Add debug info

paddle/fluid/imperative/layer.cc

@@ -175,7 +175,7 @@ std::unique_ptr<VarBase> VarBase::NewVarBase(const platform::Place& dst_place,
   PADDLE_ENFORCE(var_->IsInitialized(),
                  "Variable must be initialized when getting numpy tensor");
 
-  std::unique_ptr<VarBase> new_var(new VarBase());
+  std::unique_ptr<VarBase> new_var(new VarBase("NewVarBase"));
   framework::LoDTensor* tensor =
       new_var->var_->GetMutable<framework::LoDTensor>();
   tensor->Resize(var_->Get<framework::LoDTensor>().dims());
@@ -303,7 +303,7 @@ std::vector<VarBase*> PyLayer::Apply(int func_id,
   std::vector<Variable*> outvars = CallPythonFunc(py_funcs_[func_id], invars);
   std::vector<VarBase*> ret;
   for (Variable* v : outvars) {
-    ret.push_back(new VarBase(v, new VarBase(true)));
+    ret.push_back(new VarBase(v, new VarBase("PYLAYER_XGRAD", true), ""));
   }
   return ret;
 }

paddle/fluid/imperative/layer.h

@@ -103,26 +103,30 @@ class OpBase;
  */
 class VarBase {
  public:
-  VarBase() : VarBase(new framework::Variable(), new VarBase(true)) {}
+  VarBase(std::string name) : VarBase(new framework::Variable(), new VarBase(name + "XGRAD", true), name) {}
 
   // Owns `var` and `grad`
-  VarBase(framework::Variable* var, VarBase* grad)
+  VarBase(framework::Variable* var, VarBase* grad, std::string name)
       : var_desc_(nullptr),
         var_(var),
         grads_(grad),
         stop_gradient_(false),
         pre_op_(nullptr),
-        pre_op_out_idx_(-1) {}
+        pre_op_out_idx_(-1),
+        name_(name) { LOG(ERROR) << "create " << name; }
 
-  explicit VarBase(bool stop_gradient)
+  explicit VarBase(std::string name, bool stop_gradient)
       : var_desc_(nullptr),
         var_(new framework::Variable()),
-        grads_(stop_gradient ? nullptr : new VarBase(true)),
+        grads_(stop_gradient ? nullptr : new VarBase(name + "XGRAD", true)),
         stop_gradient_(stop_gradient),
         pre_op_(nullptr),
-        pre_op_out_idx_(-1) {}
+        pre_op_out_idx_(-1),
+        name_(name) { LOG(ERROR) << "create " << name;  }
 
   virtual ~VarBase() {
+    LOG(ERROR) << "delete " << name_;
+
     if (var_) {
       delete var_;
     }
@@ -183,6 +187,7 @@ class VarBase {
   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

paddle/fluid/pybind/pybind.cc

@@ -137,7 +137,7 @@ PYBIND11_MODULE(core, m) {
 
   py::class_<imperative::VarBase>(m, "VarBase", R"DOC()DOC")
       // .def(py::init<>())
-      .def(py::init<bool>(), py::arg("stop_gradient") = false)
+      .def(py::init<std::string, bool>(), py::arg("stop_gradient") = false, py::arg("name") = "")
       .def("_run_backward",
            [](imperative::VarBase &self) { self.RunBackward(); })
       .def("_grad_name", &imperative::VarBase::GradName)

python/paddle/fluid/framework.py

@@ -306,6 +306,10 @@ class Variable(object):
 
         if name is None:
             name = unique_name.generate('_generated_var')
+        #  print("create var", name)
+        #  import sys
+        #  sys.stdout.flush()
+
         is_new_var = False
         name = cpt.to_text(name)
         self.desc = self.block.desc.find_var(cpt.to_bytes(name))
@@ -383,7 +387,7 @@ class Variable(object):
         if _in_imperative_mode():
             self._ivar = kwargs.get("ivar", None)
             if not self._ivar:
-                self._ivar = core.VarBase()
+                self._ivar = core.VarBase(name, stop_gradient)
             self._ivar.desc = self.desc
             self._ivar.stop_gradient = stop_gradient
 
