implement ClearBlock

paddle/fluid/framework/block_desc.cc

@@ -163,6 +163,20 @@ std::vector<OpDesc *> BlockDesc::AllOps() const {
   return res;
 }
 
+void BlockDesc::ClearBlock() {
+  // 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

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

paddle/fluid/imperative/layer.h

@@ -103,7 +103,9 @@ class OpBase;
  */
 class VarBase {
  public:
-  VarBase(std::string name) : VarBase(new framework::Variable(), new VarBase(name + "XGRAD", true), name) {}
+  explicit VarBase(std::string name)
+      : VarBase(new framework::Variable(), new VarBase(name + "XGRAD", true),
+                name) {}
 
   // Owns `var` and `grad`
   VarBase(framework::Variable* var, VarBase* grad, std::string name)
@@ -113,7 +115,7 @@ class VarBase {
         stop_gradient_(false),
         pre_op_(nullptr),
         pre_op_out_idx_(-1),
-        name_(name) { LOG(ERROR) << "create " << name; }
+        name_(name) {}
 
   explicit VarBase(std::string name, bool stop_gradient)
       : var_desc_(nullptr),
@@ -122,11 +124,9 @@ class VarBase {
         stop_gradient_(stop_gradient),
         pre_op_(nullptr),
         pre_op_out_idx_(-1),
-        name_(name) { LOG(ERROR) << "create " << name;  }
+        name_(name) {}
 
   virtual ~VarBase() {
-    LOG(ERROR) << "delete " << name_;
-
     if (var_) {
       delete var_;
     }

paddle/fluid/imperative/tracer.cc

@@ -66,16 +66,38 @@ platform::Place GetExpectedPlace(platform::Place place, VarBasePtrMap inputs) {
   return result;
 }
 
+// framework::BlockDesc* InferShapeAndVarType(OpBase* op, const VarBasePtrMap&
+// inputs, const VarBasePtrMap& outputs) {
+// std::unique_ptr<BlockDesc> block(new BlockDesc());
+
+// // construct op desc
+// op->op_desc_ = block.AppendOp();
+
+// // construct op inputs and outputs
+// // for
+// //
+// for (auto it = )
+// op->op_desc_->SetInput()
+
+// op->op_desc_->InferShape(*block);
+// op->op_desc_->InferVarType(block.get());
+
+// return block.release();
+// }
+
 void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
                    const VarBasePtrMap& outputs, framework::BlockDesc* block,
                    const platform::Place expected_place,
                    const bool stop_gradient) {
   std::map<std::string, VarBase*> vars;
 
+  // framework::BlockDesc* block = InferShapeAndVarType(op, inputs, outputs);
+
   framework::OpDesc* op_desc = op->op_desc_;
   VLOG(3) << "tracer tracing " << op_desc->Type();
   op_desc->InferShape(*block);
   op_desc->InferVarType(block);
+
   std::unique_ptr<framework::OperatorBase> op_base =
       framework::OpRegistry::CreateOp(*op_desc);
 
@@ -92,7 +114,7 @@ void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
 
       invars.emplace_back(inp->var_);
       vars[inp->var_desc_->Name()] = inp;
-      if (inp->PreOp()) {
+      if (inp->PreOp() && !inp->IsStopGradient()) {
         op->pre_ops_[it.first].push_back(inp->PreOp());
         op->pre_ops_out_idx_[it.first].push_back(inp->PreOpOutIdx());
       } else {
@@ -202,7 +224,7 @@ std::vector<VarBase*> Tracer::PyTrace(OpBase* op,
   op->input_vars_[PyLayer::kFwdInp] = inputs;
   op->output_vars_[PyLayer::kFwdOut] = PyLayer::Apply(op->forward_id_, inputs);
   for (VarBase* inp : inputs) {
-    if (inp->PreOp()) {
+    if (inp->PreOp() && !inp->IsStopGradient()) {
       op->pre_ops_[PyLayer::kFwdInp].push_back(inp->PreOp());
       op->pre_ops_out_idx_[PyLayer::kFwdInp].push_back(inp->PreOpOutIdx());
     } else {

paddle/fluid/pybind/protobuf.cc

@@ -189,6 +189,9 @@ void BindBlockDesc(pybind11::module *m) {
              return self.HasVar(name);
            },
            pybind11::return_value_policy::reference)
+      .def("_clear_block",
+           [](pd::BlockDesc &self) { return self.ClearBlock(); },
+           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

@@ -1188,6 +1188,15 @@ class Block(object):
         else:
             raise ValueError("Var {0} is not found recursively".format(name))
 
