Polish code

test=develop

paddle/fluid/framework/operator.h

@@ -69,7 +69,7 @@ inline std::string GradVarName(const std::string& var_name) {
   return result;
 }
 
-inline std::string OriginVarName(const std::string& grad_var_name) {
+inline std::string GradOriginalVarName(const std::string& grad_var_name) {
   std::size_t pos = grad_var_name.rfind(kGradVarSuffix);
   if (pos == std::string::npos) {
     return grad_var_name;

paddle/fluid/framework/operator_test.cc

@@ -294,24 +294,24 @@ TEST(VarNameTest, all) {
   std::string grad_var_name = paddle::framework::GradVarName(var_name);
   ASSERT_EQ(grad_var_name, "X@GRAD");
   std::string original_var_name =
-      paddle::framework::OriginVarName(grad_var_name);
+      paddle::framework::GradOriginalVarName(grad_var_name);
   ASSERT_EQ(original_var_name, "X");
-  original_var_name = paddle::framework::OriginVarName(original_var_name);
+  original_var_name = paddle::framework::GradOriginalVarName(original_var_name);
   ASSERT_EQ(original_var_name, "X");
 
   std::string var_name_2("XYZ");
   grad_var_name = paddle::framework::GradVarName(var_name_2);
   ASSERT_EQ(grad_var_name, "XYZ@GRAD");
-  original_var_name = paddle::framework::OriginVarName(grad_var_name);
+  original_var_name = paddle::framework::GradOriginalVarName(grad_var_name);
   ASSERT_EQ(original_var_name, "XYZ");
-  original_var_name = paddle::framework::OriginVarName(original_var_name);
+  original_var_name = paddle::framework::GradOriginalVarName(original_var_name);
   ASSERT_EQ(original_var_name, "XYZ");
 
   std::string var_name_3("");
   grad_var_name = paddle::framework::GradVarName(var_name_3);
   ASSERT_EQ(grad_var_name, "@GRAD");
-  original_var_name = paddle::framework::OriginVarName(grad_var_name);
+  original_var_name = paddle::framework::GradOriginalVarName(grad_var_name);
   ASSERT_EQ(original_var_name, "");
-  original_var_name = paddle::framework::OriginVarName(original_var_name);
+  original_var_name = paddle::framework::GradOriginalVarName(original_var_name);
   ASSERT_EQ(original_var_name, "");
 }

paddle/fluid/imperative/layer.cc

@@ -32,6 +32,11 @@ using framework::Variable;
 void AddTo(Variable* src, Variable* dst) {
   framework::LoDTensor* dst_tensor = dst->GetMutable<framework::LoDTensor>();
   framework::LoDTensor* src_tensor = src->GetMutable<framework::LoDTensor>();
+  // FIXME(minqiyang): loss_grad op will pass a zero grad of label
+  // ugly fix for it
+  if (src_tensor->numel() == 0) {
+    return;
+  }
   PADDLE_ENFORCE(dst_tensor->numel() == src_tensor->numel(),
                  "dst_numel %lld vs. src_numel %lld", dst_tensor->numel(),
                  src_tensor->numel());
@@ -157,13 +162,9 @@ std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
     auto& outputs = grad_outputs[it.first];
     auto& origin_outputs = it.second;
 
-    auto& forward_inputs = input_vars_[framework::OriginVarName(it.first)];
-
     for (size_t i = 0; i < outputs.size(); ++i) {
-      if (!forward_inputs[i]->stop_gradient_) {
-        framework::Variable* orig_grad = origin_outputs[i];
-        AddTo(outputs[i], orig_grad);
-      }
+      framework::Variable* orig_grad = origin_outputs[i];
+      AddTo(outputs[i], orig_grad);
     }
   }
   return input_vars_;

paddle/fluid/imperative/layer.h

@@ -81,7 +81,15 @@ class OpBase;
 
 class VarBase {
  public:
-  explicit VarBase(bool stop_gradient = false)
+  VarBase()
+      : pre_op_(nullptr),
+        pre_op_out_idx_(-1),
+        var_desc_(nullptr),
+        var_(new framework::Variable()),
+        grads_(new framework::Variable()),
+        stop_gradient_(false) {}
+
+  explicit VarBase(bool stop_gradient)
       : pre_op_(nullptr),
         pre_op_out_idx_(-1),
         var_desc_(nullptr),
@@ -89,23 +97,12 @@ class VarBase {
         grads_(new framework::Variable()),
         stop_gradient_(stop_gradient) {}
 