@@ -1269,6 +1273,7 @@ class Block(object):
         return var
 
     def _remove_var(self, name):
+        if not _in_imperative_mode():
             self._sync_with_cpp()
         self.desc._remove_var(cpt.to_bytes(name))
         del self.vars[name]
@@ -1353,6 +1358,7 @@ class Block(object):
         Returns:
             None
         """
+        if not _in_imperative_mode():
             self._sync_with_cpp()
         self.desc._remove_op(index, index + 1)
         del self.ops[index]

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

@@ -101,7 +101,7 @@ class MNIST(fluid.imperative.Layer):
 class TestImperativeMnist(unittest.TestCase):
     def test_mnist_float32(self):
         seed = 90
-        batch_num = 2
+        batch_num = 100000
         with fluid.imperative.guard():
             fluid.default_startup_program().random_seed = seed
             fluid.default_main_program().random_seed = seed
@@ -125,85 +125,109 @@ class TestImperativeMnist(unittest.TestCase):
                 label = to_variable(y_data)
                 label._stop_gradient = True
 
+                print("forward start")
+
                 cost = mnist(img)
                 loss = fluid.layers.cross_entropy(cost, label)
                 avg_loss = fluid.layers.mean(loss)
-                dy_out = avg_loss._numpy()
+                #  dy_out = avg_loss._numpy()
+                print("forward end")
 
-                if batch_id == 0:
-                    for param in fluid.default_main_program().global_block(
-                    ).all_parameters():
-                        dy_param_init_value[param.name] = param._numpy()
+                #  if batch_id == 0:
+                    #  for param in fluid.default_main_program().global_block(
+                    #  ).all_parameters():
+                        #  dy_param_init_value[param.name] = param._numpy()
 
                 avg_loss._backward()
-                sgd.minimize(avg_loss)
-                mnist.clear_gradients()
-                dy_param_value = {}
-                for param in fluid.default_main_program().global_block(
-                ).all_parameters():
-                    dy_param_value[param.name] = param._numpy()
-
-        with new_program_scope():
-            fluid.default_startup_program().random_seed = seed
-            fluid.default_main_program().random_seed = seed
-
-            exe = fluid.Executor(fluid.CPUPlace(
-            ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
 
-            mnist = MNIST()
-            sgd = SGDOptimizer(learning_rate=1e-3)
-            train_reader = paddle.batch(
-                paddle.dataset.mnist.train(), batch_size=128)
+                print("backward end")
 
-            img = fluid.layers.data(
-                name='pixel', shape=[1, 28, 28], dtype='float32')
-            label = fluid.layers.data(name='label', shape=[1], dtype='int64')
-            cost = mnist(img)
-            loss = fluid.layers.cross_entropy(cost, label)
-            avg_loss = fluid.layers.mean(loss)
                 sgd.minimize(avg_loss)
 
-            # initialize params and fetch them
-            static_param_init_value = {}
-            static_param_name_list = []
-            for param in fluid.default_startup_program().global_block(
-            ).all_parameters():
-                static_param_name_list.append(param.name)
+                print("sgd end")
 
-            out = exe.run(fluid.default_startup_program(),
-                          fetch_list=static_param_name_list)
-
-            for i in range(len(static_param_name_list)):
-                static_param_init_value[static_param_name_list[i]] = out[i]
-
-            for batch_id, data in enumerate(train_reader()):
-                if batch_id >= batch_num:
-                    break
-
-                static_x_data = np.array(
-                    [x[0].reshape(1, 28, 28) for x in data]).astype('float32')
-                y_data = np.array([x[1] for x in data]).astype('int64').reshape(
-                    [128, 1])
-
-                fetch_list = [avg_loss.name]
-                fetch_list.extend(static_param_name_list)
-                out = exe.run(fluid.default_main_program(),
-                              feed={"pixel": static_x_data,
-                                    "label": y_data},
-                              fetch_list=fetch_list)
-
-                static_param_value = {}
-                static_out = out[0]
-                for i in range(1, len(out)):
-                    static_param_value[static_param_name_list[i - 1]] = out[i]
-
-        for key, value in six.iteritems(static_param_init_value):
-            self.assertTrue(np.allclose(value, dy_param_init_value[key]))
-
-        self.assertTrue(np.allclose(static_out, dy_out))
+                mnist.clear_gradients()
 
