diff --git a/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp b/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp
index c98d3b3481d6..0fe0a8db92e2 100644
--- a/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp
+++ b/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp
@@ -2825,12 +2825,13 @@ class DecomposeAtenAminAmaxOp : public OpRewritePattern<OpTy> {
       dims = llvm::to_vector(llvm::seq<int64_t>(0, inputTy.getSizes().size()));
     }
 
+    int64_t inputRank = inputTy.getSizes().size();
+    llvm::for_each(dims, [&](int64_t &d) { d = toPositiveDim(d, inputRank); });
+
     // For every dimension included in `dim` of the op, iterated over in
     // reverse order, we create a call to aten.max.dim.
     std::sort(dims.rbegin(), dims.rend());
     for (int64_t dimInt : dims) {
-      int64_t inputRank = inputTy.getSizes().size();
-      dimInt = toPositiveDim(dimInt, inputRank);
       if (!isValidDim(dimInt, inputRank))
         return rewriter.notifyMatchFailure(op, "dim is statically invalid");
       Value dim = Torch::ConstantIntOp::create(
diff --git a/projects/pt1/python/torch_mlir_e2e_test/test_suite/reduction.py b/projects/pt1/python/torch_mlir_e2e_test/test_suite/reduction.py
index 9b678ac02018..2a1c4b748b9f 100644
--- a/projects/pt1/python/torch_mlir_e2e_test/test_suite/reduction.py
+++ b/projects/pt1/python/torch_mlir_e2e_test/test_suite/reduction.py
@@ -1381,6 +1381,27 @@ def ReduceAmaxOutOfOrderDim_basic(module, tu: TestUtils):
 
 # ==============================================================================
 
+class ReduceAmaxOutOfOrderWithNegDim(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([-1, -1, -1, -1], torch.float32, True),
+        ]
+    )
+    def forward(self, a):
+        return torch.ops.aten.amax(a, (2, 1, -1))
+
+
+@register_test_case(module_factory=lambda: ReduceAmaxOutOfOrderWithNegDim())
+def ReduceAmaxOutOfOrderWithNegDim_basic(module, tu: TestUtils):
+    module.forward(tu.rand(3, 4, 5, 6, high=100))
+
+
+# ==============================================================================
 
 class ReduceAmaxKeepDim(torch.nn.Module):
     def __init__(self):