diff --git a/e2e_testing/xfail_sets.py b/e2e_testing/xfail_sets.py
index 7ab664dd6..62b40a3b5 100644
--- a/e2e_testing/xfail_sets.py
+++ b/e2e_testing/xfail_sets.py
@@ -764,6 +764,7 @@ STABLEHLO_PASS_SET = {
     "NewEmptyModuleNonDefaultIntDtype_basic",
     "NewEmptyStridedModuleDefaultDtype_basic",
     "EmptyStridedModule_basic",
+    "EmptyStridedSizeIntStrideModule_basic",
     "PermuteModule_basic",
     "PermuteNegativeIndexModule_basic",
     "ReduceSumDimIntListKeepDimNegativeDimStaticModule_basic",
@@ -1440,6 +1441,7 @@ LTC_XFAIL_SET = {
     "UniformStaticShapeModule_basic",
     "AtenEmbeddingBagStaticModule_basic",
     "EmptyStridedModule_basic",
+    "EmptyStridedSizeIntStrideModule_basic",
     "ElementwiseBitwiseAndScalarInt64Module_basic",
     "ElementwiseBitwiseAndScalarInt32Module_basic",
     "ElementwiseBitwiseAndScalarInt8Module_basic",
diff --git a/include/torch-mlir/Dialect/Torch/Utils/Utils.h b/include/torch-mlir/Dialect/Torch/Utils/Utils.h
index 9b715ec0d..3fa871b33 100644
--- a/include/torch-mlir/Dialect/Torch/Utils/Utils.h
+++ b/include/torch-mlir/Dialect/Torch/Utils/Utils.h
@@ -104,6 +104,12 @@ inline bool isAssumingStrictSymbolicShapes(OpBuilder &builder) {
   return isAssumingStrictSymbolicShapes(builder.getBlock());
 }
 
+// Helper function for AtenEmptyStrided and friends that checks if the stride
+// values are default or not. Throws a runtime assert if not.
+LogicalResult checkDefaultStrideHelper(Operation *op, PatternRewriter &rewriter,
+                                       Value opSize, Value opStride,
+                                       Location loc);
+
 } // namespace Torch
 } // namespace torch
 } // namespace mlir
diff --git a/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp b/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp
index 26a6bbc5f..985bfead5 100644
--- a/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp
+++ b/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp
@@ -332,7 +332,8 @@ public:
         rewriter.create<AtenAddIntOp>(loc, one.getType(), start, length);
 
     rewriter.replaceOpWithNewOp<AtenSliceTensorOp>(
-        op, op.getResult().getType(), op.getSelf(), /*dim=*/dim, /*start=*/start,
+        op, op.getResult().getType(), op.getSelf(), /*dim=*/dim,
+        /*start=*/start,
         /*end=*/startPlusLength, /*step=*/one);
 
