Using LoDTensor instead of Tensor in every operator.

paddle/framework/lod_tensor.h

@@ -59,7 +59,7 @@ class LoDTensor : public Tensor {
 
   void set_lod(const LoD& lod) { lod_ = lod; }
 
-  LoD lod() { return lod_; }
+  LoD lod() const { return lod_; }
 
   /*
    * Get a element from LoD.

paddle/framework/operator.cc

@@ -186,6 +186,54 @@ void OperatorBase::GenerateTemporaryNames() {
   }
 }
 
+template <>
+const Tensor* InferShapeContext::Input<Tensor>(const std::string& name) const {
+  auto* var = InputVar(name);
+  if (var == nullptr) return nullptr;
+  if (var->IsType<LoDTensor>()) {
+    return &var->Get<LoDTensor>();
+  }
+  PADDLE_ENFORCE(var->IsType<Tensor>(),
+                 "The Input(%s) must be LoDTensor or Tensor.");
+  return &var->Get<Tensor>();
+}
+
+template <>
+const std::vector<const Tensor*> InferShapeContext::MultiInput<Tensor>(
+    const std::string& name) const {
+  auto names = op().Inputs(name);
+  std::vector<const Tensor*> res;
+  res.reserve(names.size());
+  std::transform(
+      names.begin(), names.end(), std::back_inserter(res),
+      [&](const std::string& sub_name) { return Input<Tensor>(sub_name); });
+  return res;
+}
+
+template <>
+Tensor* ExecutionContext::Output<Tensor>(const std::string& name) const {
+  auto* var = OutputVar(name);
+  if (var == nullptr) return nullptr;
+  if (var->IsType<LoDTensor>()) {
+    return const_cast<LoDTensor*>(&var->Get<LoDTensor>());
+  }
+  PADDLE_ENFORCE(var->IsType<Tensor>(),
+                 "The Input(%s) must be LoDTensor or Tensor.");
+  return const_cast<Tensor*>(&var->Get<Tensor>());
+}
+
+template <>
+std::vector<Tensor*> ExecutionContext::MultiOutput<Tensor>(
+    const std::string& name) const {
+  auto names = op().Outputs(name);
+  std::vector<Tensor*> res;
+  res.reserve(names.size());
+  std::transform(
+      names.begin(), names.end(), std::back_inserter(res),
+      [&](const std::string& sub_name) { return Output<Tensor>(sub_name); });
+  return res;
+}
+
 void OpProtoAndCheckerMaker::Validate() {
   validated_ = true;
   CheckNoDuplicatedInOutAttrs();

paddle/framework/operator.h

@@ -22,6 +22,7 @@ limitations under the License. */
 #include "op_info.h"
 #include "paddle/framework/attribute.h"
 #include "paddle/framework/framework.pb.h"
+#include "paddle/framework/lod_tensor.h"
 #include "paddle/framework/scope.h"
 #include "paddle/framework/tensor.h"
 #include "paddle/platform/device_context.h"
@@ -305,11 +306,9 @@ class InferShapeContext {
     auto names = op_.Inputs(name);
     std::vector<const T*> res;
     res.reserve(names.size());
-    std::transform(names.begin(), names.end(), std::back_inserter(res),
-                   [&](const std::string& sub_name) {
-                     auto var = scope_.FindVar(sub_name);
-                     return var == nullptr ? nullptr : &var->Get<T>();
-                   });
+    std::transform(
+        names.begin(), names.end(), std::back_inserter(res),
+        [&](const std::string& sub_name) { return Input<T>(sub_name); });
     return res;
   }
 
@@ -318,11 +317,9 @@ class InferShapeContext {
     auto names = op_.Outputs(name);
     std::vector<T*> res;
     res.reserve(names.size());
-    std::transform(names.begin(), names.end(), std::back_inserter(res),
-                   [&](const std::string& sub_name) {
-                     auto var = scope_.FindVar(sub_name);
-                     return var == nullptr ? nullptr : var->GetMutable<T>();
-                   });
+    std::transform(
+        names.begin(), names.end(), std::back_inserter(res),
+        [&](const std::string& sub_name) { return Output<T>(sub_name); });
     return res;
   }
 
@@ -363,6 +360,27 @@ class ExecutionContext : public InferShapeContext {
     return device_context_;
   }
 
+  // redefine Output function,
+  // use Variable::Get instead of Variable::GetMutable
+  template <typename T>
+  T* Output(const std::string& name) const {
+    auto var = OutputVar(name);
+    return var == nullptr ? nullptr : const_cast<T*>(&var->Get<T>());
+  }
+
+  // redefine MultiOutput function.
+  // use Variable::Get instead of Variable::GetMutable
+  template <typename T>
+  std::vector<T*> MultiOutput(const std::string& name) const {
+    auto names = op().Outputs(name);
+    std::vector<T*> res;
+    res.reserve(names.size());
+    std::transform(
+        names.begin(), names.end(), std::back_inserter(res),
+        [&](const std::string& sub_name) { return Output<T>(sub_name); });
+    return res;
+  }
+
   const platform::DeviceContext* device_context_;
 };
 

paddle/framework/tensor_impl.h

@@ -16,8 +16,6 @@ limitations under the License. */
 #include "paddle/memory/memcpy.h"
 #include "paddle/platform/enforce.h"
 
