Add seq2seq api related code (#19820)

paddle/fluid/API.spec

@@ -306,6 +306,7 @@ paddle.fluid.layers.deformable_roi_pooling (ArgSpec(args=['input', 'rois', 'tran
 paddle.fluid.layers.filter_by_instag (ArgSpec(args=['ins', 'ins_tag', 'filter_tag', 'is_lod'], varargs=None, keywords=None, defaults=None), ('document', '7703a2088af8de4128b143ff1164ca4a'))
 paddle.fluid.layers.shard_index (ArgSpec(args=['input', 'index_num', 'nshards', 'shard_id', 'ignore_value'], varargs=None, keywords=None, defaults=(-1,)), ('document', '3c6b30e9cd57b38d4a5fa1ade887f779'))
 paddle.fluid.layers.hard_swish (ArgSpec(args=['x', 'threshold', 'scale', 'offset', 'name'], varargs=None, keywords=None, defaults=(6.0, 6.0, 3.0, None)), ('document', 'bd763b9ca99239d624c3cb4626e3627a'))
+paddle.fluid.layers.gather_tree (ArgSpec(args=['ids', 'parents'], varargs=None, keywords=None, defaults=None), ('document', '201b54fa7512305078c70a6610beaead'))
 paddle.fluid.layers.mse_loss (ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None), ('document', '88b967ef5132567396062d5d654b3064'))
 paddle.fluid.layers.uniform_random (ArgSpec(args=['shape', 'dtype', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', -1.0, 1.0, 0)), ('document', '34e7c1ff0263baf9551000b6bb3bc47e'))
 paddle.fluid.layers.data (ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True)), ('document', '9d7806e31bdf727c1a23b8782a09b545'))
@@ -318,11 +319,11 @@ paddle.fluid.layers.create_tensor (ArgSpec(args=['dtype', 'name', 'persistable']
 paddle.fluid.layers.create_parameter (ArgSpec(args=['shape', 'dtype', 'name', 'attr', 'is_bias', 'default_initializer'], varargs=None, keywords=None, defaults=(None, None, False, None)), ('document', '727aa63c061919bee38547fb126d9428'))
 paddle.fluid.layers.create_global_var (ArgSpec(args=['shape', 'value', 'dtype', 'persistable', 'force_cpu', 'name'], varargs=None, keywords=None, defaults=(False, False, None)), ('document', 'fa7f74cfb940521cc9fdffabc83debbf'))
 paddle.fluid.layers.cast (ArgSpec(args=['x', 'dtype'], varargs=None, keywords=None, defaults=None), ('document', '45df178cbd8c302f92c30ebdaaa6fa8a'))
-paddle.fluid.layers.tensor_array_to_tensor (ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', 'dd7d2f1e12a8a4225d017209866e5621'))
+paddle.fluid.layers.tensor_array_to_tensor (ArgSpec(args=['input', 'axis', 'name', 'use_stack'], varargs=None, keywords=None, defaults=(1, None, False)), ('document', '4aa82374218ccf593bb8011df79c71e3'))
 paddle.fluid.layers.concat (ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(0, None)), ('document', 'ec7d6e716fb29ef1e73e1e3efa5ca46b'))
 paddle.fluid.layers.sums (ArgSpec(args=['input', 'out'], varargs=None, keywords=None, defaults=(None,)), ('document', '5df743d578638cd2bbb9369499b44af4'))
 paddle.fluid.layers.assign (ArgSpec(args=['input', 'output'], varargs=None, keywords=None, defaults=(None,)), ('document', '8bd94aef4e123986d9a8c29f67b5532b'))
-paddle.fluid.layers.fill_constant_batch_size_like (ArgSpec(args=['input', 'shape', 'dtype', 'value', 'input_dim_idx', 'output_dim_idx'], varargs=None, keywords=None, defaults=(0, 0)), ('document', '37a288e4400f6d5510e982827461c11b'))
+paddle.fluid.layers.fill_constant_batch_size_like (ArgSpec(args=['input', 'shape', 'dtype', 'value', 'input_dim_idx', 'output_dim_idx', 'force_cpu'], varargs=None, keywords=None, defaults=(0, 0, False)), ('document', '2bb57637664173fee5f654e55896aec6'))
 paddle.fluid.layers.fill_constant (ArgSpec(args=['shape', 'dtype', 'value', 'force_cpu', 'out'], varargs=None, keywords=None, defaults=(False, None)), ('document', '66e1e468666dd47e5b2715226cebeac0'))
 paddle.fluid.layers.argmin (ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,)), ('document', '53629e27597e5dfb7020aac5bc639ebb'))
 paddle.fluid.layers.argmax (ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,)), ('document', 'd9a89fbedbaebd5f65897ac75ee636f3'))
@@ -467,6 +468,39 @@ paddle.fluid.layers.MultivariateNormalDiag.entropy (ArgSpec(args=['self'], varar
 paddle.fluid.layers.MultivariateNormalDiag.kl_divergence (ArgSpec(args=['self', 'other'], varargs=None, keywords=None, defaults=None), ('document', 'd9190d29dbd54c81f747a6436c35f062'))
 paddle.fluid.layers.MultivariateNormalDiag.log_prob (ArgSpec(args=['self', 'value'], varargs=None, keywords=None, defaults=None), ('document', 'c0edd2e2fc76711477b32dc4da9de768'))
 paddle.fluid.layers.MultivariateNormalDiag.sample (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '08a2bbcaa20ee176ee7ec3d05737a0f6'))
+paddle.fluid.layers.RNNCell ('paddle.fluid.layers.rnn.RNNCell', ('document', '2c3a2d3ecb4a3cec130395e7df0bd5c9'))
+paddle.fluid.layers.RNNCell.__init__ 
+paddle.fluid.layers.RNNCell.call (ArgSpec(args=['self', 'inputs', 'states'], varargs=None, keywords='kwargs', defaults=None), ('document', '3ac714b638258c520d66f682be67b658'))
+paddle.fluid.layers.RNNCell.get_initial_states (ArgSpec(args=['self', 'batch_ref', 'shape', 'dtype', 'init_value'], varargs=None, keywords=None, defaults=(None, None, 0)), ('document', '003d1b4c99128f798ac0b0eecc81c489'))
+paddle.fluid.layers.GRUCell ('paddle.fluid.layers.rnn.GRUCell', ('document', '7b2902a91258c4688a879805290adc00'))
