fix lint

microsoft · Feb 21, 2024 · 45747fd · 45747fd
1 parent 3e50a00
commit 45747fd
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Showing 2 changed files with 21 additions and 8 deletions.
diff --git a/onnxruntime/core/framework/allocation_planner.cc b/onnxruntime/core/framework/allocation_planner.cc
@@ -529,7 +529,7 @@ class PlannerImpl {
 
     // Initialize allocation plan:
     plan_.allocation_plan.resize(num_ml_values);
-    for (size_t i = 0; i < num_ml_values; i++) plan_.allocation_plan[i].reused_buffer = static_cast<OrtValueIndex>(i);
+    for (int i = 0; static_cast<size_t>(i) < num_ml_values; i++) AllocPlan(i).reused_buffer = i;
   }
 
   bool HasExternalOutputs(const Node& node) const {
@@ -1345,7 +1345,7 @@ class PlannerImpl {
     for (size_t i = 0; i < stream_nodes_.size(); ++i) {
       // compute use count first. TODO(leca): call ComputeReuseCount() only once is enough
       ORT_RETURN_IF_ERROR(ComputeReuseCount());
-      for (size_t j = 0; j < ort_value_info_.size(); j++) Buffer(static_cast<OrtValueIndex>(j)) = static_cast<OrtValueIndex>(j);
+      for (int j = 0; static_cast<size_t>(j) < ort_value_info_.size(); j++) Buffer(j) = j;
 #if !defined(ORT_MINIMAL_BUILD) && defined(ORT_MEMORY_PROFILE)
       if (i == 0) {
         for (auto ort_value_info : ort_value_info_) {

diff --git a/onnxruntime/test/framework/allocation_planner_test.cc b/onnxruntime/test/framework/allocation_planner_test.cc
@@ -1982,8 +1982,8 @@ TEST_F(PlannerTest, TestCpuIf) {
 //                                                 |-----------> Gather
 // Shape ----------------> Reshape --> Shape ------------------> Reshape
 //                           ^                                     ^
-// InstanceNormalization ----|         InstanceNormalization ------| 
-// 
+// InstanceNormalization ----|         InstanceNormalization ------|
+//
 // Python script to create this model:
 // def CreateModelFor19480():
 //    #shape->reshape->shape->reshape, 4 gather
@@ -1998,10 +1998,23 @@ TEST_F(PlannerTest, TestCpuIf) {
 //    graphNodes.append(h.make_node('Gather', inputs=['281', 'indices2'], outputs=['output2']))
 //    graphNodes.append(h.make_node('Gather', inputs=['281', 'indices3'], outputs=['output3']))
 //    graphNodes.append(h.make_node('Gather', inputs=['281', 'indices4'], outputs=['output4']))
-//    g = h.make_graph(graphNodes, 'issue_19480', [h.make_tensor_value_info('shape_input', tp.FLOAT, ['batch', 128, None, None]), h.make_tensor_value_info('in0_input', tp.FLOAT, ['batch', 32, None]), h.make_tensor_value_info('scale0', tp.FLOAT, [32]), h.make_tensor_value_info('B0', tp.FLOAT, [32]), 
-//                                                 h.make_tensor_value_info('in1_input', tp.FLOAT, ['batch', 32, None]), h.make_tensor_value_info('scale1', tp.FLOAT, [32]), h.make_tensor_value_info('B1', tp.FLOAT, [32]), 
-//                                                 h.make_tensor_value_info('indices1', tp.INT32, []), h.make_tensor_value_info('indices2', tp.INT32, []), h.make_tensor_value_info('indices3', tp.INT32, []), h.make_tensor_value_info('indices4', tp.INT32, [])], 
-//                     [h.make_tensor_value_info('output0', tp.FLOAT, None), h.make_tensor_value_info('output1', tp.INT64, None), h.make_tensor_value_info('output2', tp.INT64, None), h.make_tensor_value_info('output3', tp.INT64, None), h.make_tensor_value_info('output4', tp.INT64, None)])
+//    g = h.make_graph(graphNodes, 'issue_19480',
+//                     [h.make_tensor_value_info('shape_input', tp.FLOAT, ['batch', 128, None, None]),
+//                      h.make_tensor_value_info('in0_input', tp.FLOAT, ['batch', 32, None]),
+//                      h.make_tensor_value_info('scale0', tp.FLOAT, [32]),
+//                      h.make_tensor_value_info('B0', tp.FLOAT, [32]),
+//                      h.make_tensor_value_info('in1_input', tp.FLOAT, ['batch', 32, None]),
+//                      h.make_tensor_value_info('scale1', tp.FLOAT, [32]),
+//                      h.make_tensor_value_info('B1', tp.FLOAT, [32]),
+//                      h.make_tensor_value_info('indices1', tp.INT32, []),
+//                      h.make_tensor_value_info('indices2', tp.INT32, []),
+//                      h.make_tensor_value_info('indices3', tp.INT32, []),
+//                      h.make_tensor_value_info('indices4', tp.INT32, [])],
+//                     [h.make_tensor_value_info('output0', tp.FLOAT, None),
+//                      h.make_tensor_value_info('output1', tp.INT64, None),
+//                      h.make_tensor_value_info('output2', tp.INT64, None),
+//                      h.make_tensor_value_info('output3', tp.INT64, None),
+//                      h.make_tensor_value_info('output4', tp.INT64, None)])
 //    model = h.make_model(g, opset_imports=[h.make_operatorsetid("", 17)], producer_name='producer_name')
 //    onnx.save(model, 'issue_19480.onnx')
 //