Feature/lod tensor array (#5379)

* Use stable_sort in lod_rank_table

It is easy to debug and test when use `stable_sort`and the time
complexity is not changed.

* Add LoDTensorArray

paddle/framework/executor.cc

@@ -23,6 +23,7 @@ limitations under the License. */
 #include "paddle/framework/feed_fetch_type.h"
 #include "paddle/framework/lod_rank_table.h"
 #include "paddle/framework/lod_tensor.h"
+#include "paddle/framework/lod_tensor_array.h"
 #include "paddle/framework/op_registry.h"
 #include "paddle/framework/scope.h"
 
@@ -73,6 +74,8 @@ static void CreateTensor(Variable* var, VarDesc::VarType var_type) {
     var->GetMutable<std::vector<framework::Scope>>();
   } else if (var_type == VarDesc::LOD_RANK_TABLE) {
     var->GetMutable<LoDRankTable>();
+  } else if (var_type == VarDesc::LOD_TENSOR_ARRAY) {
+    var->GetMutable<LoDTensorArray>();
   } else {
     PADDLE_THROW(
         "Variable type %d is not in "

paddle/framework/framework.proto

@@ -109,6 +109,11 @@ message LoDTensorDesc {
   optional int32 lod_level = 2 [ default = 0 ];
 }
 
+message LoDTensorArrayDesc {
+  required TensorDesc tensor = 1;
+  optional int32 lod_level = 2 [ default = 0 ];
+}
+
 message VarDesc {
   enum VarType {
     LOD_TENSOR = 1;
@@ -117,11 +122,13 @@ message VarDesc {
     FETCH_LIST = 4;
     STEP_SCOPES = 5;
     LOD_RANK_TABLE = 6;
+    LOD_TENSOR_ARRAY = 7;
   }
   required string name = 1;
   required VarType type = 2;
   optional LoDTensorDesc lod_tensor = 3;
   optional TensorDesc selected_rows = 4;
+  optional LoDTensorArrayDesc tensor_array = 6;
   optional bool persistable = 5 [ default = false ];
 }
 

paddle/framework/lod_tensor_array.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+   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 <vector>
+#include "paddle/framework/lod_tensor.h"
+
+namespace paddle {
+namespace framework {
+using LoDTensorArray = std::vector<LoDTensor>;
+}
+}  // namespace paddle

paddle/framework/var_desc.cc

@@ -37,13 +37,27 @@ std::vector<int64_t> VarDescBind::Shape() const {
 DataType VarDescBind::GetDataType() const { return tensor_desc().data_type(); }
 
 void VarDescBind::SetLoDLevel(int32_t lod_level) {
-  PADDLE_ENFORCE(desc_.type() == VarDesc::LOD_TENSOR);
+  switch (desc_.type()) {
+    case VarDesc::LOD_TENSOR:
       desc_.mutable_lod_tensor()->set_lod_level(lod_level);
+      break;
+    case VarDesc::LOD_TENSOR_ARRAY:
+      desc_.mutable_tensor_array()->set_lod_level(lod_level);
+      break;
+    default:
+      PADDLE_THROW("Tensor type=%d does not support LoDLevel", desc_.type());
+  }
 }
 
 int32_t VarDescBind::GetLodLevel() const {
-  PADDLE_ENFORCE(desc_.type() == VarDesc::LOD_TENSOR);
+  switch (desc_.type()) {
+    case VarDesc::LOD_TENSOR:
       return desc_.lod_tensor().lod_level();
+    case VarDesc::LOD_TENSOR_ARRAY:
+      return desc_.tensor_array().lod_level();
+    default:
+      PADDLE_THROW("Tensor type=%d does not support LoDLevel", desc_.type());
+  }
 }
 
 const TensorDesc &VarDescBind::tensor_desc() const {
@@ -53,6 +67,8 @@ const TensorDesc &VarDescBind::tensor_desc() const {
       return desc_.selected_rows();
     case VarDesc::LOD_TENSOR:
       return desc_.lod_tensor().tensor();
+    case VarDesc::LOD_TENSOR_ARRAY:
+      return desc_.tensor_array().tensor();
     default:
       PADDLE_THROW("Unexpected branch.");
   }
@@ -66,6 +82,8 @@ TensorDesc *VarDescBind::mutable_tensor_desc() {
       return desc_.mutable_selected_rows();
     case VarDesc::LOD_TENSOR:
       return desc_.mutable_lod_tensor()->mutable_tensor();
+    case VarDesc::LOD_TENSOR_ARRAY:
+      return desc_.mutable_tensor_array()->mutable_tensor();
     default:
       PADDLE_THROW("Unexpected branch.");
   }

