diff --git a/torch_xla/csrc/BUILD b/torch_xla/csrc/BUILD
index f9039bd6df73..ccae6a6e2c83 100644
--- a/torch_xla/csrc/BUILD
+++ b/torch_xla/csrc/BUILD
@@ -132,6 +132,7 @@ ptxla_cc_library(
         "//torch_xla/csrc/runtime:stablehlo_helper",
         "//torch_xla/csrc/runtime:xla_util",
         "@com_google_absl//absl/hash",
+        "@com_google_absl//absl/log:absl_check",
         "@com_google_absl//absl/log:absl_log",
         "@com_google_absl//absl/memory",
         "@com_google_absl//absl/strings",
diff --git a/torch_xla/csrc/dl_convertor.cpp b/torch_xla/csrc/dl_convertor.cpp
index 3130831b3695..9adb63747dcb 100644
--- a/torch_xla/csrc/dl_convertor.cpp
+++ b/torch_xla/csrc/dl_convertor.cpp
@@ -2,6 +2,11 @@
 
 #include <ATen/DLConvertor.h>
 
+#include <memory>
+#include <utility>
+#include <vector>
+
+#include "absl/log/absl_check.h"
 #include "absl/status/status.h"
 #include "absl/types/span.h"
 #include "torch_xla/csrc/aten_xla_bridge.h"
@@ -11,6 +16,7 @@
 #include "torch_xla/csrc/runtime/pjrt_computation_client.h"
 #include "torch_xla/csrc/runtime/runtime.h"
 #include "torch_xla/csrc/runtime/tf_logging.h"
+#include "torch_xla/csrc/status.h"
 #include "torch_xla/csrc/tensor.h"
 #include "torch_xla/csrc/tensor_util.h"
 #include "torch_xla/csrc/unwrap_data.h"
@@ -115,32 +121,30 @@ std::vector<int64_t> StridesForShape(xla::PrimitiveType element_type,
 
 // Convert an XLA tensor to a dlPack tensor.
 DLManagedTensor* toDLPack(const at::Tensor& input) {
-  XLA_CHECK(bridge::IsXlaTensor(input)) << "The input should be an XLA tensor";
+  ABSL_CHECK(bridge::IsXlaTensor(input)) << "The input should be an XLA tensor";
   std::shared_ptr<runtime::ComputationClient::Data> handle =
       get_data_handle(input);
-  XLA_CHECK(handle != nullptr)
+  ABSL_CHECK(handle != nullptr)
       << "Could not extract a valid data handle from the input tensor";
 
   std::shared_ptr<xla::PjRtBuffer> pjrt_buffer =
       runtime::GetComputationClientOrDie()->GetPjRtBuffer(handle);
-  XLA_CHECK(pjrt_buffer != nullptr) << "Could not get a valid pjrt_buffer";
+  ABSL_CHECK(pjrt_buffer != nullptr) << "Could not get a valid pjrt_buffer";
 
-  XLA_CHECK(!pjrt_buffer->IsTuple())
+  ABSL_CHECK(!pjrt_buffer->IsTuple())
       << "Unimplemented. BufferToDLPackManagedTensor is not "
          "implemented for tuple buffers.";
-  XLA_CHECK(!pjrt_buffer->has_dynamic_dimensions())
+  ABSL_CHECK(!pjrt_buffer->has_dynamic_dimensions())
       << "Unimplemented. DynamicShape is not implemented in DLPack.";
 
   auto pack = std::make_unique<DLPackTensor>();
   DLTensor& dt = pack->tensor.dl_tensor;
   {
     // AcquireExternalReference may block
-    auto external_ref = pjrt_buffer->AcquireExternalReference();
-    XLA_CHECK_OK(external_ref.status());
-    pack->external_reference = std::move(external_ref.value());
+    pack->external_reference =
+        GetValueOrThrow(pjrt_buffer->AcquireExternalReference());
     xla::PjRtFuture<> future = pjrt_buffer->GetReadyFuture();
-    absl::Status status = future.Await();
-    XLA_CHECK_OK(status);
+    MaybeThrow(future.Await());
   }
   pack->buffer_reference = pjrt_buffer;
 