-  virtual ~VarBase() {
-    if (var_) {
-      delete var_;
-      var_ = nullptr;
-    }
-    if (grads_) {
-      delete grads_;
-      grads_ = nullptr;
-    }
-  }
+  virtual ~VarBase() {}
 
   void RunBackward();
 
   framework::LoDTensor& Grad();
 
-  inline framework::Variable* GradVar() { return grads_; }
-
   inline std::string GradName() const {
     PADDLE_ENFORCE(
         var_desc_,

paddle/fluid/imperative/tracer.h

@@ -57,7 +57,7 @@ class Tracer {
   void Trace(OpBase* op,
              const std::map<std::string, std::vector<VarBase*>>& inputs,
              const std::map<std::string, std::vector<VarBase*>>& outputs,
-             framework::BlockDesc* block, const bool stop_gradient) {
+             framework::BlockDesc* block, const bool stop_gradient = false) {
     std::map<std::string, VarBase*> vars;
 
     framework::OpDesc* op_desc = op->op_desc_;
@@ -153,6 +153,7 @@ class Tracer {
           }
         }
       }
+
       for (auto it : grad_op_desc->Outputs()) {
         auto& grad_out_vars = op->grad_output_vars_[it.first];
         for (const std::string& grad_outvar : it.second) {

paddle/fluid/pybind/pybind.cc

@@ -125,7 +125,8 @@ PYBIND11_MODULE(core, m) {
   m.add_object("_cleanup",
                py::capsule([]() { ScopePool::Instance().Clear(); }));
 
-  py::class_<imperative::VarBase, PyVarBase>(m, "VarBase", R"DOC()DOC")
+  py::class_<imperative::VarBase, std::shared_ptr<imperative::VarBase>>(
+      m, "VarBase", R"DOC()DOC")
       // .def(py::init<>())
       .def(py::init<bool>(), py::arg("stop_gradient") = false)
       .def("_run_backward",

python/paddle/fluid/imperative/layers.py

@@ -24,20 +24,29 @@ __all__ = ['PyLayer']
 
 
 class PyLayer(core.Layer):
-    def __init__(self, *args, **kwargs):
-        self._once_built = True
-
+    def __init__(self,
+                 dtype=core.VarDesc.VarType.FP32,
+                 param_attr=None,
+                 bias_attr=None,
+                 name=None):
         from ..layer_helper import LayerHelper
-        self._helper = LayerHelper(type(self).__name__, **kwargs)
-        self._dtype = kwargs.get("dtype", core.VarDesc.VarType.FP32)
+        self._helper = LayerHelper(
+            type(self).__name__,
+            param_attr=param_attr,
+            bias_attr=bias_attr,
+            dtype=dtype,
+            name=name)
+
+        self._once_built = False
+        self._dtype = dtype
 
     def _build_once(self, inputs):
         pass
 
     def __call__(self, *inputs):
-        if self._once_built:
+        if not self._once_built:
             self._build_once(*inputs)
-            self._once_built = False
+            self._once_built = True
 
         outputs = self.forward(*inputs)
 

python/paddle/fluid/layer_helper.py

@@ -314,11 +314,9 @@ class LayerHelper(object):
             WeightNormParamAttr.params_with_weight_norm.append(param)
             return param
         if _in_imperative_mode():
-            self.main_program.global_block().create_parameter(
-                dtype=dtype, shape=shape, **attr._to_kwargs())
             # In imperative mode, we want the returned parameter to be
             # initialized so that it can be used imperatively.
-            return self.startup_program.global_block().create_parameter(
+            return self.main_program.global_block().create_parameter(
                 dtype=dtype,
                 shape=shape,
                 **attr._to_kwargs(with_initializer=True))

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

@@ -111,8 +111,6 @@ class TestImperativeMnist(unittest.TestCase):
                 predict = mnist(img)
                 out = fluid.layers.cross_entropy(predict, label)
                 out._backward()
-                filter_grad = mnist._simple_img_conv_pool_1._conv2d._filter_param._gradient(
-                )
                 sgd.minimize(out)