diff --git a/e2e_testing/torchscript/basic.py b/e2e_testing/torchscript/basic.py
index a97fa4d7e..0935c859d 100644
--- a/e2e_testing/torchscript/basic.py
+++ b/e2e_testing/torchscript/basic.py
@@ -180,3 +180,20 @@ class MaxPool2dModule(torch.nn.Module):
 @register_test_case(module_factory=lambda: MaxPool2dModule())
 def MaxPool2dModule_basic(module, tu: TestUtils):
     module.forward(tu.rand(1, 1, 20, 20) - 0.5)
+
+class TransposeIntModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([3, 4, 2], torch.float32, True),
+    ])
+    def forward(self, x):
+        return torch.transpose(x, 0, 1)
+
+
+@register_test_case(module_factory=lambda: TransposeIntModule())
+def TransposeIntModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(3, 4, 2))
diff --git a/external/torch-mlir/lib/Dialect/Torch/Transforms/MaximizeValueSemantics.cpp b/external/torch-mlir/lib/Dialect/Torch/Transforms/MaximizeValueSemantics.cpp
index 29ea3d281..417643cc9 100644
--- a/external/torch-mlir/lib/Dialect/Torch/Transforms/MaximizeValueSemantics.cpp
+++ b/external/torch-mlir/lib/Dialect/Torch/Transforms/MaximizeValueSemantics.cpp
@@ -88,7 +88,8 @@ public:
       Operation *op = workList.pop_back_val();
       if (auto copyToValueTensor = dyn_cast<CopyToValueTensorOp>(op)) {
         copyToValueTensorOps.push_back(copyToValueTensor);
-      } else if (isa<AtenUnsqueezeOp, AtenFlattenUsingIntsOp>(op)) {
+      } else if (isa<AtenUnsqueezeOp, AtenFlattenUsingIntsOp,
+                     AtenTransposeIntOp>(op)) {
         viewLikeOps.push_back(op);
         llvm::append_range(workList, op->getResult(0).getUsers());
       } else {
diff --git a/lib/Conversion/TorchToLinalg/TorchToLinalg.cpp b/lib/Conversion/TorchToLinalg/TorchToLinalg.cpp
index 0276496ae..002abf35c 100644
--- a/lib/Conversion/TorchToLinalg/TorchToLinalg.cpp
+++ b/lib/Conversion/TorchToLinalg/TorchToLinalg.cpp
@@ -1275,6 +1275,83 @@ public:
 };
 } // namespace
 
+namespace {
+class ConvertAtenTransposeIntOp
+    : public OpConversionPattern<AtenTransposeIntOp> {
+public:
+  using OpConversionPattern::OpConversionPattern;
+  LogicalResult
+  matchAndRewrite(AtenTransposeIntOp op, llvm::ArrayRef<Value> operands,
+                  ConversionPatternRewriter &rewriter) const override {
+    if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
+      return failure();
+    AtenTransposeIntOp::Adaptor adaptor(operands);
+
+    int64_t dim0;
+    if (!matchPattern(op.dim0(), m_TorchConstantInt(&dim0)))
+      return rewriter.notifyMatchFailure(op, "dim0 must be constant");
+    int64_t dim1;
+    if (!matchPattern(op.dim1(), m_TorchConstantInt(&dim1)))
+      return rewriter.notifyMatchFailure(op, "dim1 must be constant");
+
+    auto inVector = adaptor.self();
+    auto inType = inVector.getType().cast<RankedTensorType>();
+    auto inputRank = inType.getRank();
+    auto outType = getTypeConverter()
+                       ->convertType(op->getResult(0).getType())
+                       .cast<RankedTensorType>();
+    auto elementType = inType.getElementType();
+
+    if (dim0 < 0)
+      dim0 += inputRank + 1;
+    if (dim0 < 0 || dim0 >= inputRank)
+      return rewriter.notifyMatchFailure(op, "dim0 out of range");
+    if (dim1 < 0)
+      dim1 += inputRank + 1;
+    if (dim1 < 0 || dim1 >= inputRank)
+      return rewriter.notifyMatchFailure(op, "dim1 out of range");
+
+    auto loc = op.getLoc();
+
+    llvm::SmallVector<Value> outputDims;
+    for (auto i = 0; i < inputRank; i++)
+      outputDims.push_back(getDimOp(rewriter, loc, adaptor.self(), i));
+    std::swap(outputDims[dim0], outputDims[dim1]);
+
+    Value outVector =
+        rewriter.create<linalg::InitTensorOp>(loc, outputDims, elementType);
+    SmallVector<AffineExpr> idExprs;
+    SmallVector<AffineExpr> swapExprs;
+    for (auto i = 0; i < inputRank; i++)
+      idExprs.push_back(getAffineDimExpr(i, rewriter.getContext()));
+    for (auto i = 0; i < inputRank; i++) {
+      if (i == dim0) {
+        swapExprs.push_back(idExprs[dim1]);
+      } else if (i == dim1) {
+        swapExprs.push_back(idExprs[dim0]);
+      } else {
+        swapExprs.push_back(idExprs[i]);
+      }
+    }
+
+    SmallVector<AffineMap> indexingMaps = {
+        AffineMap::get(inputRank, 0, idExprs, op.getContext()),
+        AffineMap::get(inputRank, 0, swapExprs, op.getContext())};
+    SmallVector<StringRef> iteratorTypes(inputRank, "parallel");
+    auto transpose = rewriter
+                         .create<linalg::GenericOp>(
+                             loc, outVector.getType(), inVector, outVector,
+                             indexingMaps, iteratorTypes,
+                             [](OpBuilder &b, Location loc, ValueRange args) {
+                               b.create<linalg::YieldOp>(loc, args[0]);
+                             })
+                         .getResult(0);
+    rewriter.replaceOpWithNewOp<tensor::CastOp>(op, outType, transpose);
+    return success();
+  }
+};
+} // namespace
+
 // -----------------------------------------------------------------------------
 // The pass
 // -----------------------------------------------------------------------------
@@ -1325,6 +1402,8 @@ public:
     patterns.add<ConvertAtenMaxPool2dOp>(typeConverter, context);
     target.addIllegalOp<AtenSumOp>();
     patterns.add<ConvertReductionOp>(typeConverter, context);
+    target.addIllegalOp<AtenTransposeIntOp>();
+    patterns.add<ConvertAtenTransposeIntOp>(typeConverter, context);
 
     if (failed(applyPartialConversion(getOperation(), target,
                                       std::move(patterns))))