diff --git a/csrc/python_frontend/fusion_definition.cpp b/csrc/python_frontend/fusion_definition.cpp
index 7525e525522..fd3cc1f5034 100644
--- a/csrc/python_frontend/fusion_definition.cpp
+++ b/csrc/python_frontend/fusion_definition.cpp
@@ -699,66 +699,4 @@ void FusionDefinition::finalizeSegmentation() {
   segmentation_state_.reset();
 }
 
-void SegmentationState::prepareGroupOrder() {
-  NVF_ERROR(segmented_fusion_ != nullptr);
-
-  // Setup group run order
-  std::unordered_set<Val*> available_input;
-
-  // setup the order tensor dimensions are bound
-  std::copy(
-      segmented_fusion_->inputs().begin(),
-      segmented_fusion_->inputs().end(),
-      std::inserter(available_input, available_input.end()));
-
-  // The size of the tensor dimensions can be used as an input of the segments.
-  // NvFuser does not support returning scalar values. Segmentation must pass
-  // those sizes as segment arguments manually.
-  std::vector<Val*> extents = getExtents(segmented_fusion_->completeFusion());
-  std::copy(
-      extents.begin(),
-      extents.end(),
-      std::inserter(available_input, available_input.end()));
-
-  // Keep track of groups that has run
-  std::vector<bool> group_ran(segmented_fusion_->groups().size(), false);
-
-  while (!std::all_of(
-      group_ran.begin(), group_ran.end(), [](bool b) { return b; })) {
-    bool ran_any_group = false;
-
-    // Find the first segment with all inputs available to run
-    for (size_t group_i : c10::irange(segmented_fusion_->groups().size())) {
-      SegmentedGroup* group = segmented_fusion_->groups().at(group_i);
-      // short-circuit: already ran group.
-      if (group_ran.at(group_i)) {
-        continue;
-      }
-      const std::vector<Val*>& group_inputs = group->inputs();
-      bool ready_to_run = std::all_of(
-          group_inputs.begin(),
-          group_inputs.end(),
-          [&available_input](Val* val) { return available_input.count(val); });
-
-      // short-circuit: group is not ready to run.
-      if (!ready_to_run) {
-        continue;
-      }
-
-      group_run_order_.push_back(group);
-
-      // Insert graph segment output to tensor map
-      const std::vector<Val*>& group_outputs = group->outputs();
-      for (size_t group_out_i : c10::irange(group_outputs.size())) {
-        available_input.insert(group_outputs.at(group_out_i));
-      }
-      group_ran[group_i] = true;
-      ran_any_group = true;
-    }
-    NVF_ERROR(
-        ran_any_group,
-        "Failed to run all groups; An error must have occured in segmentation.");
-  }
-}
-
 } // namespace nvfuser::python_frontend
diff --git a/csrc/python_frontend/segmentation.cpp b/csrc/python_frontend/segmentation.cpp
index 26dc55da2af..95ce3c8dd7f 100644
--- a/csrc/python_frontend/segmentation.cpp
+++ b/csrc/python_frontend/segmentation.cpp
@@ -244,4 +244,68 @@ std::unordered_map<int64_t, int64_t> SegmentationState::buildSegment(
   return segment_fid_to_original_fid_map;
 }
 
+void SegmentationState::prepareGroupOrder() {
+  NVF_ERROR(segmented_fusion_ != nullptr);
+
+  // Gather initial inputs for SegmentedFusion.
+  std::unordered_set<Val*> available_input;
+  std::copy(
+      segmented_fusion_->inputs().begin(),
+      segmented_fusion_->inputs().end(),
+      std::inserter(available_input, available_input.end()));
+
+  // The size of the tensor dimensions can be used as an input of the segments.
+  // NvFuser does not support returning scalar values. Segmentation must pass
+  // those sizes as segment arguments manually.
+  std::vector<Val*> extents = getExtents(segmented_fusion_->completeFusion());
+  std::copy(
+      extents.begin(),
+      extents.end(),
+      std::inserter(available_input, available_input.end()));
+
+  // Track the run status of all SegmentedGroups in SegmentedFusion
+  std::vector<bool> group_ran(segmented_fusion_->groups().size(), false);
+
+  // While not all the SegmentedGroups are run:
+  while (!std::all_of(
+      group_ran.begin(), group_ran.end(), [](bool b) { return b; })) {
+    bool ran_any_group = false;
+
+    // Find the first segment with all inputs available to run
+    for (size_t group_i : c10::irange(segmented_fusion_->groups().size())) {
+      SegmentedGroup* group = segmented_fusion_->groups().at(group_i);
+
+      // short-circuit: Already ran this segmented group.
+      if (group_ran.at(group_i)) {
+        continue;
+      }
+
+      const std::vector<Val*>& group_inputs = group->inputs();
+      bool ready_to_run = std::all_of(
+          group_inputs.begin(),
+          group_inputs.end(),
+          [&available_input](Val* val) { return available_input.count(val); });
+
+      // short-circuit: This segmented group is not ready to run.
+      if (!ready_to_run) {
+        continue;
+      }
+
+      // Add SegmentedGroup to group_run_order_.
+      group_run_order_.push_back(group);
+
+      // Mark all outputs of SegmentedGroup as ready.
+      const std::vector<Val*>& group_outputs = group->outputs();
+      for (size_t group_out_i : c10::irange(group_outputs.size())) {
+        available_input.insert(group_outputs.at(group_out_i));
+      }
+      group_ran[group_i] = true;
+      ran_any_group = true;
+    }
+    NVF_ERROR(
+        ran_any_group,
+        "Failed to run all groups; An error must have occured in segmentation.");
+  }
+}
+
 } // namespace nvfuser::python_frontend