RNNOp remove alias (#4274)

* remove alias

paddle/framework/scope.h

@@ -58,6 +58,8 @@ class Scope {
   /// nullptr if cannot find.
   Variable* FindVar(const std::string& name) const;
 
+  const Scope& parent() const { return *parent_; }
+
   /// Find the scope or an ancestor scope that contains the given variable.
   const Scope* FindScope(const Variable* var) const;
 

paddle/operators/recurrent_op.cc

@@ -29,9 +29,11 @@ using Tensor = framework::Tensor;
 using LoDTensor = framework::LoDTensor;
 
 void RecurrentAlgorithm::InferShape(const Scope& scope) const {
-  seq_len_ = scope.FindVar((arg_->inlinks[0]).external)
-                 ->GetMutable<LoDTensor>()
-                 ->dims()[0];
+  auto* input0 = scope.FindVar(arg_->inlinks[0]);
+  PADDLE_ENFORCE_NOT_NULL(input0);
+  seq_len_ = input0->GetMutable<LoDTensor>()->dims()[0];
+  PADDLE_ENFORCE_GT(seq_len_, 0);
+
   CreateScopes(scope);
   auto step_scopes = GetStepScopes(scope);
   rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
@@ -123,13 +125,11 @@ void RecurrentAlgorithm::InitMemories(Scope* step_scope,
 }
 
 const rnn::ArgumentName RecurrentOp::kArgName{
-    "step_net", "step_scopes",  "inlinks",
-    "outlinks", "inlink_alias", "outlink_alias",
+    "step_net", "step_scopes",  "inlinks",      "outlinks",
     "memories", "pre_memories", "boot_memories"};
 
 const rnn::ArgumentName RecurrentGradientOp::kArgName{
-    "step_net",    "step_scopes",  "outlink@grad",
-    "inlink@grad", "inlink_alias", "outlink_alias",
+    "step_net", "step_scopes",  "outlink@grad",      "inlink@grad",
     "memories", "pre_memories", "boot_memories@grad"};
 
 RecurrentOp::RecurrentOp(const std::string& type,
@@ -160,8 +160,6 @@ class RecurrentAlgorithmProtoAndCheckerMaker
     AddOutput(name.step_scopes, "step scopes");
 
     // Attributes stored in AttributeMap
-    AddAttr<std::vector<std::string>>(name.inlink_alias, "alias of inlinks");
-    AddAttr<std::vector<std::string>>(name.outlink_alias, "alias of outlinks");
     AddAttr<std::vector<std::string>>(name.pre_memories,
                                       "names of pre-memories");
     AddAttr<std::vector<std::string>>(name.memories, "names of memories");
@@ -206,9 +204,8 @@ void RecurrentGradientAlgorithm::LinkBootMemoryGradients(
 }
 
 void RecurrentGradientAlgorithm::InferShape(const Scope& scope) const {
-  seq_len_ = scope.FindVar((arg_->inlinks[0]).external)
-                 ->GetMutable<LoDTensor>()
-                 ->dims()[0];
+  seq_len_ =
+      scope.FindVar(arg_->inlinks[0])->GetMutable<LoDTensor>()->dims()[0];
   auto step_scopes = GetStepScopes(scope);
   rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
                      true /*infer_shape_mode*/);

paddle/operators/rnn/recurrent_op_utils.cc

@@ -24,22 +24,23 @@ using Tensor = framework::Tensor;
 using LoDTensor = framework::LoDTensor;
 
 void SegmentInputs(const std::vector<Scope*>& step_scopes,
-                   const std::vector<Link>& inlinks, const size_t seq_len,
-                   bool infer_shape_mode) {
+                   const std::vector<std::string>& inlinks,
+                   const size_t seq_len, bool infer_shape_mode) {
   PADDLE_ENFORCE(!inlinks.empty(), "no in links are provided.");
   for (size_t i = 0; i < inlinks.size(); ++i) {
-    auto input_var = step_scopes[0]->FindVar(inlinks[i].external);
-    PADDLE_ENFORCE(input_var != nullptr, "input link [%s] is not in scope.",
-                   inlinks[i].external);
+    // global inputs
+    auto input_var = step_scopes[0]->parent().FindVar(inlinks[i]);
+    PADDLE_ENFORCE_NOT_NULL(input_var, "input link [%s] is not in scope.",
+                            inlinks[i]);
 