+paddle.fluid.layers.GRUCell.__init__ (ArgSpec(args=['self', 'hidden_size', 'param_attr', 'bias_attr', 'gate_activation', 'activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, 'float32', 'GRUCell')), ('document', '3624a6c93b4a999d0d809eb1a66d272e'))
+paddle.fluid.layers.GRUCell.call (ArgSpec(args=['self', 'inputs', 'states'], varargs=None, keywords=None, defaults=None), ('document', '6094ab09a56c732c76abb5105327ea54'))
+paddle.fluid.layers.GRUCell.get_initial_states (ArgSpec(args=['self', 'batch_ref', 'shape', 'dtype', 'init_value'], varargs=None, keywords=None, defaults=(None, None, 0)), ('document', '003d1b4c99128f798ac0b0eecc81c489'))
+paddle.fluid.layers.LSTMCell ('paddle.fluid.layers.rnn.LSTMCell', ('document', '5cbd87bce446ba0f50398ce2772d43e9'))
+paddle.fluid.layers.LSTMCell.__init__ (ArgSpec(args=['self', 'hidden_size', 'param_attr', 'bias_attr', 'gate_activation', 'activation', 'forget_bias', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, 1.0, 'float32', 'LSTMCell')), ('document', '9015961869b436d2739a0347618028e3'))
+paddle.fluid.layers.LSTMCell.call (ArgSpec(args=['self', 'inputs', 'states'], varargs=None, keywords=None, defaults=None), ('document', '9c84a477021e4a7d0a497c1e6a31be2d'))
+paddle.fluid.layers.LSTMCell.get_initial_states (ArgSpec(args=['self', 'batch_ref', 'shape', 'dtype', 'init_value'], varargs=None, keywords=None, defaults=(None, None, 0)), ('document', '003d1b4c99128f798ac0b0eecc81c489'))
+paddle.fluid.layers.Decoder ('paddle.fluid.layers.rnn.Decoder', ('document', '23838bd065fddca1557a6a3368d9e365'))
+paddle.fluid.layers.Decoder.__init__ 
+paddle.fluid.layers.Decoder.finalize (ArgSpec(args=['self', 'outputs', 'final_states', 'sequence_lengths'], varargs=None, keywords=None, defaults=None), ('document', 'cab7fc752a05db18e99258473f50359d'))
+paddle.fluid.layers.Decoder.initialize (ArgSpec(args=['self', 'inits'], varargs=None, keywords=None, defaults=None), ('document', '68cf1846fb58056dbe5a524f1ca9dff5'))
+paddle.fluid.layers.Decoder.step (ArgSpec(args=['self', 'time', 'inputs', 'states'], varargs=None, keywords=None, defaults=None), ('document', '151d0229930b9654689f86c85f7c4c3f'))
+paddle.fluid.layers.BeamSearchDecoder ('paddle.fluid.layers.rnn.BeamSearchDecoder', ('document', 'd7ef0c9229bfe73e0daefcfda24a2635'))
+paddle.fluid.layers.BeamSearchDecoder.OutputWrapper ('paddle.fluid.layers.rnn.OutputWrapper', ('document', 'a7141ebf1fb097fa71006cdd35bdc219'))
+paddle.fluid.layers.BeamSearchDecoder.OutputWrapper.__init__ 
+paddle.fluid.layers.BeamSearchDecoder.OutputWrapper.count T.count(value) -> integer -- return number of occurrences of value
+paddle.fluid.layers.BeamSearchDecoder.OutputWrapper.index T.index(value, [start, [stop]]) -> integer -- return first index of value.
+paddle.fluid.layers.BeamSearchDecoder.StateWrapper ('paddle.fluid.layers.rnn.StateWrapper', ('document', '157731f37c88ea01bc746653125a41c8'))
+paddle.fluid.layers.BeamSearchDecoder.StateWrapper.__init__ 
+paddle.fluid.layers.BeamSearchDecoder.StateWrapper.count T.count(value) -> integer -- return number of occurrences of value
+paddle.fluid.layers.BeamSearchDecoder.StateWrapper.index T.index(value, [start, [stop]]) -> integer -- return first index of value.
+paddle.fluid.layers.BeamSearchDecoder.__init__ (ArgSpec(args=['self', 'cell', 'start_token', 'end_token', 'beam_size', 'embedding_fn', 'output_fn'], varargs=None, keywords=None, defaults=(None, None)), ('document', '68951eaed573ec47c17a43155514b2f1'))
+paddle.fluid.layers.BeamSearchDecoder.finalize (ArgSpec(args=['self', 'outputs', 'final_states', 'sequence_lengths'], varargs=None, keywords=None, defaults=None), ('document', '9a7f0a8fc5802bf860f2ac960466fb45'))
+paddle.fluid.layers.BeamSearchDecoder.initialize (ArgSpec(args=['self', 'initial_cell_states'], varargs=None, keywords=None, defaults=None), ('document', '01ee508a9615e2483fe6ddcf14d5fa25'))
+paddle.fluid.layers.BeamSearchDecoder.step (ArgSpec(args=['self', 'time', 'inputs', 'states'], varargs=None, keywords='kwargs', defaults=None), ('document', '35ee583c3c0fe7cceeafa289ed3374bd'))
+paddle.fluid.layers.BeamSearchDecoder.tile_beam_merge_with_batch (ArgSpec(args=['x', 'beam_size'], varargs=None, keywords=None, defaults=None), ('document', 'ce7ffacba6f56f57acbf5d4dd82fe04d'))
+paddle.fluid.layers.rnn (ArgSpec(args=['cell', 'inputs', 'initial_states', 'sequence_length', 'time_major', 'is_reverse'], varargs=None, keywords='kwargs', defaults=(None, None, False, False)), ('document', 'c36ade777ff43d2ba5542079b66a012b'))
+paddle.fluid.layers.dynamic_decode (ArgSpec(args=['decoder', 'inits', 'max_step_num', 'output_time_major'], varargs=None, keywords='kwargs', defaults=(None, None, False)), ('document', '55b44de9d290c0c2ad8fdd635e6ab575'))
 paddle.fluid.contrib.InitState ('paddle.fluid.contrib.decoder.beam_search_decoder.InitState', ('document', '3afd1f84232718e628e9e566941c5f05'))
 paddle.fluid.contrib.InitState.__init__ (ArgSpec(args=['self', 'init', 'shape', 'value', 'init_boot', 'need_reorder', 'dtype'], varargs=None, keywords=None, defaults=(None, None, 0.0, None, False, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.contrib.StateCell ('paddle.fluid.contrib.decoder.beam_search_decoder.StateCell', ('document', 'ecd0066c02867d445d7b461e28220c50'))