     return success();
@@ -404,16 +405,15 @@ public:
 } // namespace
 
 namespace {
-class DecomposeAtenZeroOp
-    : public OpRewritePattern<AtenZeroOp> {
+class DecomposeAtenZeroOp : public OpRewritePattern<AtenZeroOp> {
 public:
   using OpRewritePattern::OpRewritePattern;
   LogicalResult matchAndRewrite(AtenZeroOp op,
                                 PatternRewriter &rewriter) const override {
     Value zero = rewriter.create<ConstantIntOp>(op.getLoc(),
                                                 rewriter.getI64IntegerAttr(0));
-    rewriter.replaceOpWithNewOp<AtenFillScalarOp>(op, op.getType(), op.getSelf(),
-                                                  zero);
+    rewriter.replaceOpWithNewOp<AtenFillScalarOp>(op, op.getType(),
+                                                  op.getSelf(), zero);
     return success();
   }
 };
@@ -1139,14 +1139,21 @@ public:
     Value constantOne =
         rewriter.create<ConstantFloatOp>(loc, rewriter.getF64FloatAttr(1.0));
     Value zeroTensor = createRank0Tensor(rewriter, loc, resType, constantZero);
-    Value maxZeroX = rewriter.create<AtenMaximumOp>(loc, resType, zeroTensor, input);
-    Value positiveOutput = rewriter.create<AtenMulScalarOp>(loc, resType, maxZeroX, scale);
-    Value minZeroX = rewriter.create<AtenMinimumOp>(loc, resType, zeroTensor, input);
-    Value scaledMinZeroX = rewriter.create<AtenMulScalarOp>(loc, resType, minZeroX, inputScale);
+    Value maxZeroX =
+        rewriter.create<AtenMaximumOp>(loc, resType, zeroTensor, input);
+    Value positiveOutput =
+        rewriter.create<AtenMulScalarOp>(loc, resType, maxZeroX, scale);
+    Value minZeroX =
+        rewriter.create<AtenMinimumOp>(loc, resType, zeroTensor, input);
+    Value scaledMinZeroX =
+        rewriter.create<AtenMulScalarOp>(loc, resType, minZeroX, inputScale);
     Value expX = rewriter.create<AtenExpOp>(loc, resType, scaledMinZeroX);
-    Value expXM1 = rewriter.create<AtenSubScalarOp>(loc, resType, expX, constantOne, constantOne);
-    Value scaledExpXM1 = rewriter.create<AtenMulScalarOp>(loc, resType, expXM1, scale);
-    Value negativeOutput = rewriter.create<AtenMulScalarOp>(loc, resType, scaledExpXM1, alpha);
+    Value expXM1 = rewriter.create<AtenSubScalarOp>(loc, resType, expX,
+                                                    constantOne, constantOne);
+    Value scaledExpXM1 =
+        rewriter.create<AtenMulScalarOp>(loc, resType, expXM1, scale);
+    Value negativeOutput =
+        rewriter.create<AtenMulScalarOp>(loc, resType, scaledExpXM1, alpha);
 
     Value eluOutput = rewriter.create<AtenAddTensorOp>(
         loc, resType, positiveOutput, negativeOutput, constantOne);
@@ -1419,8 +1426,8 @@ public:
         rewriter.create<PrimListConstructOp>(loc, listType, expandedSizes);
     Value reshapedDims =
         rewriter.create<PrimListConstructOp>(loc, listType, reshapedSizes);
-    auto reshaped = rewriter.create<AtenViewOp>(loc, unsqueezedType, op.getSelf(),
-                                                unsqueezedDims);
+    auto reshaped = rewriter.create<AtenViewOp>(loc, unsqueezedType,
+                                                op.getSelf(), unsqueezedDims);
     auto expanded = rewriter.create<AtenBroadcastToOp>(loc, expandedType,
                                                        reshaped, expandedDims);
 
@@ -1601,8 +1608,8 @@ public:
       return rewriter.notifyMatchFailure(
           op, "unimplemented: requires implicit to be false");
     }
-    rewriter.replaceOpWithNewOp<AtenBroadcastToOp>(op, op.getType(), op.getSelf(),
-                                                   op.getSize());
+    rewriter.replaceOpWithNewOp<AtenBroadcastToOp>(op, op.getType(),
+                                                   op.getSelf(), op.getSize());
     return success();
   }
 };
@@ -1621,7 +1628,8 @@ public:
       return rewriter.notifyMatchFailure(op, "result should have dtype");
     }
     Value selfTensor = createRank0Tensor(rewriter, loc, resType, op.getSelf());
-    Value otherTensor = createRank0Tensor(rewriter, loc, resType, op.getOther());
+    Value otherTensor =
+        createRank0Tensor(rewriter, loc, resType, op.getOther());
     rewriter.replaceOpWithNewOp<AtenWhereSelfOp>(op, resType, op.getCondition(),
                                                  selfTensor, otherTensor);
     return success();
@@ -1642,7 +1650,8 @@ public:
     if (!resType.hasDtype()) {
       return rewriter.notifyMatchFailure(op, "result should have dtype");
     }
-    Value otherTensor = createRank0Tensor(rewriter, loc, resType, op.getOther());
+    Value otherTensor =
+        createRank0Tensor(rewriter, loc, resType, op.getOther());
     rewriter.replaceOpWithNewOp<AtenWhereSelfOp>(op, resType, op.getCondition(),
                                                  op.getSelf(), otherTensor);
     return success();
@@ -1686,8 +1695,8 @@ public:
     }
     Value mask = op.getMask();
     Value value = createRank0Tensor(rewriter, loc, resType, op.getValue());
-    rewriter.replaceOpWithNewOp<AtenWhereSelfOp>(op, resType, mask,
-                                                 value, op.getSelf());
+    rewriter.replaceOpWithNewOp<AtenWhereSelfOp>(op, resType, mask, value,
+                                                 op.getSelf());
     return success();
   }
 };
@@ -1747,8 +1756,8 @@ public:
     Value cstTrue = rewriter.create<Torch::ConstantBoolOp>(op.getLoc(), true);
     rewriter.replaceOpWithNewOp<AtenConvolutionOp>(
         op, op->getResultTypes(), op.getInput(), op.getWeight(), op.getBias(),
-        op.getStride(), op.getPadding(), op.getDilation(), /*transposed=*/cstTrue,
-        op.getOutputPadding(), op.getGroups());
+        op.getStride(), op.getPadding(), op.getDilation(),
+        /*transposed=*/cstTrue, op.getOutputPadding(), op.getGroups());
     return success();
   }
 };
@@ -2500,9 +2509,9 @@ public:
           op, "aten.std.dim expects input tensor of floating-point type");
     }
 
