diff --git a/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp b/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp
index 677ccc4f241b..cc21f2155e46 100644
--- a/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp
+++ b/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp
@@ -1059,44 +1059,44 @@ class DecomposeAtenEyeMOp : public OpRewritePattern<AtenEyeMOp> {
   LogicalResult matchAndRewrite(AtenEyeMOp op,
                                 PatternRewriter &rewriter) const override {
     Location loc = op.getLoc();
-    int64_t n;
-
-    if (!matchPattern(op.getN(), m_TorchConstantInt(&n)))
-      return rewriter.notifyMatchFailure(op,
-                                         "unimplemented: n must be constant");
-    int64_t m;
-    if (!matchPattern(op.getM(), m_TorchConstantInt(&m)))
-      return rewriter.notifyMatchFailure(op,
-                                         "unimplemented: m must be constant");
-    Value none = rewriter.create<ConstantNoneOp>(loc);
-    auto outType = dyn_cast<BaseTensorType>(op.getType());
+    auto outType = op.getType().dyn_cast<BaseTensorType>();
     if (!outType)
       return rewriter.notifyMatchFailure(
           op, "Only tensor types input are currently supported");
     if (!outType.hasDtype()) {
       return rewriter.notifyMatchFailure(op, "result should have dtype");
     }
-    if (n < 0) {
-      return rewriter.notifyMatchFailure(op, "n must be greater or equal to 0");
-    }
-    if (m < 0) {
-      return rewriter.notifyMatchFailure(op, "m must be greater or equal to 0");
-    }
-
+    Value none = rewriter.create<ConstantNoneOp>(loc);
     auto context = op.getContext();
     auto int64Dtype = getDtypeIntValueForType(
         rewriter, loc,
         rewriter.getIntegerType(/*width=*/64, /*isSigned=*/true));
     auto si64Type = IntegerType::get(context, 64, IntegerType::Signed);
-    auto arangeType = outType.getWithSizesAndDtype(llvm::ArrayRef(n), si64Type);
+
+    int64_t n = kUnknownSize;
+    int64_t m = kUnknownSize;
+    // prioritize getting shape from output shape
+    if (outType.hasSizes() && outType.getSizes().size() == 2) {
+      n = outType.getSizes().front();
+      m = outType.getSizes().back();
+    }
+    // if output shape is not available, try to get shape from input
+    if (n == kUnknownSize)
+      matchPattern(op.getN(), m_TorchConstantInt(&n));
+    if (m == kUnknownSize)
+      matchPattern(op.getM(), m_TorchConstantInt(&m));
+
+    // prepare two unsqueezed ranges that are equal on and only on the diagonal
+    auto rangeNSize = llvm::SmallVector<int64_t, 1>({n});
+    Type rangeNType = outType.getWithSizesAndDtype(rangeNSize, si64Type);
     Value rangeN = rewriter.create<AtenArangeOp>(
-        loc, arangeType, op.getN(), /*dtype=*/int64Dtype, /*layout=*/none,
+        loc, rangeNType, op.getN(), /*dtype=*/int64Dtype, /*layout=*/none,
         /*device=*/op.getDevice(), /*pin_memory=*/none);
 
-    auto arangeType1 =
-        outType.getWithSizesAndDtype(llvm::ArrayRef(m), si64Type);
+    auto rangeMSize = llvm::SmallVector<int64_t, 1>({m});
+    Type rangeMType = outType.getWithSizesAndDtype(rangeMSize, si64Type);
     Value rangeM = rewriter.create<AtenArangeOp>(
-        loc, arangeType1, op.getM(), /*dtype=*/int64Dtype, /*layout=*/none,
+        loc, rangeMType, op.getM(), /*dtype=*/int64Dtype, /*layout=*/none,
         /*device=*/none, /*pin_memory=*/none);
 
     Value constMinusOne = rewriter.create<Torch::ConstantIntOp>(
@@ -1109,7 +1109,6 @@ class DecomposeAtenEyeMOp : public OpRewritePattern<AtenEyeMOp> {
     }
     Value unsqzRangeN = *unsqzTensorInfo;
 
-    // compare unsqueezed input with boundaries
     auto eqType = ValueTensorType::get(
         context, cast<BaseTensorType>(op.getType()).getSizes(),
         IntegerType::get(context, 1));