-#include <glog/logging.h>
-
 namespace paddle {
 namespace framework {
 
@@ -55,7 +53,6 @@ inline T* Tensor::mutable_data(DDim dims, platform::Place place) {
 
 template <typename T>
 inline T* Tensor::mutable_data(platform::Place place) {
-  LOG(INFO) << "------ mutable_data ----  ";
   static_assert(std::is_pod<T>::value, "T must be POD");
   PADDLE_ENFORCE_GT(numel(), 0,
                     "Tensor's numel must be larger than zero to call "
@@ -145,7 +142,6 @@ inline Tensor Tensor::Slice(const int& begin_idx, const int& end_idx) const {
 }
 
 inline Tensor& Tensor::Resize(const DDim& dims) {
-  LOG(INFO) << "---- resize -----";
   dims_ = dims;
   numel_ = product(dims_);
   return *this;

paddle/operators/add_op.cc

@@ -26,7 +26,8 @@ class AddOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("X")->dims(),
                       ctx.Input<Tensor>("Y")->dims(),
                       "Two input of Add Op's dimension must be same.");
-    ctx.Output<Tensor>("Out")->Resize(ctx.Input<Tensor>("X")->dims());
+    ctx.Output<framework::LoDTensor>("Out")->Resize(
+        ctx.Input<Tensor>("X")->dims());
   }
 };
 

paddle/operators/concat_op.cc

@@ -26,7 +26,7 @@ class ConcatOp : public framework::OperatorWithKernel {
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
     auto ins = ctx.MultiInput<framework::Tensor>("X");
-    auto *out = ctx.Output<framework::Tensor>("Out");
+    auto *out = ctx.Output<framework::LoDTensor>("Out");
     size_t axis = static_cast<size_t>(ctx.Attr<int>("axis"));
     size_t n = ins.size();
 

paddle/operators/cos_sim_op.cc

@@ -32,9 +32,9 @@ class CosSimOp : public framework::OperatorWithKernel {
                       "Dimensions of Input(X) and Input(Y) must be the same.");
 
     auto dims = ctx.Input<Tensor>("X")->dims();
-    ctx.Output<Tensor>("Out")->Resize({dims[0], 1});
-    ctx.Output<Tensor>("XNorm")->Resize({dims[0], 1});
-    ctx.Output<Tensor>("YNorm")->Resize({dims[0], 1});
+    ctx.Output<framework::LoDTensor>("Out")->Resize({dims[0], 1});
+    ctx.Output<framework::LoDTensor>("XNorm")->Resize({dims[0], 1});
+    ctx.Output<framework::LoDTensor>("YNorm")->Resize({dims[0], 1});
   }
 };
 
@@ -88,8 +88,10 @@ class CosSimOpGrad : public framework::OperatorWithKernel {
                       "1st dimension of Out@GRAD must equal that of Input(X)");
     PADDLE_ENFORCE_EQ(out_dims[1], 1, "1st dimension of Out@GRAD must be one.");
 
-    auto *x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
-    auto *y_grad = ctx.Output<Tensor>(framework::GradVarName("Y"));
+    auto *x_grad =
+        ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
+    auto *y_grad =
+        ctx.Output<framework::LoDTensor>(framework::GradVarName("Y"));
     if (x_grad) x_grad->Resize(x_dims);
     if (y_grad) y_grad->Resize(y_dims);
   }

paddle/operators/cross_entropy_op.cc

@@ -29,7 +29,7 @@ class OnehotCrossEntropyOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_EQ(X->dims().size(), 2, "X's dimension must be 2.");
     PADDLE_ENFORCE_EQ(label->dims().size(), 1, "label's dimension must be 1.");
     PADDLE_ENFORCE_EQ(X->dims()[0], label->dims()[0]);
-    ctx.Output<Tensor>("Y")->Resize({X->dims()[0]});
+    ctx.Output<framework::LoDTensor>("Y")->Resize({X->dims()[0]});
   }
 };
 
@@ -39,7 +39,7 @@ class OnehotCrossEntropyGradientOp : public framework::OperatorWithKernel {
 
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
-    auto dX = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto dX = ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
     auto X = ctx.Input<Tensor>("X");
 
     dX->Resize(X->dims());

paddle/operators/fill_zeros_like_op.cc

@@ -23,7 +23,7 @@ class FillZerosLikeOp : public framework::OperatorWithKernel {
 
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
-    ctx.Output<framework::Tensor>("Dst")->Resize(
+    ctx.Output<framework::LoDTensor>("Dst")->Resize(
         ctx.Input<framework::Tensor>("Src")->dims());
   }
 };

paddle/operators/gather_op.cc

@@ -28,7 +28,7 @@ class GatherOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_GE(batch_size, 0, "Batch size must be >0");
     framework::DDim output_dims(ctx.Input<Tensor>("X")->dims());
     output_dims[0] = batch_size;
-    ctx.Output<Tensor>("Out")->Resize(output_dims);
+    ctx.Output<framework::LoDTensor>("Out")->Resize(output_dims);
   }
 };
 