-    Value varDim =
-        rewriter.create<AtenVarDimOp>(op->getLoc(), op.getType(), self,
-                                      op.getDim(), op.getUnbiased(), op.getKeepdim());
+    Value varDim = rewriter.create<AtenVarDimOp>(
+        op->getLoc(), op.getType(), self, op.getDim(), op.getUnbiased(),
+        op.getKeepdim());
     rewriter.replaceOpWithNewOp<AtenSqrtOp>(op, op.getType(), varDim);
     return success();
   }
@@ -2626,8 +2635,8 @@ public:
     Value one =
         rewriter.create<ConstantFloatOp>(loc, rewriter.getF64FloatAttr(1.0));
     Value emptyTensor = rewriter.create<AtenFullLikeOp>(
-        loc, resultType, input, zero, op.getDtype(), op.getLayout(), op.getDevice(),
-        op.getPinMemory(), op.getMemoryFormat());
+        loc, resultType, input, zero, op.getDtype(), op.getLayout(),
+        op.getDevice(), op.getPinMemory(), op.getMemoryFormat());
     rewriter.replaceOpWithNewOp<AtenUniformOp>(op, resultType, emptyTensor,
                                                /*from=*/zero, /*to=*/one,
                                                /*generator=*/none);
@@ -2829,7 +2838,8 @@ class DecomposeAtenNativeLayerNormOp
     SmallVector<Value> normalizedShapeSizesTorchInt;
     getListConstructElements(normalizedShape, normalizedShapeSizesTorchInt);
     int64_t axis = inputRank - normalizedShapeSizesTorchInt.size();
-    auto reduceDimInts = llvm::to_vector<4>(llvm::seq<int64_t>(axis, inputRank));
+    auto reduceDimInts =
+        llvm::to_vector<4>(llvm::seq<int64_t>(axis, inputRank));
     auto reducedTy = op.getResult(1).getType();
     auto sizeListType = ListType::get(IntType::get(context));
 
@@ -2905,8 +2915,8 @@ public:
     Value sizeList =
         rewriter.create<AtenSizeOp>(op.getLoc(), sizeListType, op.getSelf());
     rewriter.replaceOpWithNewOp<AtenEmptyMemoryFormatOp>(
-        op, op.getType(), sizeList, op.getDtype(), op.getLayout(), op.getDevice(),
-        op.getPinMemory(), op.getMemoryFormat());
+        op, op.getType(), sizeList, op.getDtype(), op.getLayout(),
+        op.getDevice(), op.getPinMemory(), op.getMemoryFormat());
     return success();
   }
 };
@@ -2927,8 +2937,8 @@ class DecomposeAtenArangeOp : public OpRewritePattern<AtenArangeOp> {
     step = rewriter.create<Torch::ConstantIntOp>(loc,
                                                  rewriter.getI64IntegerAttr(1));
     rewriter.replaceOpWithNewOp<AtenArangeStartStepOp>(
-        op, op.getType(), start, op.getEnd(), step, op.getDtype(), op.getLayout(),
-        op.getDevice(), op.getPinMemory());
+        op, op.getType(), start, op.getEnd(), step, op.getDtype(),
+        op.getLayout(), op.getDevice(), op.getPinMemory());
     return success();
   }
 };
@@ -2947,8 +2957,8 @@ class DecomposeAtenArangeStartOp : public OpRewritePattern<AtenArangeStartOp> {
     step = rewriter.create<Torch::ConstantIntOp>(loc,
                                                  rewriter.getI64IntegerAttr(1));
     rewriter.replaceOpWithNewOp<AtenArangeStartStepOp>(
-        op, op.getType(), op.getStart(), op.getEnd(), step, op.getDtype(), op.getLayout(),
-        op.getDevice(), op.getPinMemory());
+        op, op.getType(), op.getStart(), op.getEnd(), step, op.getDtype(),
+        op.getLayout(), op.getDevice(), op.getPinMemory());
     return success();
   }
 };
@@ -3035,7 +3045,8 @@ class DecomposeAtenNativeBatchNormOp
         loc, ListType::get(IntType::get(context)), runningStatsShape);
 