     LoDTensor* input = input_var->GetMutable<LoDTensor>();
     f::DDim dims = input->dims();
-    PADDLE_ENFORCE(static_cast<size_t>(dims[0]) == seq_len,
-                   "all the inlinks must have same length");
+    PADDLE_ENFORCE_EQ(static_cast<size_t>(dims[0]), seq_len,
+                      "all the inlinks be the same length");
     f::DDim step_dims = slice_ddim(dims, 1, dims.size());
     for (size_t j = 0; j < seq_len; j++) {
       Tensor* step_input =
-          step_scopes[j]->NewVar(inlinks[i].internal)->GetMutable<Tensor>();
+          step_scopes[j]->NewVar(inlinks[i])->GetMutable<Tensor>();
       if (!infer_shape_mode) {
         // The input of operators of each step is Tensor here.
         // Maybe need to modify Slice function.
@@ -51,18 +52,17 @@ void SegmentInputs(const std::vector<Scope*>& step_scopes,
 }
 
 void ConcatOutputs(const std::vector<Scope*>& step_scopes,
-                   const std::vector<Link>& outlinks, const size_t seq_len,
-                   bool infer_shape_mode) {
+                   const std::vector<std::string>& outlinks,
+                   const size_t seq_len, bool infer_shape_mode) {
   for (size_t i = 0; i < outlinks.size(); i++) {
-    auto output_var = step_scopes[0]->FindVar(outlinks[i].external);
-    PADDLE_ENFORCE(output_var != nullptr, "output link [%s] is not in scope.",
-                   outlinks[i].external);
+    auto output_var = step_scopes[0]->parent().FindVar(outlinks[i]);
+    PADDLE_ENFORCE_NOT_NULL(output_var, "output link [%s] is not in scope.",
+                            outlinks[i]);
     LoDTensor* output = output_var->GetMutable<LoDTensor>();
 
     if (infer_shape_mode) {
-      auto step_scope_var = step_scopes[0]->FindVar(outlinks[i].internal);
-      PADDLE_ENFORCE(step_scope_var != nullptr, "%s not in scope",
-                     outlinks[i].internal);
+      auto step_scope_var = step_scopes[0]->FindVar(outlinks[i]);
+      PADDLE_ENFORCE_NOT_NULL(step_scope_var, "%s not in scope", outlinks[i]);
       f::DDim step_dims =
           step_scope_var->template GetMutable<LoDTensor>()->dims();
       std::vector<int64_t> dims_vec = vectorize(step_dims);
@@ -71,9 +71,8 @@ void ConcatOutputs(const std::vector<Scope*>& step_scopes,
     } else {
       output->mutable_data<float>(platform::CPUPlace());
       for (size_t j = 0; j < seq_len; j++) {
-        LoDTensor* step_output = step_scopes[j]
-                                     ->FindVar(outlinks[i].internal)
-                                     ->GetMutable<LoDTensor>();
+        LoDTensor* step_output =
+            step_scopes[j]->FindVar(outlinks[i])->GetMutable<LoDTensor>();
         // TODO(luotao02) data type and platform::DeviceContext() should set
         // correctly
         (output->Slice<float>(j, j + 1))
@@ -113,29 +112,9 @@ void InitArgument(const ArgumentName& name, Argument* arg,
                   const framework::OperatorBase& op) {
   arg->step_scopes = op.Output(name.step_scopes);
 
-  auto inlinks = op.Inputs(name.inlinks);
-  auto inlink_alias = op.Attr<std::vector<std::string>>(name.inlink_alias);
-  PADDLE_ENFORCE(inlinks.size() == inlink_alias.size(),
-                 "the size of inlinks and inlink_alias don't match:%d,%d",
-                 inlinks.size(), inlink_alias.size());
-  for (size_t i = 0; i < inlinks.size(); ++i) {
-    rnn::Link link;
-    link.external = inlinks[i];
-    link.internal = inlink_alias[i];
-    (arg->inlinks).push_back(link);
-  }
+  arg->inlinks = op.Inputs(name.inlinks);
 
-  auto outlinks = op.Outputs(name.outlinks);
-  auto outlink_alias = op.Attr<std::vector<std::string>>(name.outlink_alias);
-  PADDLE_ENFORCE(outlinks.size() == outlink_alias.size(),
-                 "the size of outlinks and outlink_alias don't match:%d,%d",
-                 outlinks.size(), outlink_alias.size());
-  for (size_t i = 0; i < outlinks.size(); ++i) {
-    rnn::Link link;
-    link.external = outlinks[i];
-    link.internal = outlink_alias[i];
-    (arg->outlinks).push_back(link);
-  }
+  arg->outlinks = op.Outputs(name.outlinks);
 
   auto boot_memories = op.Inputs(name.boot_memories);
 

paddle/operators/rnn/recurrent_op_utils.h

@@ -41,18 +41,11 @@ struct MemoryAttr {
   std::string boot_var;
 };
 
