto flattenedMulType =
+      GetTypeFromTensorShape(indicesMatrixShape, indicesType.getElementType());
+  auto mulShift =
+      tosa::getTosaMulShiftConstTensor(rewriter, op, flattenedMulType, 0);
   auto flattenedIndicesMulOp = tosa::CreateOpAndInfer<tosa::MulOp>(
-      rewriter, op->getLoc(),
-      GetTypeFromTensorShape(indicesMatrixShape, indicesType.getElementType()),
-      indicesMatrixReshapeOp, flattenedCoeffValue.value(), 0);
+      rewriter, op->getLoc(), flattenedMulType, indicesMatrixReshapeOp,
+      flattenedCoeffValue.value(), mulShift);
 
   // Sum up the products of the coefficients and coordinates
   // [[4*0 + 1*1], [4*0 + 1*2], [4*0 + 1*3]] = [[1],[2],[3]]
@@ -1006,8 +1014,10 @@ convertReduceMeanOp(PatternRewriter &rewriter, Operation *op,
             .failed())
       return std::nullopt;
 
+    auto mulShift =
+        tosa::getTosaMulShiftConstTensor(rewriter, op, output_type, 0);
     return CreateOpAndInfer<tosa::MulOp>(rewriter, op->getLoc(), output_type,
-                                         val.value(), div_const, 0)
+                                         val.value(), div_const, mulShift)
         .getResult();
   }
 

diff --git a/lib/Conversion/TorchToTosa/TosaLegalizeUtils.cpp b/lib/Conversion/TorchToTosa/TosaLegalizeUtils.cpp
@@ -163,6 +163,18 @@ Value getTosaConstTensorSingleF32(PatternRewriter &rewriter, Operation *op,
   return const_op.getResult();
 }
 
+Value getTosaMulShiftConstTensor(PatternRewriter &rewriter, Operation *op,
+                                 Type /*resultType*/, int32_t shift) {
+  auto shiftType = RankedTensorType::get({1}, rewriter.getI8Type());
+  auto shiftAttr = DenseElementsAttr::get<int8_t>(
+      shiftType, llvm::ArrayRef<int8_t>{static_cast<int8_t>(shift)});
+
+  auto constShift =
+      rewriter.create<tosa::ConstOp>(op->getLoc(), shiftType, shiftAttr);
+
+  return constShift.getResult();
+}
+
 // Create a zero constant tensor of the desired type and shape.
 std::optional<Value> getZerosLikeTensor(PatternRewriter &rewriter,
                                         Operation *op, Type type) {
+1 −2		llvm/lib/Target/AIE/AIECombinerHelper.cpp
+0 −19		llvm/test/CodeGen/AIE/GlobalISel/combine-trunc-to-s20.mir
+33 −23		mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h
+9 −3		mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
+10 −1		mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td
+56 −34		mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
+13 −2		mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp
+109 −4		mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+8 −1		mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeDepthwise.cpp
+15 −5		mlir/lib/Dialect/Tosa/Transforms/TosaFolders.cpp
+1 −1		mlir/lib/Dialect/Tosa/Transforms/TosaMakeBroadcastable.cpp
+14 −4		mlir/lib/Dialect/Tosa/Transforms/TosaReduceTransposes.cpp
+8 −4		mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
+29 −10		mlir/test/Dialect/Tosa/canonicalize.mlir
+28 −14		mlir/test/Dialect/Tosa/constant-mul-opt.mlir
+20 −10		mlir/test/Dialect/Tosa/constant-op-fold.mlir
+50 −3		mlir/test/Dialect/Tosa/invalid.mlir
+20 −2		mlir/test/Dialect/Tosa/ops.mlir
+2 −2		mlir/test/Dialect/Tosa/tosa-decompose-depthwise.mlir
+22 −19		mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
+10 −6		mlir/test/Dialect/Tosa/tosa-reduce-transposes.mlir