     SmallVector<int64_t> runningStatsShapeInt(inputRank, 1);
-    runningStatsShapeInt[1] = runningMean.getType().cast<BaseTensorType>().getSizes()[0];
+    runningStatsShapeInt[1] =
+        runningMean.getType().cast<BaseTensorType>().getSizes()[0];
     Type dtype = input.getType().cast<ValueTensorType>().getOptionalDtype();
     Type reshapeType = ValueTensorType::get(
         context, llvm::ArrayRef(runningStatsShapeInt), dtype);
@@ -3320,11 +3331,10 @@ public:
           getDtypeIntValueForType(rewriter, op.getLoc(), tensorType.getDtype());
     }
     rewriter.replaceOpWithNewOp<AtenFullOp>(
-        op, op.getType(), op.getSize(), op.getFillValue(), dtype, op.getLayout(), op.getDevice(),
-        op.getPinMemory());
+        op, op.getType(), op.getSize(), op.getFillValue(), dtype,
+        op.getLayout(), op.getDevice(), op.getPinMemory());
 
     return success();
-
   }
 };
 } // namespace
@@ -3338,7 +3348,8 @@ public:
                                 PatternRewriter &rewriter) const override {
     Value cstFalse = rewriter.create<Torch::ConstantBoolOp>(op.getLoc(), false);
     rewriter.replaceOpWithNewOp<Aten_IndexPutImplOp>(
-        op, op.getType(), op.getSelf(), op.getIndices(), op.getValues(), op.getAccumulate(),
+        op, op.getType(), op.getSelf(), op.getIndices(), op.getValues(),
+        op.getAccumulate(),
         /*unsafe=*/cstFalse);
     return success();
   }
@@ -3355,8 +3366,8 @@ class DecomposeAtenExpandAsOp : public OpRewritePattern<AtenExpandAsOp> {
         Torch::ListType::get(Torch::IntType::get(op.getContext()));
     Value sizeList =
         rewriter.create<AtenSizeOp>(op.getLoc(), sizeListType, op.getOther());
-    rewriter.replaceOpWithNewOp<AtenBroadcastToOp>(op, op.getType(), op.getSelf(),
-                                                   sizeList);
+    rewriter.replaceOpWithNewOp<AtenBroadcastToOp>(op, op.getType(),
+                                                   op.getSelf(), sizeList);
     return success();
   }
 };
@@ -3378,8 +3389,9 @@ public:
     Value zero = getConstantWithGivenDtypeAndValue(rewriter, op.getLoc(), 0.0,
                                                    resultDtype);
     Value emptyTensor = rewriter.create<AtenFullLikeOp>(
-        op.getLoc(), op.getType(), op.getSelf(), zero, op.getDtype(), op.getLayout(),
-        op.getDevice(), op.getPinMemory(), op.getMemoryFormat());
+        op.getLoc(), op.getType(), op.getSelf(), zero, op.getDtype(),
+        op.getLayout(), op.getDevice(), op.getPinMemory(),
+        op.getMemoryFormat());
     rewriter.replaceOpWithNewOp<AtenCopyOp>(op, op.getType(), emptyTensor,
                                             op.getSelf(), op.getNonBlocking());
     return success();
@@ -3450,7 +3462,8 @@ public:
                                 PatternRewriter &rewriter) const override {
     Value cstFalse = rewriter.create<Torch::ConstantBoolOp>(op.getLoc(), false);
     rewriter.replaceOpWithNewOp<Aten_IndexPutImplOp>(
-        op, op.getType(), op.getSelf(), op.getIndices(), op.getValues(), op.getAccumulate(),
+        op, op.getType(), op.getSelf(), op.getIndices(), op.getValues(),
+        op.getAccumulate(),
         /*unsafe=*/cstFalse);
     return success();
   }
@@ -3539,9 +3552,9 @@ public:
             op, "unimplemented: layout is expected to be strided");
     }
 