@@ -38,7 +38,7 @@ class GatherGradOp : public framework::OperatorWithKernel {
 
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
-    auto X_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto X_grad = ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
     auto X = ctx.Input<Tensor>("X");
 
     X_grad->Resize(X->dims());

paddle/operators/gaussian_random_op.cc

@@ -44,7 +44,7 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
 
  protected:
   void InferShape(const framework::InferShapeContext& context) const override {
-    auto* tensor = context.Output<framework::Tensor>("Out");
+    auto* tensor = context.Output<framework::LoDTensor>("Out");
     auto dims = Attr<std::vector<int>>("dims");
     std::vector<int64_t> temp;
     temp.reserve(dims.size());

paddle/operators/lookup_table_op.cc

@@ -25,7 +25,7 @@ class LookupTableOp : public framework::OperatorWithKernel {
   void InferShape(const framework::InferShapeContext &context) const override {
     auto table_t = context.Input<Tensor>("W");
     auto ids_t = context.Input<Tensor>("Ids");
-    auto output_t = context.Output<Tensor>("Out");
+    auto output_t = context.Output<framework::LoDTensor>("Out");
 
     output_t->Resize({ids_t->dims()[0], table_t->dims()[1]});
   }
@@ -56,7 +56,8 @@ class LookupTableOpGrad : public framework::OperatorWithKernel {
  protected:
   void InferShape(const framework::InferShapeContext &context) const override {
     auto table = context.Input<Tensor>("W");
-    auto d_table = context.Output<Tensor>(framework::GradVarName("W"));
+    auto d_table =
+        context.Output<framework::LoDTensor>(framework::GradVarName("W"));
     d_table->Resize(table->dims());
   }
 };

paddle/operators/mean_op.cc

@@ -25,7 +25,7 @@ class MeanOp : public framework::OperatorWithKernel {
   void InferShape(const framework::InferShapeContext &ctx) const override {
     PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
                             "Input of MeanOp must be initialized.");
-    ctx.Output<Tensor>("Out")->Resize({1});
+    ctx.Output<framework::LoDTensor>("Out")->Resize({1});
   }
 };
 
@@ -45,7 +45,7 @@ class MeanGradOp : public framework::OperatorWithKernel {
 
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
-    ctx.Output<Tensor>(framework::GradVarName("X"))
+    ctx.Output<framework::LoDTensor>(framework::GradVarName("X"))
         ->Resize(ctx.Input<Tensor>("X")->dims());
   }
 };

paddle/operators/minus_op.cc

@@ -33,7 +33,7 @@ class MinusOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_EQ(
         left_tensor->numel(), right_tensor->numel(),
         "Minus operator must take two tensor with same num of elements");
-    ctx.Output<framework::Tensor>("Out")->Resize(left_tensor->dims());
+    ctx.Output<framework::LoDTensor>("Out")->Resize(left_tensor->dims());
   }
 };
 

paddle/operators/mul_op.cc

@@ -18,6 +18,7 @@ namespace paddle {
 namespace operators {
 
 using framework::Tensor;
+using framework::LoDTensor;
 
 class MulOp : public framework::OperatorWithKernel {
  public:
@@ -45,7 +46,8 @@ class MulOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_EQ(
         x_mat_dims[1], y_mat_dims[0],
         "First matrix's width must be equal with second matrix's height.");
-    ctx.Output<Tensor>("Out")->Resize({x_mat_dims[0], y_mat_dims[1]});
+    ctx.Output<framework::LoDTensor>("Out")->Resize(
+        {x_mat_dims[0], y_mat_dims[1]});
   }
 };
 
@@ -94,8 +96,10 @@ class MulOpGrad : public framework::OperatorWithKernel {
     auto x_dims = ctx.Input<Tensor>("X")->dims();
     auto y_dims = ctx.Input<Tensor>("Y")->dims();
     auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
-    auto *x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
-    auto *y_grad = ctx.Output<Tensor>(framework::GradVarName("Y"));
+    auto *x_grad =
+        ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
+    auto *y_grad =
+        ctx.Output<framework::LoDTensor>(framework::GradVarName("Y"));
 
     auto x_mat_dims =
         framework::flatten_to_2d(x_dims, Attr<int>("x_num_col_dims"));

paddle/operators/recurrent_op.cc

@@ -26,10 +26,11 @@ namespace operators {
 using Scope = framework::Scope;
 using Variable = framework::Variable;
 using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
 