@@ -299,7 +303,7 @@ absl::StatusOr<std::vector<int64_t>> StridesToLayout(
 }
 
 at::Tensor fromDLPack(DLManagedTensor* dlmt) {
-  XLA_CHECK(dlmt->dl_tensor.ndim >= 0)
+  ABSL_CHECK(dlmt->dl_tensor.ndim >= 0)
       << "Number of dimensions in DLManagedTensor must be nonnegative, got "
       << dlmt->dl_tensor.ndim;
   xla::PjRtDevice* device = DeviceForDLDevice(dlmt->dl_tensor.device).value();
@@ -325,18 +329,17 @@ at::Tensor fromDLPack(DLManagedTensor* dlmt) {
   if (dlmt->deleter) {
     on_delete_callback = [dlmt]() { dlmt->deleter(dlmt); };
   }
-  absl::StatusOr<std::unique_ptr<xla::PjRtBuffer>> pjrt_buffer =
-      device->client()->CreateViewOfDeviceBuffer(
+  std::unique_ptr<xla::PjRtBuffer> pjrt_buffer =
+      GetValueOrThrow(device->client()->CreateViewOfDeviceBuffer(
           static_cast<char*>(dlmt->dl_tensor.data) +
               dlmt->dl_tensor.byte_offset,
-          shape, *device->default_memory_space(), on_delete_callback);
-  XLA_CHECK_OK(pjrt_buffer.status()) << "Failed to create a pjrt buffer.";
-  XLA_CHECK(pjrt_buffer.value() != nullptr) << "pjrt buffer is null.";
+          shape, *device->default_memory_space(), on_delete_callback));
+  ABSL_CHECK(pjrt_buffer.get() != nullptr) << "pjrt buffer is null.";
 
   runtime::ComputationClient::DataPtr data =
       runtime::PjRtComputationClient::CreateData(
           runtime::GetComputationClientOrDie()->PjRtDeviceToString(device),
-          shape, std::move(pjrt_buffer.value()));
+          shape, std::move(pjrt_buffer));
 
   at::ScalarType tensor_type = at::toScalarType(dlmt->dl_tensor.dtype);
   XLATensorPtr xla_tensor = XLATensor::Create(data, tensor_type);
diff --git a/torch_xla/csrc/runtime/BUILD b/torch_xla/csrc/runtime/BUILD
index 7444a1977fe3..146de69924a8 100644
--- a/torch_xla/csrc/runtime/BUILD
+++ b/torch_xla/csrc/runtime/BUILD
@@ -401,6 +401,7 @@ cc_library(
     hdrs = ["tensor_source.h"],
     deps = [
         ":debug_macros",
+        "//torch_xla/csrc:status",
         "@torch//:headers",
         "@xla//xla:literal",
         "@xla//xla:shape_util",
diff --git a/torch_xla/csrc/runtime/tensor_source.h b/torch_xla/csrc/runtime/tensor_source.h
index 19665c5627a4..280bc4f83484 100644
--- a/torch_xla/csrc/runtime/tensor_source.h
+++ b/torch_xla/csrc/runtime/tensor_source.h
@@ -4,10 +4,13 @@
 #include <ATen/Tensor.h>
 #include <torch/csrc/lazy/core/metrics.h>
 
+#include <string>
+#include <utility>
 #include <vector>
 
 #include "torch_xla/csrc/dtype.h"
 #include "torch_xla/csrc/runtime/debug_macros.h"
+#include "torch_xla/csrc/status.h"
 #include "xla/literal.h"
 #include "xla/shape.h"
 #include "xla/shape_util.h"
@@ -18,7 +21,7 @@ namespace runtime {
 // Owns a contiguous block of data with the shape and layout matching `shape()`.
 class TensorSource {
  public:
-  TensorSource(std::string device) : device_(std::move(device)){};
+  TensorSource(std::string device) : device_(std::move(device)) {}
 
   virtual const void* data() const = 0;
 
@@ -28,7 +31,7 @@ class TensorSource {
 
   virtual std::vector<int64_t> byte_strides() const {
     std::vector<int64_t> byte_strides(shape().dimensions_size());
-    XLA_CHECK_OK(
+    MaybeThrow(
         xla::ShapeUtil::ByteStrides(shape(), absl::MakeSpan(byte_strides)));
     return byte_strides;
   }