Use heap variables

paddle/fluid/framework/details/op_handle_base.h

@@ -16,11 +16,17 @@
 
 #include "paddle/fluid/framework/details/var_handle.h"
 #include "paddle/fluid/platform/device_context.h"
+#include "paddle/fluid/platform/macros.h"
+
 namespace paddle {
 namespace framework {
 namespace details {
 
-struct OpHandleBase {
+class OpHandleBase {
+ private:
+  DISABLE_COPY_AND_ASSIGN(OpHandleBase);
+
+ public:
   std::vector<VarHandleBase *> inputs_;
   std::vector<VarHandleBase *> outputs_;
   std::unordered_map<platform::Place, platform::DeviceContext *,
@@ -31,6 +37,8 @@ struct OpHandleBase {
   std::unordered_map<int, cudaEvent_t> events_;
 #endif
 
+  OpHandleBase() {}
+
   std::string DebugString() const;
 
   virtual std::string Name() const = 0;

paddle/fluid/framework/details/threaded_ssa_graph_executor.cc

@@ -67,7 +67,7 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
   }
 
   // Step 2. Insert FetchOps
-  std::vector<FetchOpHandle> fetch_ops;
+  std::vector<std::unique_ptr<FetchOpHandle>> fetch_ops;
   std::vector<DummyVarHandle> dummy_vars;
   FeedFetchList fetch_data(fetch_tensors.size());
 
@@ -84,9 +84,9 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
 
   for (size_t i = 0; i < fetch_tensors.size(); ++i) {
     auto &var_name = fetch_tensors[i];
-    auto &vars = fetched_vars[var_name];
-    fetch_ops.emplace_back(&fetch_data, i, &local_scopes_);
-    details::FetchOpHandle *op = &fetch_ops.back();
+    auto &vars = fetched_vars.at(var_name);
+    auto *op = new FetchOpHandle(&fetch_data, i, &local_scopes_);
+    fetch_ops.emplace_back(op);
 
     // FIXME: Use new device context
     for (auto &p : places_) {
@@ -138,7 +138,6 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
       for (auto &op : pending_ops) {
         VLOG(10) << op.first->DebugString();
       }
-
       // keep waiting the ready variables
       continue;
     }

python/paddle/fluid/tests/unittests/test_parallel_executor.py

@@ -231,6 +231,9 @@ class TestMNIST(TestParallelExecutorBase):
 class TestResnet(TestParallelExecutorBase):
     @classmethod
     def setUpClass(cls):
+        import os
+        if os.path.exists('./flowers.recordio'):
+            return
         with fluid.program_guard(fluid.Program(), fluid.Program()):
             reader = paddle.batch(flowers.train(), batch_size=4)
             feeder = fluid.DataFeeder(