 void RecurrentAlgorithm::InferShape(const Scope& scope) const {
   seq_len_ = scope.FindVar((arg_->inlinks[0]).external)
-                 ->GetMutable<Tensor>()
+                 ->GetMutable<LoDTensor>()
                  ->dims()[0];
   CreateScopes(scope);
   auto step_scopes = GetStepScopes(scope);
@@ -88,7 +89,7 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
         // the weight are located in parent scope
         for (auto& var_name : input.second) {
           if (!step_scope.FindVar(var_name)) {
-            step_scope.NewVar(var_name)->GetMutable<Tensor>();
+            step_scope.NewVar(var_name)->GetMutable<LoDTensor>();
           }
         }
       }
@@ -106,11 +107,12 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
 void RecurrentAlgorithm::InitMemories(Scope* step_scope,
                                       bool infer_shape_mode) const {
   for (auto& attr : arg_->memories) {
-    Tensor* pre_mem = step_scope->NewVar(attr.pre_var)->GetMutable<Tensor>();
+    auto* pre_mem = step_scope->NewVar(attr.pre_var)->GetMutable<LoDTensor>();
     PADDLE_ENFORCE(step_scope->FindVar(attr.boot_var) != nullptr,
                    "memory [%s]'s boot variable [%s] not exists", attr.var,
                    attr.boot_var);
-    Tensor* boot_mem = step_scope->FindVar(attr.boot_var)->GetMutable<Tensor>();
+    auto* boot_mem =
+        step_scope->FindVar(attr.boot_var)->GetMutable<LoDTensor>();
     if (infer_shape_mode) {
       pre_mem->Resize(boot_mem->dims());
       PADDLE_ENFORCE_EQ(pre_mem->dims().size(), 2);
@@ -192,9 +194,9 @@ void RecurrentGradientAlgorithm::LinkBootMemoryGradients(
                    "memory variable [%s] does not exists", attr.var);
     PADDLE_ENFORCE(step_scope->FindVar(attr.boot_var) != nullptr,
                    "boot variable [%s] does not exists", attr.boot_var);
-    Tensor* mem_grad = step_scope->NewVar(attr.var)->GetMutable<Tensor>();
-    Tensor* boot_mem_grad =
-        step_scope->NewVar(attr.boot_var)->GetMutable<Tensor>();
+    auto* mem_grad = step_scope->NewVar(attr.var)->GetMutable<LoDTensor>();
+    auto* boot_mem_grad =
+        step_scope->NewVar(attr.boot_var)->GetMutable<LoDTensor>();
     if (infer_shape_mode) {
       boot_mem_grad->Resize(mem_grad->dims());
     } else {
@@ -205,7 +207,7 @@ void RecurrentGradientAlgorithm::LinkBootMemoryGradients(
 
 void RecurrentGradientAlgorithm::InferShape(const Scope& scope) const {
   seq_len_ = scope.FindVar((arg_->inlinks[0]).external)
-                 ->GetMutable<Tensor>()
+                 ->GetMutable<LoDTensor>()
                  ->dims()[0];
   auto step_scopes = GetStepScopes(scope);
   rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,

paddle/operators/reshape_op.cc

@@ -46,7 +46,7 @@ class ReshapeOp : public framework::OperatorWithKernel {
     std::transform(shape.begin(), shape.end(), shape_int64.begin(),
                    [](int a) { return static_cast<int64_t>(a); });
     auto out_dims = framework::make_ddim(shape_int64);
-    ctx.Output<framework::Tensor>("Out")->Resize(out_dims);
+    ctx.Output<framework::LoDTensor>("Out")->Resize(out_dims);
   }
 };
 
@@ -90,7 +90,7 @@ class ReshapeGradOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
                             "Input(Out@GRAD) shouldn't be null.");
     auto dims = ctx.Input<framework::Tensor>("X")->dims();
-    auto *d_in = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
+    auto *d_in = ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
     d_in->Resize(dims);
   }
 };

paddle/operators/rowwise_add_op.cc

@@ -37,7 +37,7 @@ class RowwiseAddOp : public framework::OperatorWithKernel {
         framework::slice_ddim(x_dims, num_col_dims, x_dims.size()), b_dims,
         "The width of two operands must be same");
     PADDLE_ENFORCE_EQ(ctx.OutputSize("Out"), 1, "The output size must be 1");
-    ctx.Output<Tensor>("Out")->Resize(x_dims);
+    ctx.Output<framework::LoDTensor>("Out")->Resize(x_dims);
   }
 };
 