paddle/pybind/protobuf.cc

@@ -239,7 +239,8 @@ void BindVarDsec(py::module &m) {
       .value("FEED_MINIBATCH", VarDesc::FEED_MINIBATCH)
       .value("FETCH_LIST", VarDesc::FETCH_LIST)
       .value("STEP_SCOPES", VarDesc::STEP_SCOPES)
-      .value("LOD_RANK_TABLE", VarDesc::LOD_RANK_TABLE);
+      .value("LOD_RANK_TABLE", VarDesc::LOD_RANK_TABLE)
+      .value("LOD_TENSOR_ARRAY", VarDesc::LOD_TENSOR_ARRAY);
 }
 
 void BindOpDesc(py::module &m) {

paddle/pybind/pybind.cc

@@ -23,6 +23,7 @@ limitations under the License. */
 #include "paddle/framework/framework.pb.h"
 #include "paddle/framework/lod_rank_table.h"
 #include "paddle/framework/lod_tensor.h"
+#include "paddle/framework/lod_tensor_array.h"
 #include "paddle/framework/prune.h"
 #include "paddle/framework/selected_rows.h"
 #include "paddle/framework/tensor_array.h"
@@ -233,6 +234,9 @@ All parameter, weight, gradient are variables in Paddle.
              return self.GetMutable<SelectedRows>();
            },
            py::return_value_policy::reference)
+      .def("get_lod_tensor_array",
+           [](Variable &self) { return self.GetMutable<LoDTensorArray>(); },
+           py::return_value_policy::reference)
 #ifdef PADDLE_WITH_CUDA
       .def("get_communicator",
            [](Variable &self) -> platform::Communicator * {
@@ -505,6 +509,23 @@ All parameter, weight, gradient are variables in Paddle.
         return res;
       });
 
+  py::class_<LoDTensorArray>(m, "LoDTensorArray")
+      .def("__getitem__",
+           [](LoDTensorArray &self, size_t i) { return &self.at(i); },
+           py::return_value_policy::reference)
+      .def("__len__", [](LoDTensorArray &self) { return self.size(); })
+      .def("__setitem__",
+           [](LoDTensorArray &self, size_t i, const LoDTensor &t) {
+             PADDLE_ENFORCE_LT(i, self.size());
+             self[i].ShareDataWith(t);
+             self[i].set_lod(t.lod());
+           })
+      .def("append", [](LoDTensorArray &self, const LoDTensor &t) {
+        self.emplace_back();
+        self.back().ShareDataWith(t);
+        self.back().set_lod(t.lod());
+      });
+
   m.def("op_support_gpu", OpSupportGPU);
 #ifdef PADDLE_WITH_CUDA
   m.def("get_cuda_device_count", platform::GetCUDADeviceCount);

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

+import unittest
+import paddle.v2.framework.core as core
+import numpy
+
+
+class TestLoDTensorArray(unittest.TestCase):
+    def test_get_set(self):
+        scope = core.Scope()
+        arr = scope.var('tmp_lod_tensor_array')
+        tensor_array = arr.get_lod_tensor_array()
+        self.assertEqual(0, len(tensor_array))
+        cpu = core.CPUPlace()
+        for i in xrange(10):
+            t = core.LoDTensor()
+            t.set(numpy.array([i], dtype='float32'), cpu)
+            t.set_lod([[0, 1]])
+            tensor_array.append(t)
+
+        self.assertEqual(10, len(tensor_array))
+
+        for i in xrange(10):
+            t = tensor_array[i]
+            self.assertEqual(numpy.array(t), numpy.array([i], dtype='float32'))
+            self.assertEqual([[0, 1]], t.lod())
+
+            t = core.LoDTensor()
+            t.set(numpy.array([i + 10], dtype='float32'), cpu)
+            t.set_lod([[0, 2]])
+            tensor_array[i] = t
+            t = tensor_array[i]
+            self.assertEqual(
+                numpy.array(t), numpy.array(
+                    [i + 10], dtype='float32'))
+            self.assertEqual([[0, 2]], t.lod())
+
+
+if __name__ == '__main__':
+    unittest.main()