paddle/fluid/operators/assign_op.cc

@@ -154,10 +154,12 @@ REGISTER_OPERATOR(assign, ops::AssignOp, ops::AssignGradMaker,
                   ops::AssignOpProtoMaker, ops::AssignOpInplaceInferer);
 REGISTER_OP_CPU_KERNEL_FUNCTOR(assign, float, ops::AssignKernel, double,
                                ops::AssignKernel, int, ops::AssignKernel,
-                               int64_t, ops::AssignKernel);
+                               int64_t, ops::AssignKernel, bool,
+                               ops::AssignKernel);
 
 #ifdef PADDLE_WITH_CUDA
 REGISTER_OP_CUDA_KERNEL_FUNCTOR(assign, float, ops::AssignKernel, double,
                                 ops::AssignKernel, int, ops::AssignKernel,
-                                int64_t, ops::AssignKernel);
+                                int64_t, ops::AssignKernel, bool,
+                                ops::AssignKernel);
 #endif

paddle/fluid/operators/fill_constant_batch_size_like_op.cc

@@ -38,6 +38,11 @@ class FillConstantBatchSizeLikeOpMaker : public BatchSizeLikeOpMaker {
         .SetDefault(framework::proto::VarType::FP32);
     AddAttr<float>("value", "default 0. The value to be filled")
         .SetDefault(0.0f);
+    AddAttr<bool>("force_cpu",
+                  "(bool, default false) Force fill output variable to cpu "
+                  "memory. Otherwise, fill output variable to the running "
+                  "device")
+        .SetDefault(false);
     AddComment(R"DOC(
 This function creates a tensor of specified *shape*, *dtype* and batch size,
 and initializes this with a constant supplied in *value*. The batch size is
@@ -65,4 +70,6 @@ REGISTER_OP_CPU_KERNEL(
     ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUDeviceContext,
                                            int>,
     ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUDeviceContext,
-                                           int64_t>);
+                                           int64_t>,
+    ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUDeviceContext,
+                                           bool>);

paddle/fluid/operators/fill_constant_batch_size_like_op.cu.cc

@@ -25,4 +25,6 @@ REGISTER_OP_CUDA_KERNEL(
     ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CUDADeviceContext,
                                            int>,
     ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CUDADeviceContext,
-                                           int64_t>);
+                                           int64_t>,
+    ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CUDADeviceContext,
+                                           bool>);

paddle/fluid/operators/fill_constant_batch_size_like_op.h

@@ -23,6 +23,11 @@ template <typename DeviceContext, typename T>
 class FillConstantBatchSizeLikeOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    auto data_type =
+        static_cast<framework::proto::VarType::Type>(ctx.Attr<int>("dtype"));
+    auto value = ctx.Attr<float>("value");
+    auto force_cpu = ctx.Attr<bool>("force_cpu");
+
     auto* out = ctx.Output<framework::Tensor>("Out");
     auto* in = ctx.Input<framework::LoDTensor>("Input");
     if (in->lod().size() && ctx.Attr<int>("input_dim_idx") == 0) {
@@ -32,12 +37,16 @@ class FillConstantBatchSizeLikeOpKernel : public framework::OpKernel<T> {
       odims[output_dim_idx] = static_cast<int>(in->lod().back().size()) - 1;
       out->mutable_data<T>(odims, ctx.GetPlace());
     }
-    out->mutable_data<T>(ctx.GetPlace());
-    auto value = ctx.Attr<float>("value");
 
-    math::SetConstant<DeviceContext, T> setter;
-    setter(ctx.template device_context<DeviceContext>(), out,
-           static_cast<T>(value));
+    if (force_cpu) {
+      out->mutable_data(platform::CPUPlace(), data_type);
+    } else {
+      out->mutable_data(ctx.GetPlace(), data_type);
+    }
+
+    platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
+    auto& dev_ctx = *pool.Get(ctx.GetPlace());
+    math::set_constant(dev_ctx, out, value);
   }
 };
 

paddle/fluid/operators/fill_constant_op.cu.cc

@@ -19,4 +19,5 @@ REGISTER_OP_CUDA_KERNEL(fill_constant, ops::FillConstantKernel<float>,
                         ops::FillConstantKernel<double>,
                         ops::FillConstantKernel<int64_t>,
                         ops::FillConstantKernel<int>,
+                        ops::FillConstantKernel<bool>,
                         ops::FillConstantKernel<paddle::platform::float16>);

paddle/fluid/operators/gather_nd_op.cc

@@ -60,8 +60,13 @@ class GatherNdOp : public framework::OperatorWithKernel {
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
-                                   ctx.device_context());
+    auto* x = ctx.Input<Tensor>("X");
+    const auto& x_type = x->type();
+    return framework::OpKernelType(
+        x_type,
+        x_type == framework::proto::VarType::BOOL
+            ? x->place()  // to be consistent with compare and logical ops
+            : ctx.device_context().GetPlace());
   }
 };
 
@@ -173,7 +178,7 @@ REGISTER_OPERATOR(gather_nd_grad, ops::GatherNdGradOp,
 REGISTER_OP_CPU_KERNEL(gather_nd, ops::GatherNdOpKernel<float>,
                        ops::GatherNdOpKernel<double>,
                        ops::GatherNdOpKernel<int64_t>,
-                       ops::GatherNdOpKernel<int>,
+                       ops::GatherNdOpKernel<int>, ops::GatherNdOpKernel<bool>,
                        ops::GatherNdOpKernel<uint8_t>);
 
 REGISTER_OP_CPU_KERNEL(gather_nd_grad, ops::GatherNdGradOpKernel<float>,

paddle/fluid/operators/gather_nd_op.cu

@@ -95,6 +95,7 @@ REGISTER_OP_CUDA_KERNEL(gather_nd, ops::GatherNdOpCUDAKernel<CUDA, float>,
                         ops::GatherNdOpCUDAKernel<CUDA, double>,
                         ops::GatherNdOpCUDAKernel<CUDA, int64_t>,
                         ops::GatherNdOpCUDAKernel<CUDA, int>,
+                        ops::GatherNdOpCUDAKernel<CUDA, bool>,
                         ops::GatherNdOpCUDAKernel<CUDA, plat::float16>);
 
 REGISTER_OP_CUDA_KERNEL(gather_nd_grad,

paddle/fluid/operators/gather_tree_op.cc

+/* Copyright (c) 2019 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. */
+
+#include "paddle/fluid/operators/gather_tree_op.h"
+
+namespace paddle {
+namespace operators {
+
+class GatherTreeOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Ids"),
+                   "Input(Ids) of GatherTreeOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Parents"),
+                   "Input(Parents) of GatherTreeOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of GatherTreeOp should not be null.");
+
+    auto ids_dims = ctx->GetInputDim("Ids");
+    auto parents_dims = ctx->GetInputDim("Parents");
+    PADDLE_ENFORCE(ids_dims == parents_dims,
+                   "The shape of Input(Parents) must be same with the shape of "
+                   "Input(Ids).");
+    ctx->SetOutputDim("Out", ids_dims);
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(ctx.Input<Tensor>("Ids")->type(),
+                                   ctx.device_context());
+  }
+};
+
+class GatherTreeOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("Ids",
+             "The Tensor with shape [length, batch_size, beam_size] containing "
+             "the selected ids of all time steps.");
+    AddInput("Parents",
+             "The Tensor has the same shape as Ids and contains the parents "
+             "corresponding to selected ids when searching among beams.");
+    AddOutput(
+        "Out",
+        "A Tensor with shape [length, batch_size, beam_size] containing the "
+        "full sequences. The sequences is collected by backtracing from the "
+        "last time step of Ids.");
+    AddComment(R"DOC(
+GatherTree Operator.
+
+Backtrace from the last time step and generate the full sequences by collecting beam search
+selected ids.
+
+)DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(gather_tree, ops::GatherTreeOp, ops::GatherTreeOpMaker);
+REGISTER_OP_CPU_KERNEL(gather_tree, ops::GatherTreeOpKernel<int32_t>,
+                       ops::GatherTreeOpKernel<int64_t>);