+    def _clear_block(self):
+        self.desc._clear_block()
+
+        for name, var in self.vars.items():
+            if not var.persistable:
+                del self.vars[name]
+
+        self.ops.clear()
+
     def all_parameters(self):
         return list(self.iter_parameters())
 
@@ -1273,8 +1282,7 @@ class Block(object):
         return var
 
     def _remove_var(self, name):
-        if not _in_imperative_mode():
-            self._sync_with_cpp()
+        self._sync_with_cpp()
         self.desc._remove_var(cpt.to_bytes(name))
         del self.vars[name]
 
@@ -1358,8 +1366,7 @@ class Block(object):
         Returns:
             None
         """
-        if not _in_imperative_mode():
-            self._sync_with_cpp()
+        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 +101,8 @@ class MNIST(fluid.imperative.Layer):
 class TestImperativeMnist(unittest.TestCase):
     def test_mnist_float32(self):
         seed = 90
-        batch_num = 100000
+        epoch_num = 1
+        batch_num = 200
         with fluid.imperative.guard():
             fluid.default_startup_program().random_seed = seed
             fluid.default_main_program().random_seed = seed
@@ -109,125 +110,112 @@ class TestImperativeMnist(unittest.TestCase):
             mnist = MNIST()
             sgd = SGDOptimizer(learning_rate=1e-3)
             train_reader = paddle.batch(
-                paddle.dataset.mnist.train(), batch_size=128)
+                paddle.dataset.mnist.train(), batch_size=128, drop_last=True)
 
             dy_param_init_value = {}
-            for batch_id, data in enumerate(train_reader()):
-                if batch_id >= batch_num:
-                    break
-
-                dy_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)
-
-                img = to_variable(dy_x_data)
-                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()
-                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()
-
-                avg_loss._backward()
-
-                print("backward end")
-
-                sgd.minimize(avg_loss)
-
-                print("sgd end")
-
-                mnist.clear_gradients()
-
-                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)
+            for epoch in range(epoch_num):
+                print("epoch", epoch)
+                for batch_id, data in enumerate(train_reader()):
+                    #  if batch_id >= batch_num:
+                    #  break
 
-                assert len(gc.get_referrers(avg_loss)) == 1
+                    dy_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)
 
-                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__)
+                    img = to_variable(dy_x_data)
+                    label = to_variable(y_data)
+                    label._stop_gradient = True
 
-                #  dy_param_value = {}
-                #  for param in fluid.default_main_program().global_block(
-                #  ).all_parameters():
-                    #  dy_param_value[param.name] = param._numpy()
+                    cost = mnist(img)
+                    loss = fluid.layers.cross_entropy(cost, label)
+                    avg_loss = fluid.layers.mean(loss)
 
-        #  with new_program_scope():
-            #  fluid.default_startup_program().random_seed = seed
-            #  fluid.default_main_program().random_seed = seed
+                    dy_out = avg_loss._numpy()
 
-            #  exe = fluid.Executor(fluid.CPUPlace(
-            #  ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
+                    if epoch == 0 and batch_id == 0:
+                        for param in fluid.default_main_program().global_block(
+                        ).all_parameters():
+                            dy_param_init_value[param.name] = param._numpy()
 
-            #  mnist = MNIST()
-            #  sgd = SGDOptimizer(learning_rate=1e-3)
-            #  train_reader = paddle.batch(
-                #  paddle.dataset.mnist.train(), batch_size=128)
+                    avg_loss._backward()
+                    sgd.minimize(avg_loss)
+                    mnist.clear_gradients()
 
-            #  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)
+                    fluid.default_main_program().global_block()._clear_block()
 
-            #  # 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)
+                    dy_param_value = {}
+                    for param in fluid.default_main_program().global_block(
+                    ).all_parameters():
+                        dy_param_value[param.name] = param._numpy()
 
-            #  out = exe.run(fluid.default_startup_program(),
-                          #  fetch_list=static_param_name_list)
+        with new_program_scope():
+            fluid.default_startup_program().random_seed = seed
+            fluid.default_main_program().random_seed = seed
 
-            #  for i in range(len(static_param_name_list)):
-                #  static_param_init_value[static_param_name_list[i]] = out[i]
+            exe = fluid.Executor(fluid.CPUPlace(
+            ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
 
-            #  for batch_id, data in enumerate(train_reader()):
-                #  if batch_id >= batch_num:
+            mnist = MNIST()
+            sgd = SGDOptimizer(learning_rate=1e-3)
+            train_reader = paddle.batch(
+                paddle.dataset.mnist.train(), batch_size=128, drop_last=True)
+
+            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 epoch in range(epoch_num):
+                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]))
+                    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__':