-    rewriter.replaceOpWithNewOp<AtenToDtypeOp>(op, op.getType(), op.getSelf(),
-                                               op.getDtype(), op.getNonBlocking(),
-                                               op.getCopy(), op.getMemoryFormat());
+    rewriter.replaceOpWithNewOp<AtenToDtypeOp>(
+        op, op.getType(), op.getSelf(), op.getDtype(), op.getNonBlocking(),
+        op.getCopy(), op.getMemoryFormat());
     return success();
   }
 };
@@ -3557,9 +3570,9 @@ public:
 
     // Device information isn't relevant to torch-mlir, so we can drop that info
     // here.
-    rewriter.replaceOpWithNewOp<AtenToDtypeOp>(op, op.getType(), op.getSelf(),
-                                               op.getDtype(), op.getNonBlocking(),
-                                               op.getCopy(), op.getMemoryFormat());
+    rewriter.replaceOpWithNewOp<AtenToDtypeOp>(
+        op, op.getType(), op.getSelf(), op.getDtype(), op.getNonBlocking(),
+        op.getCopy(), op.getMemoryFormat());
 
     return success();
   }
@@ -3798,8 +3811,8 @@ class DecomposeAtenBaddbmmOp : public OpRewritePattern<AtenBaddbmmOp> {
   LogicalResult matchAndRewrite(AtenBaddbmmOp op,
                                 PatternRewriter &rewriter) const override {
     Location loc = op.getLoc();
-    Value bmm =
-        rewriter.create<AtenBmmOp>(loc, op.getType(), op.getBatch1(), op.getBatch2());
+    Value bmm = rewriter.create<AtenBmmOp>(loc, op.getType(), op.getBatch1(),
+                                           op.getBatch2());
     Value alphaTimesBmm =
         rewriter.create<AtenMulScalarOp>(loc, op.getType(), bmm, op.getAlpha());
     Value input = op.getSelf();
@@ -4160,7 +4173,8 @@ public:
                                   resultType.getOptionalDtype())
             .cast<BaseTensorType>();
 
-    Value sub = createTensorSub(rewriter, loc, subType, op.getSelf(), op.getTarget());
+    Value sub =
+        createTensorSub(rewriter, loc, subType, op.getSelf(), op.getTarget());
     Value result = rewriter.create<AtenSquareOp>(loc, subType, sub);
     if (reductionType == torch_upstream::Reduction::None) {
       rewriter.replaceOp(op, result);
@@ -4242,7 +4256,8 @@ public:
                                   rewriter.getF32Type())
             .cast<BaseTensorType>();
     Value emptyTensor = rewriter.create<AtenEmptyMemoryFormatOp>(
-        loc, floatResultType, op.getSize(), /*dtype=*/none, /*layout=*/op.getLayout(),
+        loc, floatResultType, op.getSize(), /*dtype=*/none,
+        /*layout=*/op.getLayout(),
         /*device=*/op.getDevice(), /*pinMemory=*/op.getPinMemory(),
         /*memoryFormat=*/none);
 
@@ -4272,11 +4287,11 @@ public:
 