paddle/fluid/operators/gather_tree_op.cu

+/* Copyright (c) 2019 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. */
+
+#include <algorithm>
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/gather_tree_op.h"
+
+namespace paddle {
+namespace operators {
+
+#define CUDA_1D_KERNEL_LOOP(i, n)                              \
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
+       i += blockDim.x * gridDim.x)
+
+template <typename T>
+__global__ void GatherTree(const T *ids_data, const T *parents_data,
+                           T *out_data, const int64_t max_length,
+                           const int64_t batch_size, const int64_t beam_size) {
+  CUDA_1D_KERNEL_LOOP(i, batch_size * beam_size) {
+    int batch = i / beam_size;
+    int beam = i % beam_size;
+    auto idx =
+        (max_length - 1) * batch_size * beam_size + batch * beam_size + beam;
+    out_data[idx] = ids_data[idx];
+    auto parent = parents_data[idx];
+    for (int step = max_length - 2; step >= 0; step--) {
+      idx = step * batch_size * beam_size + batch * beam_size;
+      out_data[idx + beam] = ids_data[idx + parent];
+      parent = parents_data[idx + parent];
+    }
+  }
+}
+
+template <typename T>
+class GatherTreeOpCUDAKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    auto *ids = ctx.Input<Tensor>("Ids");
+    auto *parents = ctx.Input<Tensor>("Parents");
+    auto *out = ctx.Output<Tensor>("Out");
+
+    const auto *ids_data = ids->data<T>();
+    const auto *parents_data = parents->data<T>();
+    auto *out_data = out->mutable_data<T>(ctx.GetPlace());
+
+    auto &ids_dims = ids->dims();
+    int64_t max_length = ids_dims[0];
+    int64_t batch_size = ids_dims[1];
+    int64_t beam_size = ids_dims[2];
+
+    auto &dev_ctx = ctx.cuda_device_context();
+
+    const int block = 512;
+    int max_threads =
+        std::min(static_cast<int64_t>(dev_ctx.GetMaxPhysicalThreadCount()),
+                 batch_size * beam_size);
+    const int grid = std::max(max_threads / block, 1);
+    GatherTree<<<grid, block>>>(ids_data, parents_data, out_data, max_length,
+                                batch_size, beam_size);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_CUDA_KERNEL(gather_tree, ops::GatherTreeOpCUDAKernel<int32_t>,
+                        ops::GatherTreeOpCUDAKernel<int64_t>);

paddle/fluid/operators/gather_tree_op.h

+/* Copyright (c) 2019 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 "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename T>
+class GatherTreeOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    auto *ids = ctx.Input<Tensor>("Ids");
+    auto *parents = ctx.Input<Tensor>("Parents");
+    auto *out = ctx.Output<Tensor>("Out");
+
+    const auto *ids_data = ids->data<T>();
+    const auto *parents_data = parents->data<T>();
+    auto *out_data = out->mutable_data<T>(ctx.GetPlace());
+
+    auto &ids_dims = ids->dims();
+    auto max_length = ids_dims[0];
+    auto batch_size = ids_dims[1];
+    auto beam_size = ids_dims[2];
+
+    for (int batch = 0; batch < batch_size; batch++) {
+      for (int beam = 0; beam < beam_size; beam++) {
+        auto idx = (max_length - 1) * batch_size * beam_size +
+                   batch * beam_size + beam;
+        out_data[idx] = ids_data[idx];
+        auto parent = parents_data[idx];
+        for (int step = max_length - 2; step >= 0; step--) {
+          idx = step * batch_size * beam_size + batch * beam_size;
+          out_data[idx + beam] = ids_data[idx + parent];
+          parent = parents_data[idx + parent];
+        }
+      }
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/reduce_ops/reduce_all_op.cc

@@ -14,7 +14,9 @@
 
 #include "paddle/fluid/operators/reduce_ops/reduce_all_op.h"
 
-REGISTER_REDUCE_OP_WITHOUT_GRAD(reduce_all);
+// kernel's device type is decided by input tensor place, to be consistent with
+// compare and logical ops
+REGISTER_REDUCE_OP_WITHOUT_GRAD(reduce_all, UseInputPlace);
 REGISTER_OP_CPU_KERNEL(reduce_all,
                        ops::ReduceKernel<paddle::platform::CPUDeviceContext,
                                          bool, ops::AllFunctor>);

paddle/fluid/operators/reduce_ops/reduce_any_op.cc

@@ -14,7 +14,9 @@
 
 #include "paddle/fluid/operators/reduce_ops/reduce_any_op.h"
 
-REGISTER_REDUCE_OP_WITHOUT_GRAD(reduce_any);
+// kernel's device type is decided by input tensor place, to be consistent with
+// compare and logical ops
+REGISTER_REDUCE_OP_WITHOUT_GRAD(reduce_any, UseInputPlace);
 REGISTER_OP_CPU_KERNEL(reduce_any,
                        ops::ReduceKernel<paddle::platform::CPUDeviceContext,
                                          bool, ops::AnyFunctor>);

paddle/fluid/operators/reduce_ops/reduce_op.h

@@ -223,6 +223,19 @@ class ReduceOp : public framework::OperatorWithKernel {
   }
 };
 
+class ReduceOpUseInputPlace : public ReduceOp {
+ public:
+  using ReduceOp::ReduceOp;
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    framework::OpKernelType kt = OperatorWithKernel::GetExpectedKernelType(ctx);
+    kt.place_ = ctx.Input<framework::LoDTensor>("X")->place();
+    return kt;
+  }
+};
+
 class ReduceGradOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -313,11 +326,11 @@ namespace ops = paddle::operators;
                     paddle::framework::DefaultGradOpDescMaker<true>);    \
   REGISTER_OPERATOR(op_name##_grad, ops::ReduceGradOp)
 
-#define REGISTER_REDUCE_OP_WITHOUT_GRAD(op_name)                         \
+#define REGISTER_REDUCE_OP_WITHOUT_GRAD(op_name, ...)                          \
   class __##op_name##Maker__ : public ops::ReduceOpMaker {                     \
    protected:                                                                  \
     virtual std::string GetName() const { return #op_name; }                   \
     virtual std::string GetOpType() const { return "Reduce " #op_name; }       \
   };                                                                           \
-  REGISTER_OPERATOR(op_name, ops::ReduceOp, __##op_name##Maker__,        \
+  REGISTER_OPERATOR(op_name, ops::ReduceOp##__VA_ARGS__, __##op_name##Maker__, \
                     paddle::framework::EmptyGradOpMaker);

paddle/fluid/operators/tensor_array_to_tensor_op.cc

@@ -120,11 +120,18 @@ class LoDTensorArray2TensorOp : public framework::OperatorBase {
     out.Resize(out_dims);
 
     LodTensorArray2LodTensorVector(scope, base_name, Input("X"), &names);
-    // Invoke concat Op
-    auto concat_op = framework::OpRegistry::CreateOp(
+
+    auto use_stack = Attr<bool>("use_stack");
+
+    // Invoke concat Op or stack Op
+    auto op =
+        use_stack
+            ? framework::OpRegistry::CreateOp("stack", {{"X", names}},
+                                              {{"Y", {Output("Out")}}}, attrs)
+            : framework::OpRegistry::CreateOp(
                   "concat", {{"X", names}}, {{"Out", {Output("Out")}}}, attrs);
 
-    concat_op->Run(scope, place);
+    op->Run(scope, place);
   }
 };
 
@@ -139,17 +146,32 @@ class LoDTensorArray2TensorOpMaker : public framework::OpProtoAndCheckerMaker {
     AddAttr<int>("axis",
                  "The axis along which the input tensors will be concatenated.")
         .SetDefault(0);
+    AddAttr<bool>("use_stack",
+                  "Act as concat_op or stack_op. For stack mode, all tensors "
+                  "in the tensor array must have the same shape.")
+        .SetDefault(false);
     AddComment(R"DOC(
 tensor_array_to_tensor Operator.
 
