Rename shrink_state -> shrink_rnn_memory

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paddle/operators/shrink_state_op.cc → paddle/operators/shrink_rnn_memory_op.cc

@@ -18,12 +18,12 @@
 namespace paddle {
 namespace operators {
 
-class ShrinkStateOp : public ArrayOp {
+class ShrinkRNNMemoryOp : public ArrayOp {
  public:
-  ShrinkStateOp(const std::string &type,
-                const framework::VariableNameMap &inputs,
-                const framework::VariableNameMap &outputs,
-                const framework::AttributeMap &attrs)
+  ShrinkRNNMemoryOp(const std::string &type,
+                    const framework::VariableNameMap &inputs,
+                    const framework::VariableNameMap &outputs,
+                    const framework::AttributeMap &attrs)
       : ArrayOp(type, inputs, outputs, attrs) {}
 
   void Run(const framework::Scope &scope,
@@ -36,18 +36,12 @@ class ShrinkStateOp : public ArrayOp {
     PADDLE_ENFORCE(rank_table_var != nullptr, "RankTable must be set");
     auto &rank_table = rank_table_var->Get<framework::LoDRankTable>();
 
-    int dst_num_rows = 0;
-
-    {
-      auto &rank_items = rank_table.items();
-      for (auto &rank_item : rank_items) {
-        if (offset < rank_item.length) {
-          ++dst_num_rows;
-        } else {
-          break;
-        }
-      }
-    }
+    auto &rank_items = rank_table.items();
+    int dst_num_rows =
+        std::lower_bound(rank_items.begin(), rank_items.end(), offset,
+                         [](const framework::LoDRankTable::TableItem &a,
+                            size_t b) { return a.length > b; }) -
+        rank_items.begin();
 
     auto *out_var = scope.FindVar(Output("Out"));
     PADDLE_ENFORCE(out_var != nullptr, "Output Out must be set");
@@ -58,10 +52,10 @@ class ShrinkStateOp : public ArrayOp {
   }
 };
 
-class ShrinkStateOpProtoMaker : public framework::OpProtoAndCheckerMaker {
+class ShrinkRNNMemoryOpProtoMaker : public framework::OpProtoAndCheckerMaker {
  public:
-  ShrinkStateOpProtoMaker(framework::OpProto *proto,
-                          framework::OpAttrChecker *op_checker)
+  ShrinkRNNMemoryOpProtoMaker(framework::OpProto *proto,
+                              framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "");
     AddInput("RankTable", "");
@@ -71,7 +65,7 @@ class ShrinkStateOpProtoMaker : public framework::OpProtoAndCheckerMaker {
   }
 };
 
-class ShrinkStateOpInferShape : public framework::InferShapeBase {
+class ShrinkRNNMemoryInferShape : public framework::InferShapeBase {
  public:
   void operator()(framework::InferShapeContext *context) const override {
     PADDLE_ENFORCE(context->HasInput("X"));
@@ -81,19 +75,18 @@ class ShrinkStateOpInferShape : public framework::InferShapeBase {
   }
 };
 
-class ShrinkStateGradOp : public ArrayOp {
+class ShrinkRNNMemoryGradOp : public ArrayOp {
  public:
-  ShrinkStateGradOp(const std::string &type,
-                    const framework::VariableNameMap &inputs,
-                    const framework::VariableNameMap &outputs,
-                    const framework::AttributeMap &attrs)
+  ShrinkRNNMemoryGradOp(const std::string &type,
+                        const framework::VariableNameMap &inputs,
+                        const framework::VariableNameMap &outputs,
+                        const framework::AttributeMap &attrs)
       : ArrayOp(type, inputs, outputs, attrs) {}
 