-        for key, value in six.iteritems(static_param_value):
-            self.assertTrue(np.allclose(value, dy_param_value[key]))
+                import gc
+                for name, var in fluid.default_main_program().global_block().vars.items():
+                    if not var.persistable:
+                        fluid.default_main_program().global_block()._remove_var(name)
+                        #  var._ivar._clear_values()
+                for op in fluid.default_main_program().global_block().ops:
+                    fluid.default_main_program().global_block()._remove_op(op.idx)
+
+                assert len(gc.get_referrers(avg_loss)) == 1
+
+                print("clear end")
+                print("ivar ref ", gc.get_referrers(gc.get_referrers(avg_loss._ivar)[0])[0].__class__.__name__)
+                print("ivar ref ", gc.get_referrers(gc.get_referrers(avg_loss._ivar)[1])[0].__class__.__name__)
+
+                #  dy_param_value = {}
+                #  for param in fluid.default_main_program().global_block(
+                #  ).all_parameters():
+                    #  dy_param_value[param.name] = param._numpy()
+
+        #  with new_program_scope():
+            #  fluid.default_startup_program().random_seed = seed
+            #  fluid.default_main_program().random_seed = seed
+
+            #  exe = fluid.Executor(fluid.CPUPlace(
+            #  ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
+
+            #  mnist = MNIST()
+            #  sgd = SGDOptimizer(learning_rate=1e-3)
+            #  train_reader = paddle.batch(
+                #  paddle.dataset.mnist.train(), batch_size=128)
+
+            #  img = fluid.layers.data(
+                #  name='pixel', shape=[1, 28, 28], dtype='float32')
+            #  label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+            #  cost = mnist(img)
+            #  loss = fluid.layers.cross_entropy(cost, label)
+            #  avg_loss = fluid.layers.mean(loss)
+            #  sgd.minimize(avg_loss)
+
+            #  # initialize params and fetch them
+            #  static_param_init_value = {}
+            #  static_param_name_list = []
+            #  for param in fluid.default_startup_program().global_block(
+            #  ).all_parameters():
+                #  static_param_name_list.append(param.name)
+
+            #  out = exe.run(fluid.default_startup_program(),
+                          #  fetch_list=static_param_name_list)
+
+            #  for i in range(len(static_param_name_list)):
+                #  static_param_init_value[static_param_name_list[i]] = out[i]
+
+            #  for batch_id, data in enumerate(train_reader()):
+                #  if batch_id >= batch_num:
+                    #  break
+
+                #  static_x_data = np.array(
+                    #  [x[0].reshape(1, 28, 28) for x in data]).astype('float32')
+                #  y_data = np.array([x[1] for x in data]).astype('int64').reshape(
+                    #  [128, 1])
+
+                #  fetch_list = [avg_loss.name]
+                #  fetch_list.extend(static_param_name_list)
+                #  out = exe.run(fluid.default_main_program(),
+                              #  feed={"pixel": static_x_data,
+                                    #  "label": y_data},
+                              #  fetch_list=fetch_list)
+
+                #  static_param_value = {}
+                #  static_out = out[0]
+                #  for i in range(1, len(out)):
+                    #  static_param_value[static_param_name_list[i - 1]] = out[i]
+
+        #  for key, value in six.iteritems(static_param_init_value):
+            #  self.assertTrue(np.allclose(value, dy_param_init_value[key]))
+
+        #  self.assertTrue(np.allclose(static_out, dy_out))
+
+        #  for key, value in six.iteritems(static_param_value):
+            #  self.assertTrue(np.allclose(value, dy_param_value[key]))
 
 
 if __name__ == '__main__':