-struct Link {
-  // input or output links name.
-  std::string internal;
-  // alias to avoid duplicate keys in scopes.
-  std::string external;
-};
-
 struct Argument {
   std::string step_net;
   std::string step_scopes;
-  std::vector<Link> inlinks;
-  std::vector<Link> outlinks;
+  std::vector<std::string> inlinks;
+  std::vector<std::string> outlinks;
   std::vector<rnn::MemoryAttr> memories;
 };
 
@@ -61,8 +54,6 @@ struct ArgumentName {
   std::string step_scopes;
   std::string inlinks;
   std::string outlinks;
-  std::string inlink_alias;   // the alias of inlinks in step net.
-  std::string outlink_alias;  // the alias of outlinks in step net.
   std::string memories;       // the memory name
   std::string pre_memories;   // the previous memory name
   std::string boot_memories;  // the boot memory name
@@ -72,15 +63,15 @@ struct ArgumentName {
  * Prepare inputs for each step net.
  */
 void SegmentInputs(const std::vector<Scope*>& step_scopes,
-                   const std::vector<Link>& inlinks, const size_t seq_len,
-                   bool infer_shape_mode);
+                   const std::vector<std::string>& inlinks,
+                   const size_t seq_len, bool infer_shape_mode);
 
 /**
  * Process outputs of step nets and merge to variables.
  */
 void ConcatOutputs(const std::vector<Scope*>& step_scopes,
-                   const std::vector<Link>& outlinks, const size_t seq_len,
-                   bool infer_shape_mode);
+                   const std::vector<std::string>& outlinks,
+                   const size_t seq_len, bool infer_shape_mode);
 
 void LinkMemories(const std::vector<Scope*>& step_scopes,
                   const std::vector<MemoryAttr>& memories, const size_t step_id,

python/paddle/v2/framework/tests/test_recurrent_op.py

@@ -59,7 +59,6 @@ class PySimpleRNNTest(unittest.TestCase):
 
     def test_forward(self):
         output = self.rnn.forward()
-        print 'output', output
 
 
 def create_tensor(scope, name, shape, np_data):
@@ -103,7 +102,7 @@ class TestRecurrentOp(unittest.TestCase):
         ctx = core.DeviceContext.create(core.CPUPlace())
         self.rnnop.infer_shape(self.scope)
         self.rnnop.run(self.scope, ctx)
-        return np.array(self.scope.find_var("h").get_tensor())
+        return np.array(self.scope.find_var("h@mem").get_tensor())
 
     def create_global_variables(self):
         # create inlink
@@ -123,8 +122,7 @@ class TestRecurrentOp(unittest.TestCase):
         create_tensor(self.scope, "h_boot", [self.batch_size, self.input_dim],
                       h_boot_np_data)
         self.scope.new_var("step_scopes")
-        self.scope.new_var("h@alias")
-        self.scope.new_var("h")
+        self.scope.new_var("h@mem")
 
     def create_rnn_op(self):
         # create RNNOp
@@ -134,20 +132,18 @@ class TestRecurrentOp(unittest.TestCase):
             boot_memories=["h_boot"],
             step_net="stepnet",
             # outputs
-            outlinks=["h"],
+            outlinks=["h@mem"],
             step_scopes="step_scopes",
             # attributes
-            inlink_alias=["x@alias"],
-            outlink_alias=["h@alias"],
             pre_memories=["h@pre"],
-            memories=["h@alias"])
+            memories=["h@mem"])
 
     def create_step_net(self):
         stepnet = core.Net.create()
-        x_fc_op = Operator("mul", X="x@alias", Y="W", Out="Wx")
+        x_fc_op = Operator("mul", X="x", Y="W", Out="Wx")
         h_fc_op = Operator("mul", X="h@pre", Y="U", Out="Uh")
         sum_op = Operator("add", X="Wx", Y="Uh", Out="sum")
-        sig_op = Operator("sigmoid", X="sum", Y="h@alias")
+        sig_op = Operator("sigmoid", X="sum", Y="h@mem")
 
         for op in [x_fc_op, h_fc_op, sum_op, sig_op]:
             stepnet.append_op(op)