   void Run(const framework::Scope &scope,
            const platform::DeviceContext &dev_ctx) const override {
     auto *dout_var = scope.FindVar(Input(framework::GradVarName("Out")));
-    auto dx_name = Output(framework::GradVarName("X"));
-    auto *dx_var = scope.FindVar(dx_name);
+    auto *dx_var = scope.FindVar(Output(framework::GradVarName("X")));
     PADDLE_ENFORCE(dx_var != nullptr, "Input Gradient should not be nullptr");
     auto *x_var = scope.FindVar(Input("X"));
     PADDLE_ENFORCE(x_var != nullptr);
@@ -110,7 +103,7 @@ class ShrinkStateGradOp : public ArrayOp {
       auto height = dout_tensor.dims()[0];
       dx_tensor.Slice(0, static_cast<int>(height))
           .CopyFrom(dout_tensor, dout_tensor.place(), dev_ctx);
-      if (height < dout_tensor.dims()[0]) {
+      if (dx_tensor.dims()[0] < height) {
         auto rest_tensor = dx_tensor.Slice(
             static_cast<int>(height), static_cast<int>(dout_tensor.dims()[0]));
         math::set_constant(dev_ctx, &rest_tensor, 0.0f);
@@ -119,7 +112,7 @@ class ShrinkStateGradOp : public ArrayOp {
   }
 };
 
-class ShrikStateGradInferShape : public framework::InferShapeBase {
+class ShrinkRNNMemoryGradInferShape : public framework::InferShapeBase {
  public:
   void operator()(framework::InferShapeContext *context) const override {
     PADDLE_ENFORCE(context->HasInput("X"));
@@ -129,14 +122,14 @@ class ShrikStateGradInferShape : public framework::InferShapeBase {
   }
 };
 
-class ShrinkStateGradOpMaker : public framework::SingleGradOpDescMaker {
+class ShrinkRNNGradOpMaker : public framework::SingleGradOpDescMaker {
  public:
   using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
 
  protected:
   std::unique_ptr<framework::OpDescBind> Apply() const override {
     auto *op = new framework::OpDescBind();
-    op->SetType("shrink_state_grad");
+    op->SetType("shrink_rnn_memory_grad");
     op->SetInput("X", Input("X"));
     op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
     op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
@@ -149,8 +142,8 @@ class ShrinkStateGradOpMaker : public framework::SingleGradOpDescMaker {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OPERATOR(shrink_state, ops::ShrinkStateOp,
-                  ops::ShrinkStateOpInferShape, ops::ShrinkStateOpProtoMaker,
-                  ops::ShrinkStateGradOpMaker);
-REGISTER_OPERATOR(shrink_state_grad, ops::ShrinkStateGradOp,
-                  ops::ShrikStateGradInferShape);
+REGISTER_OPERATOR(shrink_rnn_memory, ops::ShrinkRNNMemoryOp,
+                  ops::ShrinkRNNMemoryInferShape,
+                  ops::ShrinkRNNMemoryOpProtoMaker, ops::ShrinkRNNGradOpMaker);
+REGISTER_OPERATOR(shrink_rnn_memory_grad, ops::ShrinkRNNMemoryGradOp,
+                  ops::ShrinkRNNMemoryGradInferShape);

paddle/operators/tensor_array_read_write_op.cc

@@ -85,7 +85,6 @@ class WriteToArrayInferVarType : public framework::VarTypeInference {
  public:
   void operator()(const framework::OpDescBind &op_desc,
                   framework::BlockDescBind *block) const override {
-    VLOG(10) << "I am here?";
     for (auto &out_var : op_desc.OutputArgumentNames()) {
       VLOG(10) << "Set Variable " << out_var << " as LOD_TENSOR_ARRAY";
       block->Var(out_var)->SetType(framework::VarDesc::LOD_TENSOR_ARRAY);

python/paddle/v2/framework/layers.py

@@ -844,7 +844,7 @@ def shrink_memory(x, i, table, main_program=None):
     helper = LayerHelper('shrink_memory', **locals())
     out = helper.create_tmp_variable(dtype=x.data_type)
     helper.append_op(
-        type='shrink_state',
+        type='shrink_rnn_memory',
         inputs={'X': [x],
                 'I': [i],
                 'RankTable': [table]},

python/paddle/v2/framework/tests/test_shrink_state.py → python/paddle/v2/framework/tests/test_shrink_rnn_memory.py

@@ -7,8 +7,8 @@ from paddle.v2.framework.framework import g_main_program
 import numpy
 
 
-class TestShrinkState(unittest.TestCase):
-    def test_shrink_state(self):
+class TestShrinkRNNMemory(unittest.TestCase):
+    def test_shrink_rnn_memory(self):
         x = layers.data('x', shape=[100], data_type='float32')
         x.stop_gradient = False
         table = layers.lod_rank_table(x=x)