@@ -76,8 +76,8 @@ class RowwiseAddGradOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_EQ(
         framework::slice_ddim(x_dims, num_col_dims, x_dims.size()), b_dims,
         "The width of two operands must be same");
-    auto *dx = ctx.Output<Tensor>(framework::GradVarName("X"));
-    auto *db = ctx.Output<Tensor>(framework::GradVarName("b"));
+    auto *dx = ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
+    auto *db = ctx.Output<framework::LoDTensor>(framework::GradVarName("b"));
     if (dx) dx->Resize(x_dims);
     if (db) db->Resize(b_dims);
   }

paddle/operators/scale_op.cc

@@ -28,7 +28,7 @@ class ScaleOp : public framework::OperatorWithKernel {
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
     auto *in = ctx.Input<framework::Tensor>("X");
-    auto *out = ctx.Output<framework::Tensor>("Out");
+    auto *out = ctx.Output<framework::LoDTensor>("Out");
     out->Resize(in->dims());
   }
 };

paddle/operators/scatter_op.cc

@@ -35,7 +35,8 @@ class ScatterOp : public framework::OperatorWithKernel {
     framework::DDim data_dim(ctx.Input<Tensor>("Updates")->dims());
     for (int i = 1; i < data_dim.size(); ++i)
       PADDLE_ENFORCE_EQ(data_dim[i], ctx.Input<Tensor>("Updates")->dims()[i]);
-    ctx.Output<Tensor>("Out")->Resize(ctx.Input<Tensor>("Ref")->dims());
+    ctx.Output<framework::LoDTensor>("Out")->Resize(
+        ctx.Input<Tensor>("Ref")->dims());
   }
 };
 
@@ -45,9 +46,11 @@ class ScatterGradOp : public framework::OperatorWithKernel {
 
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
-    auto *dUpdates = ctx.Output<Tensor>(framework::GradVarName("Updates"));
+    auto *dUpdates =
+        ctx.Output<framework::LoDTensor>(framework::GradVarName("Updates"));
     auto *Updates = ctx.Input<Tensor>("Updates");
-    auto *dRef = ctx.Output<Tensor>(framework::GradVarName("Ref"));
+    auto *dRef =
+        ctx.Output<framework::LoDTensor>(framework::GradVarName("Ref"));
     auto *Ref = ctx.Input<Tensor>("Ref");
 
     dRef->Resize(Ref->dims());

paddle/operators/sequence_avg_pool_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/operators/sequence_avg_pool_op.h"
+
+namespace paddle {
+namespace operators {
+
+class SequenceAvgPoolOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext& ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
+                            "Input of SequenceAvgPoolOp"
+                            "must be initialized.");
+    auto* x = ctx.Input<framework::LoDTensor>("X");
+    auto dims = x->dims();
+    auto lod = x->lod();
+    PADDLE_ENFORCE_EQ(lod.size(), 1UL, "Only support one level sequence now.");
+    PADDLE_ENFORCE_GE(
+        dims[0],
+        /*batch size = */ static_cast<int64_t>(lod[0].size() - 1),
+        "The first dimension of Input(X) must be large than batch size.");
+    dims[0] = lod[0].size() - 1;
+    ctx.Output<framework::LoDTensor>("Out")->Resize({dims});
+  }
+};
+
+class SequenceAvgPoolOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  SequenceAvgPoolOpMaker(framework::OpProto* proto,
+                         framework::OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "Input of SequenceAvgPoolOp.");
+    AddOutput("Out", "The output of SequenceAvgPoolOp.");
+    AddComment(R"DOC(
+    SequenceAvgPoolOp averages features of all time-steps of each instance.
+    More detailed comments will be added later.
+    )DOC");
+  }
+};
+
+class SequenceAvgPoolGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext& ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
+                            "Gradient of Out should not be null");
+    auto og_dims =
+        ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"))->dims();
+    auto x_dims = ctx.Input<framework::LoDTensor>("X")->dims();
+    PADDLE_ENFORCE_EQ(og_dims.size(), x_dims.size(),
+                      "The rank of output grad must equal to Input(X).");
+    for (size_t i = 1; i < og_dims.size(); ++i) {
+      PADDLE_ENFORCE_EQ(og_dims[i], x_dims[i], "The dimension mismatch.");
+    }
+    auto* x_grad =
+        ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
+    x_grad->Resize(x_dims);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(sequence_avg_pool, ops::SequenceAvgPoolOp,
+            ops::SequenceAvgPoolOpMaker, sequence_avg_pool_grad,
+            ops::SequenceAvgPoolGradOp);
+REGISTER_OP_CPU_KERNEL(
+    sequence_avg_pool,
+    ops::SequenceAvgPoolKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    sequence_avg_pool_grad,
+    ops::SequenceAvgPoolGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/sequence_avg_pool_op.cu

