diff --git a/src/ATen/native/xpu/mkl/BatchLinearAlgebra.cpp b/src/ATen/native/xpu/mkl/BatchLinearAlgebra.cpp
index e9631cf9d1..34f670c182 100644
--- a/src/ATen/native/xpu/mkl/BatchLinearAlgebra.cpp
+++ b/src/ATen/native/xpu/mkl/BatchLinearAlgebra.cpp
@@ -11,6 +11,7 @@
 #include <ATen/ops/_linalg_check_errors_native.h>
 #include <ATen/ops/empty.h>
 #include <ATen/ops/from_blob.h>
+#include <ATen/ops/zeros_like.h>
 
 #include <comm/SYCLContext.h>
 #include <comm/TensorInfo.h>
@@ -40,6 +41,16 @@ static oneapi::mkl::transpose to_blas_(TransposeType trans) {
 void error_handle(int32_t* infos, const oneapi::mkl::lapack::batch_error& be) {
   auto errs = be.exceptions();
   auto ids = be.ids();
+
+  if (!errs.size()) {
+    TORCH_WARN("Caught lapack exception:\nWhat: ", be.what(), "\nInfo: ", be.info());
+    for (auto& i : ids) {
+      TORCH_WARN("Error in matrix #", i);
+      infos[i] = 1;
+    }
+    return;
+  }
+
   for (auto& i : ids) {
     try {
       std::rethrow_exception(errs[i]);
@@ -529,8 +540,8 @@ void lu_factor_mkl(
       "linalg.lu_factor: LU without pivoting is not implemented on the XPU");
 
   // handle the info
-  info.zero_();
-  int32_t* infos_data = info.data_ptr<int32_t>();
+  Tensor info_ = at::zeros_like(info, Device(at::kCPU));
+  int32_t* infos_data = info_.data_ptr<int32_t>();
 
   // oneMKL requires Long for pivots but PyTorch provides Int
   Tensor pivots_ = at::empty(pivots.sizes(), pivots.options().dtype(kLong));
@@ -539,7 +550,8 @@ void lu_factor_mkl(
     apply_lu_xpu_<scalar_t>(LU, pivots_, infos_data);
   });
 
-  // Copy to original pivots tensor
+  // Copy to original info and pivots tensor
+  info.copy_(info_);
   pivots.copy_(pivots_);
 }