Move ClearBlock into OpBase and VarBase's destructor

test=develop

paddle/fluid/framework/block_desc.cc

@@ -163,20 +163,6 @@ std::vector<OpDesc *> BlockDesc::AllOps() const {
   return res;
 }
 
-void BlockDesc::Clear() {
-  // clear all ops
-  ops_.clear();
-
-  // clear all vars which are not persistable
-  for (auto it = vars_.begin(); it != vars_.end();) {
-    if (it->second->Persistable()) {
-      ++it;
-    } else {
-      vars_.erase(it++);
-    }
-  }
-}
-
 void BlockDesc::Flush() {
   for (auto &op_desc : ops_) {
     op_desc->Flush();

paddle/fluid/framework/block_desc.h

@@ -97,8 +97,6 @@ class BlockDesc {
 
   std::vector<OpDesc *> AllOps() const;
 
-  void Clear();
-
   size_t OpSize() const { return ops_.size(); }
 
   OpDesc *Op(int idx) const { return ops_.at(idx).get(); }

paddle/fluid/imperative/layer.h

@@ -126,12 +126,19 @@ class VarBase {
       : var_desc_(nullptr),
         var_(var),
         grads_(grad),
+        block_(nullptr),
         stop_gradient_(stop_gradient),
         pre_op_(nullptr),
         pre_op_out_idx_(-1) {}
 
  public:
   virtual ~VarBase() {
+    LOG(ERROR) << "remove var " << name_;
+
+    if (block_) {
+      block_->RemoveVar(name_);
+    }
+
     if (var_) {
       delete var_;
     }
@@ -189,11 +196,14 @@ class VarBase {
   framework::Variable* var_;
   VarBase* grads_;
 
+  framework::BlockDesc* block_;
+
  private:
   bool stop_gradient_;
   OpBase* pre_op_;
   std::string pre_op_out_name_;
   int pre_op_out_idx_;
+  std::string name_;
 };
 
 /* The wrapper for OpDesc which holds a OpDesc and a OpDesc of its
@@ -212,6 +222,12 @@ class OpBase {
     for (framework::OpDesc* desc : grad_op_descs_) {
       delete desc;
     }
+
+    LOG(ERROR) << "remove op " << op_desc_->Type() << " id " << trace_id_;
+
+    if (block_) {
+      block_->RemoveOp(trace_id_, trace_id_ + 1);
+    }
   }
 
   std::map<std::string, std::vector<VarBase*>> ApplyGrad();

paddle/fluid/pybind/protobuf.cc

@@ -189,8 +189,6 @@ void BindBlockDesc(pybind11::module *m) {
              return self.HasVar(name);
            },
            pybind11::return_value_policy::reference)
-      .def("_clear_block", [](pd::BlockDesc &self) { return self.Clear(); },
-           pybind11::return_value_policy::reference)
       .def("_rename_var",
            [](pd::BlockDesc &self, const pybind11::bytes &byte_name,
               const pybind11::bytes &byte_name_new) {

python/paddle/fluid/framework.py

@@ -390,6 +390,8 @@ class Variable(object):
         if _in_imperative_mode():
             # record vars in tracer rather than blocks
             self._ivar = kwargs.get("ivar", None)
+            self._ivar.block = block.desc
+            self._ivar.name = name
             if not self._ivar:
                 self._ivar = core.VarBase(stop_gradient)
             self._ivar.desc = self.desc
@@ -1200,15 +1202,6 @@ class Block(object):
         else:
             raise ValueError("Var {0} is not found recursively".format(name))
 
-    def _clear_block(self):
-        assert _in_imperative_mode()
-
-        # TODO(minqiyang): move this to Variable and Operator's __del__
-        self.desc._clear_block()
-
-        assert len(self.vars) == 0
-        assert len(self.ops) == 0
-
     def all_parameters(self):
         return list(self.iter_parameters())
 

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

@@ -142,8 +142,6 @@ class TestImperativeMnist(unittest.TestCase):
                     sgd.minimize(avg_loss)
                     mnist.clear_gradients()
 
-                    fluid.default_main_program().global_block()._clear_block()
-
                     dy_param_value = {}
                     for param in mnist.parameters():
                         dy_param_value[param.name] = param._numpy()

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

@@ -286,8 +286,6 @@ class TestImperativeResnet(unittest.TestCase):
                 optimizer.minimize(avg_loss)
                 resnet.clear_gradients()
 
-                fluid.default_main_program().global_block()._clear_block()
-
                 dy_param_value = {}
                 for param in resnet.parameters():
                     dy_param_value[param.name] = param._numpy()