     Value low = rewriter.create<Torch::ConstantIntOp>(
         loc, rewriter.getI64IntegerAttr(0));
-    
+
     rewriter.replaceOpWithNewOp<AtenRandintLowOp>(
-        op, resultType, low, op.getHigh(), op.getSize(), op.getDtype(), op.getLayout(),
-        op.getDevice(), op.getPinMemory());
-    
+        op, resultType, low, op.getHigh(), op.getSize(), op.getDtype(),
+        op.getLayout(), op.getDevice(), op.getPinMemory());
+
     return success();
   }
 };
@@ -4294,10 +4309,11 @@ public:
     Location loc = op.getLoc();
     Value noneVal = rewriter.create<ConstantNoneOp>(loc);
     Value var = rewriter.create<AtenVarCorrectionOp>(
-        loc, op.getType(0), op.getSelf(), op.getDim(), op.getCorrection(), op.getKeepdim());
-    Value mean =
-        rewriter.create<AtenMeanDimOp>(loc, op.getType(0), op.getSelf(), op.getDim(),
-                                       op.getKeepdim(), /*dtype=*/noneVal);
+        loc, op.getType(0), op.getSelf(), op.getDim(), op.getCorrection(),
+        op.getKeepdim());
+    Value mean = rewriter.create<AtenMeanDimOp>(
+        loc, op.getType(0), op.getSelf(), op.getDim(), op.getKeepdim(),
+        /*dtype=*/noneVal);
     rewriter.replaceOp(op, {var, mean});
     return success();
   }
@@ -4394,14 +4410,16 @@ public:
         /*device=*/op.getDevice(), /*pin_memory=*/op.getPinMemory(),
         /*memory_format=*/none);
 
-    Value uOne = rewriter.create<AtenUniformOp>(loc, resultType, emptyTensorA,
-                                                /*from=*/low,
-                                                /*to=*/high,
-                                                /*generator=*/op.getGenerator());
-    Value uTwo = rewriter.create<AtenUniformOp>(loc, resultType, emptyTensorB,
-                                                /*from=*/low,
-                                                /*to=*/high,
-                                                /*generator=*/op.getGenerator());
+    Value uOne =
+        rewriter.create<AtenUniformOp>(loc, resultType, emptyTensorA,
+                                       /*from=*/low,
+                                       /*to=*/high,
+                                       /*generator=*/op.getGenerator());
+    Value uTwo =
+        rewriter.create<AtenUniformOp>(loc, resultType, emptyTensorB,
+                                       /*from=*/low,
+                                       /*to=*/high,
+                                       /*generator=*/op.getGenerator());
 
     Value logUOne = rewriter.create<AtenLogOp>(loc, resultType, uOne);
     Value minusTwoLogUOne =
@@ -4526,37 +4544,18 @@ public:
   using OpRewritePattern::OpRewritePattern;
   LogicalResult matchAndRewrite(AtenNewEmptyStridedOp op,
                                 PatternRewriter &rewriter) const override {
-    SmallVector<int64_t> sizeListInts, strideListInts;
-    if (!matchPattern(op.getSize(), m_TorchListOfConstantInts(sizeListInts)))
-      return rewriter.notifyMatchFailure(
-          op, "all size list elements must be constant ints");
-    if (!matchPattern(op.getStride(),
-                      m_TorchListOfConstantInts(strideListInts)))
-      return rewriter.notifyMatchFailure(
-          op, "all stride list elements must be constant ints");
+    Location loc = op.getLoc();
+    Value opSize = op.getSize();
+    Value opStride = op.getStride();
 
-    // We only support the cases with default stride values.
-    // For ex: aten.new_empty_strided(self, size=[2, 3, 4], stride=[12, 4, 1])
-    // Here the stride[0] == size[1] * size[2], stride[1] == size[2], and
-    // stride[2] == 1.
-    bool isDefaultStride = true;
-    for (unsigned i = 0; i < strideListInts.size(); i++) {
-      int64_t defaultStride = 1;
-      for (unsigned j = i + 1; j < sizeListInts.size(); j++)
-        defaultStride *= sizeListInts[j];
-      if (defaultStride != strideListInts[i]) {
-        isDefaultStride = false;
-        break;
-      }
-    }
-
-    if (!isDefaultStride)
+    if (failed(checkDefaultStrideHelper(op, rewriter, opSize, opStride, loc)))
       return rewriter.notifyMatchFailure(
-          op, "only default strides supported for new_empty_strided op");
+          op, "Unable to determine if stride is default");
 