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#define EIGEN_USE_GPU
+
+#include "paddle/operators/sequence_avg_pool_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(
+    sequence_avg_pool,
+    ops::SequenceAvgPoolKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+    sequence_avg_pool_grad,
+    ops::SequenceAvgPoolGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/sequence_avg_pool_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
+
+template <typename Place, typename T>
+class SequenceAvgPoolKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* in = context.Input<LoDTensor>("X");
+    auto* out = context.Output<LoDTensor>("Out");
+
+    auto dims = in->dims();
+    auto lod = in->lod();
+    int64_t w = in->numel() / dims[0];
+
+    out->mutable_data<T>(context.GetPlace());
+    auto place = context.GetEigenDevice<Place>();
+    for (int i = 0; i < lod[0].size() - 1; ++i) {
+      Tensor in_t = in->Slice<T>(static_cast<int>(lod[0][i]),
+                                 static_cast<int>(lod[0][i + 1]));
+      Tensor out_t = out->Slice<T>(i, i + 1);
+      int64_t h = static_cast<int64_t>(lod[0][i + 1] - lod[0][i]);
+      auto in_e = EigenMatrix<T>::From(in_t, {h, w});
+      auto out_e = EigenMatrix<T>::From(out_t, {h, w});
+      out_e.device(place) = in_e.mean(Eigen::array<int, 1>({{0}}));
+    }
+  }
+};
+
+template <typename Place, typename T>
+class SequenceAvgPoolGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* in = context.Output<LoDTensor>("X");
+    auto* in_g = context.Output<LoDTensor>(framework::GradVarName("X"));
+    auto* out_g = context.Input<LoDTensor>(framework::GradVarName("Out"));
+
+    auto dims = in->dims();
+    auto lod = in->lod();
+    int64_t w = in->numel() / dims[0];
+
+    in_g->mutable_data<T>(context.GetPlace());
+    auto place = context.GetEigenDevice<Place>();
+    for (int i = 0; i < lod[0].size() - 1; ++i) {
+      auto in_g_t = in_g->Slice<T>(static_cast<int>(lod[0][i]),
+                                   static_cast<int>(lod[0][i + 1]));
+      auto out_g_t = out_g->Slice<T>(i, i + 1);
+      int64_t h = static_cast<int64_t>(lod[0][i + 1] - lod[0][i]);
+      auto in_g_e = EigenMatrix<T>::From(in_g_t, {h, w});
+      auto out_g_e = EigenMatrix<T>::From(out_g_t, {1, w});
+      Eigen::DSizes<int, 2> bcast(h, w);
+      in_g_e.device(place) = (out_g_e / static_cast<T>(h)).broadcast(bcast);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/operators/sgd_op.cc

@@ -23,10 +23,11 @@ class SGDOp : public framework::OperatorWithKernel {
 
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
-    PADDLE_ENFORCE(
-        ctx.Input<Tensor>("param")->dims() == ctx.Input<Tensor>("grad")->dims(),
+    PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("param")->dims(),
+                      ctx.Input<Tensor>("grad")->dims(),
                       "Two input of SGD Op's dimension must be same.");
-    ctx.Output<Tensor>("param_out")->Resize(ctx.Input<Tensor>("param")->dims());
+    ctx.Output<framework::LoDTensor>("param_out")
+        ->Resize(ctx.Input<Tensor>("param")->dims());
   }
 };
 

paddle/operators/sigmoid_op.cc

@@ -23,7 +23,8 @@ class SigmoidOp : public framework::OperatorWithKernel {
 
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
-    ctx.Output<Tensor>("Y")->Resize(ctx.Input<Tensor>("X")->dims());
+    ctx.Output<framework::LoDTensor>("Y")->Resize(
+        ctx.Input<Tensor>("X")->dims());
   }
 };
 
@@ -44,7 +45,7 @@ class SigmoidOpGrad : public framework::OperatorWithKernel {
 
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
-    ctx.Output<Tensor>(framework::GradVarName("X"))
+    ctx.Output<framework::LoDTensor>(framework::GradVarName("X"))
         ->Resize(ctx.Input<Tensor>("Y")->dims());
   }
 };

paddle/operators/softmax_op.cc

@@ -25,7 +25,8 @@ class SoftmaxOp : public framework::OperatorWithKernel {
   void InferShape(const framework::InferShapeContext &ctx) const override {
     PADDLE_ENFORCE(ctx.Input<Tensor>("X")->dims().size() == 2UL,
                    "The input of softmax op must be a matrix.");
-    ctx.Output<Tensor>("Y")->Resize(ctx.Input<Tensor>("X")->dims());
+    ctx.Output<framework::LoDTensor>("Y")->Resize(
+        ctx.Input<Tensor>("X")->dims());
   }
 };
 
@@ -71,7 +72,7 @@ class SoftmaxOpGrad : public framework::OperatorWithKernel {
                       ctx.Input<Tensor>(framework::GradVarName("Y"))->dims(),
                       "Input(Y) and its gradients should have a same shape.");
 
-    ctx.Output<Tensor>(framework::GradVarName("X"))
+    ctx.Output<framework::LoDTensor>(framework::GradVarName("X"))
         ->Resize(ctx.Input<Tensor>("X")->dims());
   }
 };

paddle/operators/squared_l2_distance_op.cc

@@ -48,9 +48,9 @@ class SquaredL2DistanceOp : public framework::OperatorWithKernel {
                    "First dimension of target must be equal to input "
                    "or to 1.");
 