-Concatenate the input LoDTensorArray along dimension axis to the output Tensor.
+If use concat mode, concatenate all tensors in the input LoDTensorArray along
+axis into the output Tensor.
+
+Examples:
+  Input = {[1,2], [3,4], [5,6]}
+  axis = 0
+  Output = [1,2,3,4,5,6]
+  OutputIndex = [2,2,2]
+
+If use stack mode, stack all tensors in the input LoDTensorArray along axis into
+the output Tensor.
+
 Examples:
   Input = {[1,2], [3,4], [5,6]}
   axis = 0
   Output = [[1,2],
             [3,4],
             [5,6]]
-  OutputIndex = [1,1,1]
+  OutputIndex = [2,2,2]
 
 )DOC");
   }
@@ -157,12 +179,34 @@ Examples:
 
 class LoDTensorArray2TensorOpInferShape : public framework::InferShapeBase {
  public:
-  void operator()(framework::InferShapeContext *ctx) const override {}
+  void operator()(framework::InferShapeContext *ctx) const override {
+    // in runtime, shape is determined by RunImpl
+    if (ctx->IsRuntime()) return;
+    auto dims = ctx->GetInputDim("X");
+    // if the shape is empty
+    if (dims == framework::make_ddim({0UL})) return;
+    // otherwise, suppose the shape of array is the shape of tensor in the
+    // array, which is consistent with what tensor_array_read_write dose
+    auto axis = ctx->Attrs().Get<int>("axis");
+    auto use_stack = ctx->Attrs().Get<bool>("use_stack");
+    if (use_stack) {
+      auto dim_vec = framework::vectorize<int>(dims);
+      // use -1 for the stack dim size
+      dim_vec.insert(dim_vec.begin() + axis, -1);
+      dims = framework::make_ddim(dim_vec);
+    } else {
+      // use -1 for the concat dim size
+      dims[axis] = -1;
+    }
+    ctx->SetOutputDim("Out", dims);
+  }
 };
 
 class LoDTensorArray2TensorGradInferShape : public framework::InferShapeBase {
  public:
-  void operator()(framework::InferShapeContext *context) const override {}
+  void operator()(framework::InferShapeContext *ctx) const override {
+    ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
+  }
 };
 
 class LoDTensorArray2TensorGradInferVarType
@@ -204,11 +248,18 @@ class LoDTensorArray2TensorGradOp : public framework::OperatorBase {
     LodTensorVectorResizeFromLodTensorArray(scope, "grad_name", Input("X"),
                                             &grad_names);
 
-    auto concat_grad_op = framework::OpRegistry::CreateOp(
+    auto use_stack = Attr<bool>("use_stack");
+
+    auto grad_op =
+        use_stack
+            ? framework::OpRegistry::CreateOp(
+                  "stack_grad", {{"X", names}, {"Y@GRAD", {dout_name}}},
+                  {{"X@GRAD", grad_names}}, attrs)
+            : framework::OpRegistry::CreateOp(
                   "concat_grad", {{"X", names}, {"Out@GRAD", {dout_name}}},
                   {{"X@GRAD", grad_names}}, attrs);
 
-    concat_grad_op->Run(scope, place);
+    grad_op->Run(scope, place);
 
     LodTensorArrayCreateFromLodTensorArray(scope, Input("X"), dx_name);
     auto &grad_inx =

python/paddle/fluid/layers/__init__.py

@@ -35,6 +35,7 @@ from .metric_op import *
 from .learning_rate_scheduler import *
 from .collective import *
 from .distributions import *
+from . import rnn
 
 __all__ = []
 __all__ += nn.__all__
@@ -47,3 +48,6 @@ __all__ += detection.__all__
 __all__ += metric_op.__all__
 __all__ += learning_rate_scheduler.__all__
 __all__ += distributions.__all__
+__all__ += rnn.__all__
+
+from .rnn import *

python/paddle/fluid/layers/nn.py

@@ -221,6 +221,7 @@ __all__ = [
     'filter_by_instag',
     'shard_index',
     'hard_swish',
+    'gather_tree',
     'mse_loss',
     'uniform_random',
 ]
@@ -16994,6 +16995,81 @@ def hard_swish(x, threshold=6.0, scale=6.0, offset=3.0, name=None):
     return out
 
 
+def gather_tree(ids, parents):
+    """
+    To be used after beam search. After beam search, we get selected ids at
+    each time step and the corresponding parents in the search tree. Both ids
+    and parents have the layout :attr:`[max_time, batch_size, beam_size]`. Then
+    :attr:`gather_tree` is used to backtrace from the last time step and
+    generate the full sequences by collecting selected ids.
+
+    Here is an example:
+
+    .. code-block:: text
+
+            Given:
+                ids = [[[2 2]
+                        [6 1]]
+                       [[3 9]
+                        [6 1]]
+                       [[0 1]
+                        [9 0]]]
+                parents = [[[0 0]
+                            [1 1]]
+                           [[1 0]
+                            [1 0]]
+                           [[0 0]
+                            [0 1]]]
+
+            Then:                
+                gather_tree(ids, parents)  
+                         = [[[2 2]
+                             [1 6]]
+                            [[3 3]
+                             [6 1]]
+                            [[0 1]
+                             [9 0]]]
+
+    Args:
+        ids(Variable): A Tensor with shape :attr:`[length, batch_size, beam_size]`
+            and data type :attr:`int32` or :attr:`int64`. It contains the selected
+            ids of all time steps.
+        parents(Variable): A Tensor with the same shape and data type as :attr:`ids`,
+            It contains the parents corresponding to selected ids when searching
+            among beams.
+
+    Returns:
+        Variable: A Tensor with the same shape and data type as :attr:`ids`. \
+            It contains the full sequences. The sequences are collected from \
+            :attr:`ids` by backtracing according to :attr:`parents`.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle.fluid as fluid
+
+            ids = fluid.layers.data(name='ids',
+                                    shape=[5, 2, 2],
+                                    dtype='int64',
+                                    append_batch_size=False)
+            parents = fluid.layers.data(name='parents',
+                                        shape=[5, 2, 2],
+                                        dtype='int64',
+                                        append_batch_size=False)
+            final_sequences = fluid.layers.gather_tree(ids, parents)
+    """
+    helper = LayerHelper('gather_tree', **locals())
+    out = helper.create_variable_for_type_inference(dtype=ids.dtype)
+
+    helper.append_op(
+        type="gather_tree",
+        inputs={"Ids": ids,
+                "Parents": parents},
+        outputs={"Out": out})
+
+    return out
+
+
 def mse_loss(input, label):
     """
     This op accepts input predications and target label and returns the mean square error.