     rewriter.replaceOpWithNewOp<AtenNewEmptyOp>(
         op, op.getType(), op.getSelf(), op.getSize(), op.getDtype(),
         op.getLayout(), op.getDevice(), op.getPinMemory());
+
     return success();
   }
 };
@@ -4569,42 +4568,20 @@ public:
   using OpRewritePattern::OpRewritePattern;
   LogicalResult matchAndRewrite(AtenEmptyStridedOp op,
                                 PatternRewriter &rewriter) const override {
-    SmallVector<int64_t> sizeListInts, strideListInts;
-    if (!matchPattern(op.getSize(), m_TorchListOfConstantInts(sizeListInts)))
-      return rewriter.notifyMatchFailure(
-          op, "all size list elements must be constant ints");
-    if (!matchPattern(op.getStride(),
-                      m_TorchListOfConstantInts(strideListInts)))
-      return rewriter.notifyMatchFailure(
-          op, "all stride list elements must be constant ints");
+    Location loc = op.getLoc();
+    Value opSize = op.getSize();
+    Value opStride = op.getStride();
 
-    // We only support the cases with default stride values.
-    // For ex: aten.new_empty_strided(self, size=[2, 3, 4], stride=[12, 4, 1])
-    // Here the stride[0] == size[1] * size[2], stride[1] == size[2], and
-    // stride[2] == 1.
-    bool isDefaultStride = true;
-    for (unsigned i = 0; i < strideListInts.size(); i++) {
-      int64_t defaultStride = 1;
-      for (unsigned j = i + 1; j < sizeListInts.size(); j++)
-        defaultStride *= sizeListInts[j];
-      if (defaultStride != strideListInts[i]) {
-        isDefaultStride = false;
-        break;
-      }
-    }
-    if (!isDefaultStride)
+    if (failed(checkDefaultStrideHelper(op, rewriter, opSize, opStride, loc)))
       return rewriter.notifyMatchFailure(
-          op, "only default strides supported for new_empty_strided op");
+          op, "Unable to determine if stride is default");
 
     Value noneVal = rewriter.create<ConstantNoneOp>(op.getLoc());
 
     rewriter.replaceOpWithNewOp<AtenEmptyMemoryFormatOp>(
-        op, op.getType(), op.getSize(), op.getDtype(), op.getLayout(), op.getDevice(),
-        op.getPinMemory(), /*memoryFormat=*/noneVal);
-
+        op, op.getType(), op.getSize(), op.getDtype(), op.getLayout(),
+        op.getDevice(), op.getPinMemory(), /*memoryFormat=*/noneVal);
     return success();
-
-
   }
 };
 } // namespace
@@ -4993,8 +4970,8 @@ public:
     auto selectGreater =
         rewriter.create<AtenWhereScalarOp>(loc, outType, greater, one, zero);
 
