Merge branch 'develop' of https://github.com/PaddlePaddle/paddle into enhance-include-pool

paddle/framework/shape_inference.cc

@@ -22,6 +22,12 @@ std::vector<framework::DDim> InferShapeContext::GetInputsDim(
   return GetDims(names);
 }
 
+DDim InferShapeContext::GetInputsElementDim(const std::string &name,
+                                            int idx) const {
+  const std::vector<std::string> &names = Inputs(name);
+  return this->GetDim(names[idx]);
+}
+
 void InferShapeContext::SetOutputsDim(
     const std::string &name, const std::vector<framework::DDim> &dims) {
   auto &names = Outputs(name);

paddle/framework/shape_inference.h

@@ -37,6 +37,7 @@ class InferShapeContext {
   virtual framework::DDim GetInputDim(const std::string &name) const = 0;
 
   std::vector<framework::DDim> GetInputsDim(const std::string &name) const;
+  DDim GetInputsElementDim(const std::string &name, int idx) const;
 
   virtual void SetOutputDim(const std::string &name, const DDim &dim) = 0;
   void SetOutputsDim(const std::string &name,

paddle/operators/tensor_array_read_write_op.cc

@@ -37,9 +37,15 @@ class WriteToArrayOp : public ArrayOp {
                << " to " << offset + 1;
       out->resize(offset + 1);
     }
-    auto *out_tensor = &out->at(offset);
-    CopyFrom(x_tensor, dev_ctx.GetPlace(), dev_ctx, out_tensor);
-    out_tensor->set_lod(x_tensor.lod());
+    if (x_tensor.memory_size() > 0) {
+      auto *out_tensor = &out->at(offset);
+      CopyFrom(x_tensor, dev_ctx.GetPlace(), dev_ctx, out_tensor);
+      out_tensor->set_lod(x_tensor.lod());
+    } else {
+      VLOG(10) << "WARNING: The input tensor 'x_tensor' holds no memory, so "
+                  "nothing has been written to output array["
+               << offset << "].";
+    }
   }
 };
 

paddle/operators/while_op.cc

@@ -287,7 +287,6 @@ class WhileGradOpShapeInference : public framework::InferShapeBase {
 
     auto p_names = ctx->Inputs(kParameters);
     auto pg_names = ctx->Outputs(kParamGrads);
-    auto dims = ctx->GetInputsDim(kParameters);
     auto var_types = ctx->GetInputsVarType(kParameters);
     std::vector<std::string> names_to_set;
     std::vector<framework::DDim> dims_to_set;
@@ -295,13 +294,14 @@ class WhileGradOpShapeInference : public framework::InferShapeBase {
       if (pg_names[i] == framework::kEmptyVarName) {
         continue;
       }
+      auto dims = ctx->GetInputsElementDim(kParameters, i);
       if (var_types[i] == framework::VarDesc::LOD_TENSOR) {
         names_to_set.push_back(pg_names[i]);
-        dims_to_set.push_back(dims[i]);
+        dims_to_set.push_back(dims);
       } else if (var_types[i] == framework::VarDesc::LOD_TENSOR_ARRAY) {
         // not sure how to set the dim of LOD_TENSOR_ARRAY
         names_to_set.push_back(pg_names[i]);
-        dims_to_set.push_back(dims[i]);
+        dims_to_set.push_back(dims);
       }
     }
     ctx->SetDims(names_to_set, dims_to_set);

python/paddle/v2/fluid/layers.py

@@ -185,6 +185,7 @@ def data(name,
          shape,
          append_batch_size=True,
          dtype='float32',
+         lod_level=0,
          type=core.VarDesc.VarType.LOD_TENSOR,
          main_program=None,
          startup_program=None,
@@ -198,6 +199,7 @@ def data(name,
        append_batch_size: Whether or not to append the data as a batch.
        dtype: The type of data : float32, float_16, int etc
        type: The output type. By default it is LOD_TENSOR.
+       lod_level(int): The LoD Level. 0 means the input data is not a sequence.
        main_program: Name of the main program that calls this
        startup_program: Name of the startup program
        stop_gradient: A boolean that mentions whether gradient should flow.
@@ -228,7 +230,8 @@ def data(name,
         shape=shape,
         dtype=dtype,
         type=type,
-        stop_gradient=stop_gradient)
+        stop_gradient=stop_gradient,
+        lod_level=lod_level)
 
 
 def create_tensor(dtype, name=None, main_program=None, startup_program=None):