-    ctx.Output<Tensor>("sub_result")
+    ctx.Output<framework::LoDTensor>("sub_result")
         ->Resize({x_dims[0], x->numel() / x_dims[0]});
-    ctx.Output<Tensor>("Out")->Resize({x_dims[0], 1});
+    ctx.Output<framework::LoDTensor>("Out")->Resize({x_dims[0], 1});
   }
 };
 
@@ -94,8 +94,10 @@ class SquaredL2DistanceGradOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_EQ(out_dims[1], 1,
                       "Second dimension of output gradient "
                       "must be 1.");
-    auto* x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
-    auto* y_grad = ctx.Output<Tensor>(framework::GradVarName("Y"));
+    auto* x_grad =
+        ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
+    auto* y_grad =
+        ctx.Output<framework::LoDTensor>(framework::GradVarName("Y"));
     if (x_grad) x_grad->Resize(x_dims);
     if (y_grad) y_grad->Resize(y_dims);
   }

paddle/operators/sum_op.cc

@@ -23,7 +23,7 @@ class SumOp : public framework::OperatorWithKernel {
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
     auto ins = ctx.MultiInput<framework::Tensor>("X");
-    auto *out = ctx.Output<framework::Tensor>("Out");
+    auto *out = ctx.Output<framework::LoDTensor>("Out");
     int N = ins.size();
 
     auto in_dim = ins[0]->dims();
@@ -55,7 +55,8 @@ class SumGradOp : public framework::OperatorWithKernel {
 
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
-    auto outputs = ctx.MultiOutput<Tensor>(framework::GradVarName("X"));
+    auto outputs =
+        ctx.MultiOutput<framework::LoDTensor>(framework::GradVarName("X"));
     auto dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
     for (auto output : outputs) {
       output->Resize(dims);

paddle/operators/top_k_op.cc

@@ -35,8 +35,8 @@ class TopkOp : public framework::OperatorWithKernel {
 
     framework::DDim dims = input->dims();
     dims[dims.size() - 1] = k;
-    ctx.Output<Tensor>("Out")->Resize(dims);
-    ctx.Output<Tensor>("Indices")->Resize(dims);
+    ctx.Output<framework::LoDTensor>("Out")->Resize(dims);
+    ctx.Output<framework::LoDTensor>("Indices")->Resize(dims);
   }
 };
 

paddle/operators/uniform_random_op.cc

@@ -50,7 +50,7 @@ class UniformRandomOp : public framework::OperatorWithKernel {
   void InferShape(const framework::InferShapeContext& ctx) const override {
     PADDLE_ENFORCE(Attr<float>("min") < Attr<float>("max"),
                    "uniform_random's min must less then max");
-    auto* tensor = ctx.Output<framework::Tensor>("Out");
+    auto* tensor = ctx.Output<framework::LoDTensor>("Out");
     auto dims = Attr<std::vector<int>>("dims");
     std::vector<int64_t> temp;
     temp.reserve(dims.size());

paddle/pybind/pybind.cc

@@ -122,6 +122,8 @@ PYBIND11_PLUGIN(core) {
       });
 
   py::class_<LoDTensor, Tensor>(m, "LoDTensor")
+      .def_buffer(
+          [](Tensor &self) -> py::buffer_info { return CastToPyBuffer(self); })
       .def(
           "__init__",
           [](LoDTensor &instance, const std::vector<std::vector<size_t>> &lod) {
@@ -172,10 +174,11 @@ All parameter, weight, gradient are variables in Paddle.
       .def("set_int",
            [](Variable &var, int val) -> void { *var.GetMutable<int>() = val; })
       .def("get_int", [](const Variable &var) -> int { return var.Get<int>(); })
+      //      .def("get_tensor",
+      //         [](Variable &self) -> Tensor * { return
+      //         self.GetMutable<Tensor>(); },
+      //         py::return_value_policy::reference)
       .def("get_tensor",
-           [](Variable &self) -> Tensor * { return self.GetMutable<Tensor>(); },
-           py::return_value_policy::reference)
-      .def("get_lod_tensor",
            [](Variable &self) -> LoDTensor * {
              return self.GetMutable<LoDTensor>();
            },

paddle/pybind/tensor_py.h

@@ -42,7 +42,6 @@ template <size_t I, typename... ARGS>
 struct CastToPyBufferImpl<true, I, ARGS...> {
   using CUR_TYPE = typename std::tuple_element<I, std::tuple<ARGS...>>::type;
   py::buffer_info operator()(framework::Tensor &tensor) {
-    LOG(INFO) << "---- CastToPyBufferImpl -----";
     if (std::type_index(typeid(CUR_TYPE)) == tensor.holder_->type()) {
       auto dim_vec = framework::vectorize(tensor.dims());
       std::vector<size_t> dims_outside;

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

@@ -4,7 +4,7 @@ import numpy
 
 
 class TestTensor(unittest.TestCase):
-    def not_test_int_tensor(self):
+    def test_int_tensor(self):
         scope = core.Scope()
         var = scope.new_var("test_tensor")
         place = core.CPUPlace()
@@ -23,7 +23,7 @@ class TestTensor(unittest.TestCase):
         self.assertEqual(1, tensor_array_2[3, 9])
         self.assertEqual(2, tensor_array_2[19, 11])
 
