wip

paddle/framework/block_desc.cc

@@ -42,28 +42,30 @@ bool BlockDesc::HasVar(const std::string &name) const {
   return vars_.find(name) != vars_.end();
 }
 
-void BlockDesc::RenameVar(const std::string &old_name,
-                          const std::string &new_name) {
-  if (this->HasVar(old_name)) {
-    auto *var = this->Var(old_name);
-    var->SetName(new_name);
-    vars_[new_name].reset(var);
-    vars_.erase(old_name);
-    // rename inputs and outputs
-    for (const auto &op : ops_) {
-      auto *it = op.get();
-      for (auto in_name : it->InputArgumentNames()) {
-        if (in_name == old_name) {
-          it->RenameInput(old_name, new_name);
-        }
-      }
-      for (auto out_name : it->OutputArgumentNames()) {
-        if (out_name == old_name) {
-          it->RenameOutput(old_name, new_name);
-        }
-      }
-    }
+VarDesc *BlockDesc::RenameVar(const std::string &old_name,
+                              const std::string &new_name) {
+  if (!this->HasVar(old_name)) {
+    return nullptr;
+  }
+  need_update_ = true;
+  auto *var = this->Var(old_name);
+  VarDesc *new_var = new VarDesc(*(var->Proto()));
+  new_var->SetName(new_name);
+  // new_var->SetShape(var->GetShape());
+  // new_var->SetType(var->GetType());
+  // new_var->SetDataType(var->GetDataType());
+  // new_var->SetLoDLevel(var->GetLoDLevel());
+  // new_var->SetPersistable(var->Persistable());
+
+  vars_[new_name].reset(new_var);
+
+  // rename inputs and outputs
+  for (const auto &op : ops_) {
+    auto *it = op.get();
+    it->Rename(old_name, new_name);
   }
+  vars_.erase(old_name);
+  return new_var;
 }
 
 VarDesc *BlockDesc::FindVarRecursive(const std::string &name) const {

paddle/framework/block_desc.h

@@ -55,7 +55,7 @@ class BlockDesc {
 
   bool HasVar(const std::string &var_name) const;
 
-  void RenameVar(const std::string &old_name, const std::string &new_name);
+  VarDesc *RenameVar(const std::string &old_name, const std::string &new_name);
 
   VarDesc *FindVarRecursive(const std::string &name_bytes) const;
 

paddle/pybind/protobuf.cc

@@ -170,12 +170,14 @@ void BindBlockDesc(py::module &m) {
            [](BlockDesc &self, py::bytes byte_name) {
              std::string name = byte_name;
              return self.HasVar(name);
-           })
+           },
+           py::return_value_policy::reference)
       .def("rename_var",
-           [](BlockDesc &self, py::bytes byte_name, py::bytes byte_name_new) {
+           [](BlockDesc &self, const py::bytes &byte_name,
+              const py::bytes &byte_name_new) {
              std::string name = byte_name;
              std::string new_name = byte_name_new;
-             return self.RenameVar(name, new_name);
+             self.RenameVar(name, new_name);
            })
       .def("has_var_recursive",
            [](BlockDesc &self, py::bytes byte_name) {
@@ -213,7 +215,7 @@ void BindVarDsec(py::module &m) {
   py::class_<VarDesc> var_desc(m, "VarDesc", "");
   var_desc
       .def("name",
-           [](const VarDesc &self) {
+           [](VarDesc &self) {
              py::bytes name = self.Name();
              return name;
            },

python/paddle/v2/dataset/common.py

@@ -74,6 +74,8 @@ def download(url, module_name, md5sum, save_name=None):
     retry = 0
     retry_limit = 3
     while not (os.path.exists(filename) and md5file(filename) == md5sum):
+        if os.path.exists(filename):
+            print "file md5", md5file(filename), md5sum
         if retry < retry_limit:
             retry += 1
         else:

python/paddle/v2/fluid/distribute_transpiler.py

@@ -175,6 +175,7 @@ class DistributeTranspiler:
             shape=[0])
 