python/paddle/fluid/layers/tensor.py

@@ -273,50 +273,85 @@ def concat(input, axis=0, name=None):
     return out
 
 
-def tensor_array_to_tensor(input, axis=1, name=None):
+def tensor_array_to_tensor(input, axis=1, name=None, use_stack=False):
     """
-    This OP concatenates the input LodTensorArray along the axis.
+    This function concatenates or stacks all tensors in the input LoDTensorArray
+    along the axis mentioned and returns that as the output.
+
+    For Example:
+
+    .. code-block:: text
+
+        Case 1:
+
+            Given:
+
+                input.data = {[[0.6, 0.1, 0.3],
+                               [0.5, 0.3, 0.2]],
+                              [[1.3],
+                               [1.8]],
+                              [[2.3, 2.1],
+                               [2.5, 2.4]]}
+
+                axis = 1, use_stack = False
+
+            Then:
+
+                output.data = [[0.6, 0.1, 0.3, 1.3, 2.3, 2.1],
+                               [0.5, 0.3, 0.2, 1.8, 2.5, 2.4]]
+
+                output_index.data = [3, 1, 2]
+
+        Case 2:
+
+            Given:
+
+                input.data = {[[0.6, 0.1],
+                               [0.5, 0.3]],
+                              [[0.3, 1.3],
+                               [0.2, 1.8]],
+                              [[2.3, 2.1],
+                               [2.5, 2.4]]}
+
+                axis = 1, use_stack = True
+
+            Then:
+
+                output.data = [[[0.6, 0.1]
+                                [0.3, 1.3]
+                                [2.3, 2.1],
+                               [[0.5, 0.3]
+                                [0.2, 1.8]
+                                [2.5, 2.4]]]
+
+                output_index.data = [2, 2, 2]
 
     Args:
-        input(Variable): A LodTensorArray with data type float32, float64, int32,
-            int64.
-        axis(int, optional): Axis to compute indices along. The effective range
-            is [-R, R), where R is Rank(x). when axis<0, it works the same way
-            as axis+R. Default is 1.
-        name (str, optional): The default value is None. Normally there is no
-            need for user to set this property. For more information, please
-            refer to :ref:`api_guide_Name`.
+        input(Variable): A LodTensorArray variable.
+        axis(int): The axis along which the tensors in attr::`input` will be
+            concatenated or stacked.
+        name(str|None): A name for this layer(optional). If set None, the layer
+                       will be named automatically.
+        use_stack(bool): Act as concat_op or stack_op. For stack mode, all
+            tensors in the tensor array must have the same shape.
 
     Returns:
-        Variable: A LoDTensor with the same data type as input's
-        Variable: The input LodTensorArray items' dims along the axis.
+        Variable: The concatenated or stacked tensor variable.
+        Variable: A 1-D tensor variable with int32 data type. The data in this \
+            tensor contains all input including tensors' sizes along the axis.
 
     Examples:
         .. code-block:: python
 
             import paddle.fluid as fluid
             import numpy as np
-
-            place = fluid.CPUPlace()
-
-            x1 = fluid.data(name="x", shape=[2,2], lod_level=0)
-            tmp = fluid.layers.fill_constant(shape=[2,3], dtype="float32", value=1)
-            x_arr = fluid.layers.create_array(dtype="float32")
-            c0 = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
-            fluid.layers.array_write(x=tmp, i=c0, array=x_arr)
-            c1 = fluid.layers.fill_constant(shape=[1], dtype='int64', value=1)
-            fluid.layers.array_write(x=x1, i=c1, array=x_arr)
-            output, output_index = fluid.layers.tensor_array_to_tensor(input=x_arr, axis=1)
-
-            exe = fluid.Executor(place)
-            exe.run(fluid.default_startup_program())
-
-            feedx = fluid.LoDTensor()
-            feedx.set(np.array([[1.3,-2.4],[0,4]]).astype("float32"), place)
-            res = exe.run(fluid.default_main_program(), feed={'x':feedx}, fetch_list=[output], return_numpy=False)
-            print(np.array(res[0]))
-            # [[ 1.   1.   1.   1.3 -2.4]
-            #  [ 1.   1.   1.   0.   4. ]]
+            x0 = fluid.layers.assign(np.random.rand(2, 2).astype("float32"))
+            x1 = fluid.layers.assign(np.random.rand(2, 2).astype("float32"))
+            i = fluid.layers.fill_constant(shape=[1], dtype="int64", value=0)
+            array = fluid.layers.create_array(dtype='float32')
+            fluid.layers.array_write(x0, i, array)
+            fluid.layers.array_write(x1, i + 1, array)
+            output, output_index = fluid.layers.tensor_array_to_tensor(input=array)
     """
     helper = LayerHelper('tensor_array_to_tensor', **locals())
     out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
@@ -326,7 +361,8 @@ def tensor_array_to_tensor(input, axis=1, name=None):
         inputs={'X': input},
         outputs={'Out': [out],
                  'OutIndex': [out_index]},
-        attrs={'axis': axis})
+        attrs={'axis': axis,
+               'use_stack': use_stack})
     return out, out_index
 
 
@@ -517,7 +553,8 @@ def fill_constant_batch_size_like(input,
                                   dtype,
                                   value,
                                   input_dim_idx=0,
-                                  output_dim_idx=0):
+                                  output_dim_idx=0,
+                                  force_cpu=False):
     """
     This OP creates a Tesnor accroding the shape and dtype, and initializes the
     Tensor with the constants provided in ``value``. When the input is LoDTensor
@@ -537,6 +574,7 @@ def fill_constant_batch_size_like(input,
             The default value is 0.
         output_dim_idx(int): Used to specify which dimension of Tensor is created to be set
             the value of batch_size of input Tensor. The default value is 0.
+        force_cpu(bool): data should be on CPU if it's true, defalut value is False.
 