-    def not_test_float_tensor(self):
+    def test_float_tensor(self):
         scope = core.Scope()
         var = scope.new_var("test_tensor")
         place = core.CPUPlace()
@@ -44,28 +44,19 @@ class TestTensor(unittest.TestCase):
         self.assertAlmostEqual(2.0, tensor_array_2[19, 11])
 
     def test_int_lod_tensor(self):
-        places = [core.CPUPlace(), core.GPUPlace(0)]
-        for place in places:
+        place = core.CPUPlace()
         scope = core.Scope()
-            #var = scope.new_var("test_tensor")
         var_lod = scope.new_var("test_lod_tensor")
-
-            # tensor = var.get_tensor()
-            lod_tensor = var_lod.get_lod_tensor()
+        lod_tensor = var_lod.get_tensor()
 
         lod_tensor.set_dims([4, 4, 6])
         lod_tensor.alloc_int(place)
-            print lod_tensor
         array = numpy.array(lod_tensor)
-            print "---- array ----", array
         array[0, 0, 0] = 3
         array[3, 3, 5] = 10
         lod_tensor.set(array, place)
-
-            # lod_tensor.set_tensor(tensor)
         lod_tensor.set_lod([[0, 2, 4]])
 
-            # lod_v = numpy.array(lod_tensor.tensor())
         lod_v = numpy.array(lod_tensor)
         self.assertTrue(numpy.alltrue(array == lod_v))
 
@@ -74,28 +65,22 @@ class TestTensor(unittest.TestCase):
         self.assertEqual(2, lod[0][1])
         self.assertEqual(4, lod[0][2])
 
-    def not_test_float_lod_tensor(self):
-        places = [core.CPUPlace(), core.GPUPlace(0)]
-        for place in places:
+    def test_float_lod_tensor(self):
+        place = core.CPUPlace()
         scope = core.Scope()
-            var = scope.new_var("test_tensor")
         var_lod = scope.new_var("test_lod_tensor")
 
-            tensor = var.get_tensor()
-            lod_tensor = var_lod.get_lod_tensor()
+        lod_tensor = var_lod.get_tensor()
+        lod_tensor.set_dims([5, 2, 3, 4])
+        lod_tensor.alloc_float(place)
 
-            tensor.set_dims([5, 2, 3, 4])
-            tensor.alloc_float(place)
-
-            tensor_array = numpy.array(tensor)
+        tensor_array = numpy.array(lod_tensor)
         self.assertEqual((5, 2, 3, 4), tensor_array.shape)
         tensor_array[0, 0, 0, 0] = 1.0
         tensor_array[0, 0, 0, 1] = 2.0
-            tensor.set(tensor_array, place)
-
-            lod_tensor.set_tensor(tensor)
+        lod_tensor.set(tensor_array, place)
 
-            lod_v = numpy.array(lod_tensor.tensor())
+        lod_v = numpy.array(lod_tensor)
         self.assertAlmostEqual(1.0, lod_v[0, 0, 0, 0])
         self.assertAlmostEqual(2.0, lod_v[0, 0, 0, 1])
         self.assertEqual(len(lod_tensor.lod()), 0)
@@ -105,21 +90,20 @@ class TestTensor(unittest.TestCase):
         lod = lod_tensor.lod()
         self.assertListEqual(lod_py, lod)
 
-    def not_test_lod_tensor_init(self):
+    def test_lod_tensor_init(self):
         scope = core.Scope()
-        var = scope.new_var("test_tensor")
         place = core.CPUPlace()
-        tensor = var.get_tensor()
-        tensor.set_dims([5, 2, 3, 4])
-        tensor.alloc_float(place)
-        tensor_array = numpy.array(tensor)
+        lod_py = [[0, 2, 5], [0, 2, 4, 5]]
+        lod_tensor = core.LoDTensor(lod_py)
+
+        lod_tensor.set_dims([5, 2, 3, 4])
+        lod_tensor.alloc_float(place)
+        tensor_array = numpy.array(lod_tensor)
         tensor_array[0, 0, 0, 0] = 1.0
         tensor_array[0, 0, 0, 1] = 2.0
-        tensor.set(tensor_array, place)
-        lod_py = [[0, 2, 5], [0, 2, 4, 5]]
+        lod_tensor.set(tensor_array, place)
 
-        lod_tensor = core.LoDTensor(lod_py, tensor)
-        lod_v = numpy.array(lod_tensor.tensor())
+        lod_v = numpy.array(lod_tensor)
         self.assertAlmostEqual(1.0, lod_v[0, 0, 0, 0])
         self.assertAlmostEqual(2.0, lod_v[0, 0, 0, 1])
         self.assertListEqual(lod_py, lod_tensor.lod())