-    rewriter.replaceOpWithNewOp<AtenWhereScalarOtherOp>(op, outType, greaterEqual,
-                                                   selectGreater, minusOne);
+    rewriter.replaceOpWithNewOp<AtenWhereScalarOtherOp>(
+        op, outType, greaterEqual, selectGreater, minusOne);
     return success();
   }
 };
@@ -5407,7 +5384,8 @@ public:
     addPatternIfTargetOpIsIllegal<DeomposeAtenNativeDropoutOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenNewEmptyOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenIndexPutHackedTwinOp>(patterns);
-    addPatternIfTargetOpIsIllegal<DecomposeAten_UnsafeIndexPutHackedTwinOp>(patterns);
+    addPatternIfTargetOpIsIllegal<DecomposeAten_UnsafeIndexPutHackedTwinOp>(
+        patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenPadOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenToDtypeLayoutOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenToDeviceOp>(patterns);
diff --git a/lib/Dialect/Torch/Utils/Utils.cpp b/lib/Dialect/Torch/Utils/Utils.cpp
index ddc95bd4b..14a264ada 100644
--- a/lib/Dialect/Torch/Utils/Utils.cpp
+++ b/lib/Dialect/Torch/Utils/Utils.cpp
@@ -333,3 +333,61 @@ bool Torch::isAssumingStrictSymbolicShapes(Block *block) {
   }
   return false;
 }
+
+LogicalResult Torch::checkDefaultStrideHelper(Operation *op,
+                                              PatternRewriter &rewriter,
+                                              Value opSize, Value opStride,
+                                              Location loc) {
+
+  SmallVector<int64_t> sizeListInts, strideListInts;
+  if (matchPattern(opSize, m_TorchListOfConstantInts(sizeListInts)) &&
+      matchPattern(opStride, m_TorchListOfConstantInts(strideListInts))) {
+
+    // We only support the cases with default stride values.
+    // For ex: aten.new_empty_strided(self, size=[2, 3, 4], stride=[12, 4, 1])
+    // Here the stride[0] == size[1] * size[2], stride[1] == size[2], and
+    // stride[2] == 1.
+    bool isDefaultStride = true;
+    for (unsigned i = 0; i < strideListInts.size(); i++) {
+      int64_t defaultStride = 1;
+      for (unsigned j = i + 1; j < sizeListInts.size(); j++)
+        defaultStride *= sizeListInts[j];
+      if (defaultStride != strideListInts[i]) {
+        isDefaultStride = false;
+        break;
+      }
+    }
+    if (!isDefaultStride)
+      return rewriter.notifyMatchFailure(
+          op, "only default strides supported for empty_strided op");
+
+    return success();
+
+  } else {
+    SmallVector<Value> sizeListValues;
+    if (!getListConstructElements(opSize, sizeListValues))
+      return rewriter.notifyMatchFailure(op, "couldn't get size list values");
+    SmallVector<Value> strideListValues;
+    if (!getListConstructElements(opStride, strideListValues))
+      return rewriter.notifyMatchFailure(op,
+                                         "couldn't get stride list values.");
+    SmallVector<Value> boolVector;
+    for (unsigned i = 0; i < strideListValues.size(); i++) {
+      Value defaultStride = rewriter.createOrFold<Torch::ConstantIntOp>(
+          loc, rewriter.getI64IntegerAttr(1));
+      for (unsigned j = i + 1; j < sizeListValues.size(); j++) {
+        defaultStride = rewriter.createOrFold<Torch::AtenMulIntOp>(
+            loc, defaultStride, sizeListValues[j]);
+      }
+      boolVector.push_back(rewriter.createOrFold<Torch::AtenEqIntOp>(
+          loc, defaultStride, strideListValues[i]));
+    }
+    Value allBoolOpList = rewriter.createOrFold<PrimListConstructOp>(
+        loc, Torch::ListType::get(rewriter.getType<Torch::BoolType>()),
+        boolVector);
+    Value cmp = rewriter.createOrFold<Torch::AtenAllBoolOp>(loc, allBoolOpList);
+    rewriter.createOrFold<Torch::RuntimeAssertOp>(
+        loc, cmp, "not all strides are default");
+    return success();
+  }
+}
diff --git a/python/torch_mlir_e2e_test/test_suite/constant_alloc.py b/python/torch_mlir_e2e_test/test_suite/constant_alloc.py
index 27cf2eb4a..675e32757 100644
--- a/python/torch_mlir_e2e_test/test_suite/constant_alloc.py
+++ b/python/torch_mlir_e2e_test/test_suite/constant_alloc.py
@@ -1629,7 +1629,6 @@ class NewEmptyStridedModuleDefaultDtype(torch.nn.Module):
 def NewEmptyStridedModuleDefaultDtype_basic(module, tu: TestUtils):
     module.forward(tu.rand(2, 3, 4))
 
-
 # ==============================================================================
 
 
@@ -1651,4 +1650,27 @@ class EmptyStridedModule(torch.nn.Module):
 
 @register_test_case(module_factory=lambda: EmptyStridedModule())
 def EmptyStridedModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(4, 3, 4))
+
+# ==============================================================================
+
+
+class EmptyStridedSizeIntStrideModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([2, 3, -1], torch.float32, True),
+    ])
+    def forward(self, a):
+        x = torch.ops.aten.empty_strided(a.size(), stride=[12, a.size(2), 1])
+        y = x.copy_(a)
+        return y
+
+
+@register_test_case(module_factory=lambda: EmptyStridedSizeIntStrideModule())
+def EmptyStridedSizeIntStrideModule_basic(module, tu: TestUtils):
     module.forward(tu.rand(2, 3, 4))