     Returns:
         Variable: Tensor which will be created according to dtype.
@@ -562,7 +600,8 @@ def fill_constant_batch_size_like(input,
             'dtype': out.dtype,
             'value': float(value),
             'input_dim_idx': input_dim_idx,
-            'output_dim_idx': output_dim_idx
+            'output_dim_idx': output_dim_idx,
+            'force_cpu': force_cpu or force_init_on_cpu()
         })
     out.stop_gradient = True
     return out

python/paddle/fluid/layers/utils.py

@@ -13,6 +13,8 @@
 # limitations under the License.
 
 from __future__ import print_function
+import collections
+import six
 import numpy as np
 
 
@@ -59,3 +61,173 @@ def convert_to_list(value, n, name, dtype=np.int):
                     "including element " + str(single_value) + " of type" + " "
                     + str(type(single_value)))
         return value_list
+
+
+def is_sequence(seq):
+    """
+    Whether `seq` is an entry or nested structure
+    """
+    if isinstance(seq, dict):
+        return True
+    return (isinstance(seq, collections.Sequence) and
+            not isinstance(seq, six.string_types))
+
+
+def _sorted(dict_):
+    """
+    Returns a sorted list of the dict keys, with error if keys not sortable.
+    """
+    try:
+        return sorted(six.iterkeys(dict_))
+    except TypeError:
+        raise TypeError("nest only supports dicts with sortable keys.")
+
+
+def _yield_value(iterable):
+    if isinstance(iterable, dict):
+        # Iterate through dictionaries in a deterministic order by sorting the
+        # keys. Notice this means that we ignore the original order of `OrderedDict`
+        # instances. This is intentional, to avoid potential bugs caused by mixing
+        # ordered and plain dicts (e.g., flattening a dict but using a
+        # corresponding `OrderedDict` to pack it back).
+        for key in _sorted(iterable):
+            yield iterable[key]
+    else:
+        for value in iterable:
+            yield value
+
+
+def _yield_flat_nest(nest):
+    for n in _yield_value(nest):
+        if is_sequence(n):
+            for ni in _yield_flat_nest(n):
+                yield ni
+        else:
+            yield n
+
+
+def flatten(nest):
+    """
+    Traverse all entries in the nested structure and put them into an list.
+    """
+    if is_sequence(nest):
+        return list(_yield_flat_nest(nest))
+    else:
+        return [nest]
+
+
+def _sequence_like(instance, args):
+    """
+    Convert the sequence `args` to the same type as `instance`.
+    """
+    if isinstance(instance, dict):
+        # Pack dictionaries in a deterministic order by sorting the keys.
+        # Notice this means that we ignore the original order of `OrderedDict`
+        # instances. This is intentional, to avoid potential bugs caused by mixing
+        # ordered and plain dicts (e.g., flattening a dict but using a
+        # corresponding `OrderedDict` to pack it back).
+        result = dict(zip(_sorted(instance), args))
+        return type(instance)((key, result[key])
+                              for key in six.iterkeys(instance))
+    elif (isinstance(instance, tuple) and hasattr(instance, "_fields") and
+          isinstance(instance._fields, collections.Sequence) and
+          all(isinstance(f, six.string_types) for f in instance._fields)):
+        # This is a namedtuple
+        return type(instance)(*args)
+    else:
+        # Not a namedtuple
+        return type(instance)(args)
+
+
+def _packed_nest_with_indices(structure, flat, index):
+    """
+    Helper function for pack_sequence_as.
+    """
+    packed = []
+    for s in _yield_value(structure):
+        if is_sequence(s):
+            new_index, child = _packed_nest_with_indices(s, flat, index)
+            packed.append(_sequence_like(s, child))
+            index = new_index
+        else:
+            packed.append(flat[index])
+            index += 1
+    return index, packed
+
+
+def pack_sequence_as(structure, flat_sequence):
+    """
+    Pack a given flattened sequence into a given structure.
+    """
+    if not is_sequence(flat_sequence):
+        raise TypeError("flat_sequence must be a sequence")
+    if not is_sequence(structure):
+        if len(flat_sequence) != 1:
+            raise ValueError(
+                "Structure is a scalar but len(flat_sequence) == %d > 1" %
+                len(flat_sequence))
+        return flat_sequence[0]
+    flat_structure = flatten(structure)
+    if len(flat_structure) != len(flat_sequence):
+        raise ValueError(
+            "Could not pack sequence. Structure had %d elements, but flat_sequence "
+            "had %d elements.  Structure: %s, flat_sequence: %s." %
+            (len(flat_structure), len(flat_sequence), structure, flat_sequence))
+    _, packed = _packed_nest_with_indices(structure, flat_sequence, 0)
+    return _sequence_like(structure, packed)
+
+
+def map_structure(func, *structure):
+    """
+    Apply `func` to each entry in `structure` and return a new structure.
+    """
+    flat_structure = [flatten(s) for s in structure]
+    entries = zip(*flat_structure)
+    return pack_sequence_as(structure[0], [func(*x) for x in entries])
+
+
+def _recursive_assert_same_structure(nest1, nest2, check_types):
+    """
+    Helper function for `assert_same_structure`.
+    """
+    is_sequence_nest1 = is_sequence(nest1)
+    if is_sequence_nest1 != is_sequence(nest2):
+        raise ValueError(
+            "The two structures don't have the same nested structure.\n\n"
+            "First structure: %s\n\nSecond structure: %s." % (nest1, nest2))
+    if not is_sequence_nest1:
+        return  # finished checking
+    if check_types:
+        type_nest1 = type(nest1)
+        type_nest2 = type(nest2)
+        if type_nest1 != type_nest2:
+            raise TypeError(
+                "The two structures don't have the same sequence type. First "
+                "structure has type %s, while second structure has type %s." %
+                (type_nest1, type_nest2))
+        if isinstance(nest1, dict):
+            keys1 = set(six.iterkeys(nest1))
+            keys2 = set(six.iterkeys(nest2))
+            if keys1 != keys2:
+                raise ValueError(
+                    "The two dictionaries don't have the same set of keys. First "
+                    "structure has keys {}, while second structure has keys {}."
+                    .format(keys1, keys2))
+    nest1_as_sequence = [n for n in _yield_value(nest1)]
+    nest2_as_sequence = [n for n in _yield_value(nest2)]
+    for n1, n2 in zip(nest1_as_sequence, nest2_as_sequence):
+        _recursive_assert_same_structure(n1, n2, check_types)
+
+
+def assert_same_structure(nest1, nest2, check_types=True):
+    """
+    Confirm two nested structures with the same structure.
+    """
+    len_nest1 = len(flatten(nest1)) if is_sequence(nest1) else 1
+    len_nest2 = len(flatten(nest2)) if is_sequence(nest2) else 1
+    if len_nest1 != len_nest2:
+        raise ValueError("The two structures don't have the same number of "
+                         "elements.\n\nFirst structure (%i elements): %s\n\n"
+                         "Second structure (%i elements): %s" %
+                         (len_nest1, nest1, len_nest2, nest2))
+    _recursive_assert_same_structure(nest1, nest2, check_types)

python/paddle/fluid/tests/unittests/test_gather_tree_op.py

+#   Copyright (c) 2019 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from op_test import OpTest
+import paddle.fluid as fluid
+
+
+class TestGatherTreeOp(OpTest):
+    def setUp(self):
+        self.op_type = "gather_tree"
+        max_length, batch_size, beam_size = 5, 2, 2
+        ids = np.random.randint(
+            0, high=10, size=(max_length, batch_size, beam_size))
+        parents = np.random.randint(
+            0, high=beam_size, size=(max_length, batch_size, beam_size))
+        self.inputs = {"Ids": ids, "Parents": parents}
+        self.outputs = {'Out': self.backtrace(ids, parents)}
+
+    def test_check_output(self):
+        self.check_output()
+
+    @staticmethod
+    def backtrace(ids, parents):
+        out = np.zeros_like(ids)
+        (max_length, batch_size, beam_size) = ids.shape
+        for batch in range(batch_size):
+            for beam in range(beam_size):
+                out[max_length - 1, batch, beam] = ids[max_length - 1, batch,
+                                                       beam]
+                parent = parents[max_length - 1, batch, beam]
+                for step in range(max_length - 2, -1, -1):
+                    out[step, batch, beam] = ids[step, batch, parent]
+                    parent = parents[step, batch, parent]
+        return out
+
+
+class TestGatherTreeOpAPI(OpTest):
+    def test_case(self):
+        ids = fluid.layers.data(
+            name='ids', shape=[5, 2, 2], dtype='int64', append_batch_size=False)
+        parents = fluid.layers.data(
+            name='parents',
+            shape=[5, 2, 2],
+            dtype='int64',
+            append_batch_size=False)
+        final_sequences = fluid.layers.gather_tree(ids, parents)
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/test_rnn_cell_api.py

