Polish code

test=develop

paddle/fluid/framework/framework.proto

@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 syntax = "proto2";
-/* option optimize_for = LITE_RUNTIME; */
+option optimize_for = LITE_RUNTIME;
 package paddle.framework.proto;
 
 // Any incompatible changes to ProgramDesc and its dependencies should

paddle/fluid/imperative/layer.cc

@@ -192,7 +192,6 @@ std::vector<Variable*> OpBase::ApplyGrad(framework::Scope* scope) {
         LOG(ERROR) << "tracer doesn't support yet";
       }
     }
-    VLOG(3) << "op grad output var " << outvar << " is inited";
   }
 
   grad_op_desc_->InferShape(*block_);

paddle/fluid/imperative/tracer.h

@@ -52,7 +52,7 @@ class Tracer {
              const std::vector<VarBase*>& outputs, framework::BlockDesc* block,
              const bool stop_gradient) {
     framework::OpDesc* op_desc = op->op_desc_;
-    LOG(ERROR) << "tracer tracing " << op_desc->Type();
+    VLOG(3) << "tracer tracing " << op_desc->Type();
     op_desc->InferShape(*block);
     op_desc->InferVarType(block);
     std::unique_ptr<framework::OperatorBase> op_base =
@@ -61,10 +61,7 @@ class Tracer {
     *op->input_vars_ = inputs;
     for (VarBase* input : inputs) {
       const std::string vname = input->var_desc_->Name();
-      LOG(ERROR) << "input: " << vname;
-      LOG(ERROR) << "input var: " << input->var_;
       framework::Variable* var = root_scope_->Var(vname);
-      LOG(ERROR) << "var_ in tracer pointer: " << var;
       input->var_ = var;
       if (!var->IsInitialized()) {
         framework::VarDesc* var_desc = block->FindVar(vname);
@@ -102,7 +99,7 @@ class Tracer {
       outputs[i]->pre_op_out_idx_ = i;
     }
 
-    LOG(ERROR) << "tracer running " << op_desc->Type();
+    VLOG(3) << "tracer running " << op_desc->Type();
     op_base->Run(*root_scope_, platform::CPUPlace());
     if (!stop_gradient) {
       framework::OpDesc* grad_op_desc;

paddle/fluid/operators/optimizers/sgd_op.h

@@ -29,8 +29,6 @@ class SGDOpKernel : public framework::OpKernel<T> {
     const auto *param_var = ctx.InputVar("Param");
     const auto *grad_var = ctx.InputVar("Grad");
 
-    LOG(ERROR) << "grad_var: " << grad_var;
-
     if (param_var->IsType<framework::LoDTensor>()) {
       const auto *param = ctx.Input<framework::Tensor>("Param");
       auto *param_out = ctx.Output<framework::Tensor>("ParamOut");
@@ -41,11 +39,8 @@ class SGDOpKernel : public framework::OpKernel<T> {
         const auto *grad = ctx.Input<framework::Tensor>("Grad");
 
         auto p = framework::EigenVector<T>::Flatten(*param);
-        LOG(ERROR) << "param flattened";
         auto g = framework::EigenVector<T>::Flatten(*grad);
-        LOG(ERROR) << "grad flattened";
         auto o = framework::EigenVector<T>::Flatten(*param_out);
-        LOG(ERROR) << "paramout flattened";
         auto *lr = learning_rate->data<T>();
 
         o = p - lr[0] * g;

paddle/fluid/pybind/pybind.cc

@@ -117,19 +117,14 @@ PYBIND11_MODULE(core, m) {
            [](imperative::VarBase &self, framework::Scope *scope) {
              self.RunBackward(scope);
            })
-      .def("_grad_var",
-           [](const imperative::VarBase &self) {
-             LOG(ERROR) << "grad_var_ pointer: " << self.grads_;
-             return self.grads_;
-           },
-           py::return_value_policy::reference)
       .def("_grad_name", &imperative::VarBase::GradName)
       .def("_grad", &imperative::VarBase::Grad)
-      .def("_print_var_pointer",
-           [](const imperative::VarBase &self) {
-             LOG(ERROR) << self.var_desc_->Name()
-                        << " print_var pointer: " << self.var_;
-           })
+      .def_property("grad_value",
+                    [](const imperative::VarBase &self) { return self.grads_; },
+                    [](imperative::VarBase &self, framework::Variable *grad) {
+                      self.grads_ = grad;
+                    },
+                    py::return_value_policy::reference)
       .def_property("value",
                     [](const imperative::VarBase &self) { return self.var_; },
                     [](imperative::VarBase &self, framework::Variable *var) {

python/paddle/fluid/framework.py

@@ -361,6 +361,7 @@ class Variable(object):
 
         self.block.vars[name] = self
         self.op = None
+        self.stop_gradient = stop_gradient
         self.is_data = is_data
         if _in_imperative_mode():
             self._ivar = core.VarBase()
@@ -368,7 +369,6 @@ class Variable(object):
             self._ivar.stop_gradient = stop_gradient
 
     def _numpy(self):
-        print("get_variable_tensor", self.desc.name())
         scope = _imperative_tracer().get_scope()
         tensor = core.get_variable_tensor(scope, self.desc.name())
         return np.array(tensor)
@@ -597,8 +597,7 @@ class Operator(object):
                  type=None,
                  inputs=None,
                  outputs=None,
-                 attrs=None,
-                 stop_gradient=False):
+                 attrs=None):
         self.block = block
         self.desc = desc
         # note: not add self.attrs here:
@@ -640,7 +639,6 @@ class Operator(object):
 
         if inputs is not None:
             for in_proto in proto.inputs:
-                print("create op: find_name", in_proto.name)
                 found = find_name(inputs, in_proto.name)
                 assert found or in_proto.dispensable, "Input {} not found".format(
                     in_proto.name)
@@ -1178,7 +1176,6 @@ class Block(object):
     def create_var(self, *args, **kwargs):
         var = Variable(block=self, *args, **kwargs)
         if 'initializer' in kwargs:
-            print("initializer, ", type(kwargs['initializer']))
             kwargs['initializer'](var, self)
         return var
 