         # create send_op
+        print("send inputs: ", send_inputs)
         send_op = program.global_block().append_op(
             type="send",
             inputs={"X": send_inputs},
@@ -204,12 +205,12 @@ class DistributeTranspiler:
             block_map[varname].append((long(offset), long(size)))
         for varname, splited in block_map.iteritems():
             orig_var = program.global_block().var(varname)
-
             if len(splited) == 1:
                 # rename var to the trainer_id var
                 new_var_name = "%s.trainer_%d" % \
                     (orig_var.name, self.trainer_id)
                 program.global_block().rename_var(varname, new_var_name)
+                print("renaming OK...", varname, new_var_name)
                 var_mapping[varname] = \
                     [program.global_block().var(new_var_name)]
                 continue
@@ -375,7 +376,10 @@ class DistributeTranspiler:
         new_inputs = dict()
         # update param/grad shape first, then other inputs like
         # moment can use the updated shape
+        print("mark1")
         for key in opt_op.input_names:
+            # print("opt type: ", opt_op.type)
+            # print("opt op input: ", key)
             if key == "Grad":
                 grad_block = None
                 for g in self.param_grad_ep_mapping[endpoint]["grads"]:
@@ -422,6 +426,7 @@ class DistributeTranspiler:
 
                 new_inputs[key] = tmpvar
 
+        print("mark2")
         for key in opt_op.input_names:
             if key in ["Param", "Grad"]:
                 continue
@@ -453,6 +458,7 @@ class DistributeTranspiler:
             inputs=new_inputs,
             outputs=outputs,
             attrs=opt_op.attrs)
+        print("mark3")
 
     def _append_pserver_non_opt_ops(self, program, pserver_program, opt_op):
         # Append the ops for parameters that do not need to be optimized/updated
@@ -523,6 +529,11 @@ class DistributeTranspiler:
         optimize_sub_program = Program()
         # Iterate through the ops and append ops as needed
         for idx, opt_op in enumerate(self.optimize_ops):
+            print("mark0")
+            print(opt_op.inputs.keys())
+            for v in opt_op.inputs.values():
+                print(v.name)
+                print(v.shape)
             is_op_on_pserver = self._is_op_on_pserver(endpoint,
                                                       self.optimize_ops, idx)
             if not is_op_on_pserver:

python/paddle/v2/fluid/framework.py

@@ -741,9 +741,75 @@ class Block(object):
         """
         if not self.has_var(name):
             raise ValueError("var %s is not in current" % name)
+        v = self.var(name)
+        stop_gradient = None
+        trainable = None
+        optimize_attr = None
+        regularizer = None
+        gradient_clip_attr = None
+        error_clip = None
+        if type(v) == Parameter:
+            stop_gradient = v.stop_gradient
+            trainable = v.trainable
+            optimize_attr = v.optimize_attr
+            regularizer = v.regularizer
+            gradient_clip_attr = v.gradient_clip_attr
+            error_clip = v.error_clip
+        elif type(v) == Variable:
+            error_clip = v.error_clip
+            stop_gradient = v.stop_gradient
+        else:
+            raise ValueError("unsupported var type: %s", type(v))
+
+        def _clear_op_io_for_var(name):
+            for op in self.ops:
+                for k in op.inputs.keys():
+
+                    if op.inputs[k].name == name:
+                        op.inputs[k] = None
+                for k in op.outputs.keys():
+                    if op.outputs[k].name == name:
+                        op.outputs[k] = None
+
+        _clear_op_io_for_var(name)
         self.desc.rename_var(name, new_name)
+        d = self.desc.find_var(new_name)
+        var = None
+        if type(v) == Parameter:
+            var = Parameter(
+                self,
+                d.shape(),
+                d.dtype(),
+                name=new_name,
+                stop_gradient=stop_gradient,
+                trainable=trainable,
+                optimize_attr=optimize_attr,
+                regularizer=regularizer,
+                gradient_clip_attr=gradient_clip_attr,
+                error_clip=error_clip)
+        elif type(v) == Variable:
+            var = Variable(
+                self,
+                name=new_name,
+                error_clip=error_clip,
+                stop_gradient=stop_gradient)
+
+        # rename the python side, sync_with_cpp will only add
+        # new vars/ops to python side.
+        self.vars[new_name] = var
+        for op in self.ops:
+            print("### rename op i/o ", name, op.inputs)
+            if op.inputs:
+                for k in op.inputs.keys():
+                    if op.inputs[k] == None:
+                        print("rename input: ", name, var)
+                        op.inputs[k] = var
+            if op.outputs:
+                for k in op.outputs.keys():
+                    if op.outputs[k] == None:
+                        op.outputs[k] = var
+        del self.vars[name]
         self.sync_with_cpp()
-        print("renamed var: ", self.var(new_name))
 
     def create_parameter(self, *args, **kwargs):
         global_block = self.program.global_block()