1 files changed, 10 insertions, 7 deletions
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp
index 0779cdb9667a..db1e219b601b 100644
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp
@@ -75,13 +75,15 @@ public:
           loc, resultTy, input, reverse2, bias,
           rewriter.getDenseI64ArrayAttr(convPad),
           rewriter.getDenseI64ArrayAttr(stride),
-          rewriter.getDenseI64ArrayAttr({1, 1}), *op.getQuantizationInfo());
+          rewriter.getDenseI64ArrayAttr({1, 1}),
+          /* acc_type = */ op.getAccType(), *op.getQuantizationInfo());
     } else {
       conv2d = rewriter.create<tosa::Conv2DOp>(
           loc, resultTy, input, reverse2, bias,
           rewriter.getDenseI64ArrayAttr(convPad),
           rewriter.getDenseI64ArrayAttr(stride),
-          rewriter.getDenseI64ArrayAttr({1, 1}));
+          rewriter.getDenseI64ArrayAttr({1, 1}),
+          /* acc_type = */ op.getAccTypeAttr());
     }
 
     rewriter.replaceOp(op, conv2d);
@@ -139,7 +141,7 @@ public:
     weightPadding[5] =
         (weightWidth % stride[1]) ? (stride[1] - weightWidth % stride[1]) : 0;
     DenseElementsAttr weightPaddingAttr = DenseIntElementsAttr::get(
-        RankedTensorType::get({4, 2}, rewriter.getI32Type()), weightPadding);
+        RankedTensorType::get({8}, rewriter.getI32Type()), weightPadding);
     Value weightPaddingVal = CreateOpAndInferShape<tosa::ConstOp>(
         rewriter, loc, weightPaddingAttr.getType(), weightPaddingAttr);
 
@@ -202,7 +204,7 @@ public:
     inputPadding[5] += restridedWeightTy.getDimSize(2) - 1;
 
     DenseElementsAttr inputPaddingAttr = DenseIntElementsAttr::get(
-        RankedTensorType::get({4, 2}, rewriter.getI32Type()), inputPadding);
+        RankedTensorType::get({8}, rewriter.getI32Type()), inputPadding);
 
     Value inputPaddingVal = CreateOpAndInferShape<tosa::ConstOp>(
         rewriter, loc, inputPaddingAttr.getType(), inputPaddingAttr);
@@ -238,7 +240,7 @@ public:
                    /*pad=*/rewriter.getDenseI64ArrayAttr({0, 0, 0, 0}),
                    /*stride=*/rewriter.getDenseI64ArrayAttr({1, 1}),
                    /*dilation=*/rewriter.getDenseI64ArrayAttr({1, 1}),
-                   *op.getQuantizationInfo())
+                   /* acc_type = */ op.getAccType(), *op.getQuantizationInfo())
                    .getResult();
     } else {
       conv2d = CreateOpAndInferShape<tosa::Conv2DOp>(
@@ -246,7 +248,8 @@ public:
                    weight, zeroBias,
                    /*pad=*/rewriter.getDenseI64ArrayAttr({0, 0, 0, 0}),
                    /*stride=*/rewriter.getDenseI64ArrayAttr({1, 1}),
-                   /*dilation=*/rewriter.getDenseI64ArrayAttr({1, 1}))
+                   /*dilation=*/rewriter.getDenseI64ArrayAttr({1, 1}),
+                   /* acc_type = */ op.getAccTypeAttr())
                    .getResult();
     }
 
@@ -314,7 +317,7 @@ public:
     resultPadding[5] = resultTy.getDimSize(2) - resultPadLeft - sliceSize[2];
 
     DenseElementsAttr resultPaddingAttr = DenseIntElementsAttr::get(
-        RankedTensorType::get({4, 2}, rewriter.getI32Type()), resultPadding);
+        RankedTensorType::get({8}, rewriter.getI32Type()), resultPadding);
 
     Value resultPaddingVal = CreateOpAndInferShape<tosa::ConstOp>(
         rewriter, loc, resultPaddingAttr.getType(), resultPaddingAttr);