@@ -1293,16 +1290,6 @@ class Block(object):
         """
         op_desc = self.desc.append_op()
         op = Operator(block=self, desc=op_desc, *args, **kwargs)
-        print("op inputs: ", [v._numpy() for v in op.inputs])
-        print("op inputs: ", [v for v in op.inputs])
-        import sys
-        sys.stdout.flush()
-        for v in op.inputs:
-            v._ivar._print_var_pointer()
-        print("print var pointer end")
-        import sys
-        sys.stdout.flush()
-
         if _in_imperative_mode():
             _imperative_tracer().trace(op.iop, [v._ivar for v in op.inputs],
                                        [v._ivar for v in op.outputs], self.desc,
@@ -1360,10 +1347,6 @@ class Block(object):
             _imperative_tracer().trace(op.iop, [v._ivar for v in op.inputs],
                                        [v._ivar for v in op.outputs], self.desc,
                                        kwargs.get("stop_gradient", False))
-            print([v.name for v in op.outputs])
-            for v in op.outputs:
-                v._ivar._print_var_pointer()
-            print("fill_constant end")
         self.ops.insert(0, op)
         return op
 

python/paddle/fluid/initializer.py

@@ -153,7 +153,6 @@ class ConstantInitializer(Initializer):
         assert isinstance(var, framework.Variable)
         assert isinstance(block, framework.Block)
         # Initialization Ops should be prepended and not appended
-        print("fill_constant")
         op = block._prepend_op(
             type="fill_constant",
             outputs={"Out": var},

python/paddle/fluid/layer_helper.py

@@ -22,6 +22,7 @@ import numpy as np
 
 from .framework import Variable, Parameter, default_main_program, default_startup_program, dtype_is_floating
 from . import unique_name
+from paddle.fluid.imperative import base as imperative_base
 from paddle.fluid.imperative.base import to_variable
 from paddle.fluid.initializer import Constant, Xavier
 from .param_attr import ParamAttr, WeightNormParamAttr
@@ -369,13 +370,16 @@ class LayerHelper(object):
 
     def set_variable_initializer(self, var, initializer):
         assert isinstance(var, Variable)
-        return self.startup_program.global_block().create_var(
-            name=var.name,
-            type=var.type,
-            dtype=var.dtype,
-            shape=var.shape,
-            persistable=True,
-            initializer=initializer)
+        if imperative_base.enabled():
+            initializer(var, self.startup_program.global_block())
+        else:
+            self.startup_program.global_block().create_var(
+                name=var.name,
+                type=var.type,
+                dtype=var.dtype,
+                shape=var.shape,
+                persistable=True,
+                initializer=initializer)
 
     def append_bias_op(self, input_var, dim_start=1, dim_end=None):
         """

python/paddle/fluid/layers/tensor.py

@@ -132,16 +132,9 @@ def create_global_var(shape,
         persistable=persistable,
         name=name,
         stop_gradient=True)
-    print("set_variable_initializer, ", var.name)
-    if imperative_base.enabled():
-        var = helper.set_variable_initializer(
-            var, initializer=Constant(
-                value=float(value), force_cpu=force_cpu))
-        print("get var", var)
-    else:
-        helper.set_variable_initializer(
-            var, initializer=Constant(
-                value=float(value), force_cpu=force_cpu))
+    helper.set_variable_initializer(
+        var, initializer=Constant(
+            value=float(value), force_cpu=force_cpu))
 
     return var
 

python/paddle/fluid/optimizer.py

@@ -109,7 +109,6 @@ class Optimizer(object):
         # create learning rate variable for every parameter
         param = param_and_grad[0]
         param_lr = param.optimize_attr['learning_rate']
-        print("param_lr: ", param_lr, self._global_learning_rate()._numpy())
         if type(param_lr) == Variable:
             return param_lr
         else:
@@ -311,15 +310,12 @@ class Optimizer(object):
                 parameters = program.global_block().all_parameters()
                 params_grads = []
                 for param in parameters:
+                    # create gradient variable
                     grad_var = Variable(
                         block=loss.block,
                         name=param._ivar._grad_name(),
                         stop_gradient=True)
                     grad_var._value = param._ivar._grad_var()
-                    print("create grad var: ", grad_var.name)
-                    print("grad_var value: ", grad_var._numpy())
-                    import sys
-                    sys.stdout.flush()
                     params_grads.append((param, grad_var))
 
             optimize_ops = self._create_optimization_pass(params_grads, loss,
@@ -381,10 +377,6 @@ class SGDOptimizer(Optimizer):
     def _append_optimize_op(self, block, param_and_grad):
         assert isinstance(block, framework.Block)
 
-        print("append sgd")
-        import sys
-        sys.stdout.flush()
-
         # create the optimize op
         sgd_op = block.append_op(
             type=self.type,