+# Copyright (c) 2019 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy
+
+import paddle.fluid as fluid
+import paddle.fluid.layers as layers
+import paddle.fluid.core as core
+
+from paddle.fluid.executor import Executor
+from paddle.fluid import framework
+
+from paddle.fluid.layers.rnn import LSTMCell, GRUCell, RNNCell
+from paddle.fluid.layers import rnn as dynamic_rnn
+from paddle.fluid import contrib
+from paddle.fluid.contrib.layers import basic_lstm
+import paddle.fluid.layers.utils as utils
+
+import numpy as np
+
+
+class TestLSTMCell(unittest.TestCase):
+    def setUp(self):
+        self.batch_size = 4
+        self.input_size = 16
+        self.hidden_size = 16
+
+    def test_run(self):
+        inputs = fluid.data(
+            name='inputs', shape=[None, self.input_size], dtype='float32')
+        pre_hidden = fluid.data(
+            name='pre_hidden', shape=[None, self.hidden_size], dtype='float32')
+        pre_cell = fluid.data(
+            name='pre_cell', shape=[None, self.hidden_size], dtype='float32')
+
+        cell = LSTMCell(self.hidden_size)
+        lstm_hidden_new, lstm_states_new = cell(inputs, [pre_hidden, pre_cell])
+
+        lstm_unit = contrib.layers.rnn_impl.BasicLSTMUnit(
+            "basicLSTM", self.hidden_size, None, None, None, None, 1.0,
+            "float32")
+        lstm_hidden, lstm_cell = lstm_unit(inputs, pre_hidden, pre_cell)
+
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+        else:
+            place = core.CPUPlace()
+        exe = Executor(place)
+        exe.run(framework.default_startup_program())
+
+        inputs_np = np.random.uniform(
+            -0.1, 0.1, (self.batch_size, self.input_size)).astype('float32')
+        pre_hidden_np = np.random.uniform(
+            -0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32')
+        pre_cell_np = np.random.uniform(
+            -0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32')
+
+        param_names = [[
+            "LSTMCell/BasicLSTMUnit_0.w_0", "basicLSTM/BasicLSTMUnit_0.w_0"
+        ], ["LSTMCell/BasicLSTMUnit_0.b_0", "basicLSTM/BasicLSTMUnit_0.b_0"]]
+
+        for names in param_names:
+            param = np.array(fluid.global_scope().find_var(names[0]).get_tensor(
+            ))
+            param = np.random.uniform(
+                -0.1, 0.1, size=param.shape).astype('float32')
+            fluid.global_scope().find_var(names[0]).get_tensor().set(param,
+                                                                     place)
+            fluid.global_scope().find_var(names[1]).get_tensor().set(param,
+                                                                     place)
+
+        out = exe.run(feed={
+            'inputs': inputs_np,
+            'pre_hidden': pre_hidden_np,
+            'pre_cell': pre_cell_np
+        },
+                      fetch_list=[lstm_hidden_new, lstm_hidden])
+
+        self.assertTrue(np.allclose(out[0], out[1], rtol=1e-4, atol=0))
+
+
+class TestGRUCell(unittest.TestCase):
+    def setUp(self):
+        self.batch_size = 4
+        self.input_size = 16
+        self.hidden_size = 16
+
+    def test_run(self):
+        inputs = fluid.data(
+            name='inputs', shape=[None, self.input_size], dtype='float32')
+        pre_hidden = layers.data(
+            name='pre_hidden',
+            shape=[None, self.hidden_size],
+            append_batch_size=False,
+            dtype='float32')
+
+        cell = GRUCell(self.hidden_size)
+        gru_hidden_new, _ = cell(inputs, pre_hidden)
+
+        gru_unit = contrib.layers.rnn_impl.BasicGRUUnit(
+            "basicGRU", self.hidden_size, None, None, None, None, "float32")
+        gru_hidden = gru_unit(inputs, pre_hidden)
+
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+        else:
+            place = core.CPUPlace()
+        exe = Executor(place)
+        exe.run(framework.default_startup_program())
+
+        inputs_np = np.random.uniform(
+            -0.1, 0.1, (self.batch_size, self.input_size)).astype('float32')
+        pre_hidden_np = np.random.uniform(
+            -0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32')
+
+        param_names = [
+            ["GRUCell/BasicGRUUnit_0.w_0", "basicGRU/BasicGRUUnit_0.w_0"],
+            ["GRUCell/BasicGRUUnit_0.w_1", "basicGRU/BasicGRUUnit_0.w_1"],
+            ["GRUCell/BasicGRUUnit_0.b_0", "basicGRU/BasicGRUUnit_0.b_0"],
+            ["GRUCell/BasicGRUUnit_0.b_1", "basicGRU/BasicGRUUnit_0.b_1"]
+        ]
+
+        for names in param_names:
+            param = np.array(fluid.global_scope().find_var(names[0]).get_tensor(
+            ))
+            param = np.random.uniform(
+                -0.1, 0.1, size=param.shape).astype('float32')
+            fluid.global_scope().find_var(names[0]).get_tensor().set(param,
+                                                                     place)
+            fluid.global_scope().find_var(names[1]).get_tensor().set(param,
+                                                                     place)
+
+        out = exe.run(feed={'inputs': inputs_np,
+                            'pre_hidden': pre_hidden_np},
+                      fetch_list=[gru_hidden_new, gru_hidden])
+
+        self.assertTrue(np.allclose(out[0], out[1], rtol=1e-4, atol=0))
+
+
+class TestRnn(unittest.TestCase):
+    def setUp(self):
+        self.batch_size = 4
+        self.input_size = 16
+        self.hidden_size = 16
+        self.seq_len = 4
+
+    def test_run(self):
+        inputs_basic_lstm = fluid.data(
+            name='inputs_basic_lstm',
+            shape=[None, None, self.input_size],
+            dtype='float32')
+        sequence_length = fluid.data(
+            name="sequence_length", shape=[None], dtype='int64')
+
+        inputs_dynamic_rnn = layers.transpose(inputs_basic_lstm, perm=[1, 0, 2])
+        cell = LSTMCell(self.hidden_size, name="LSTMCell_for_rnn")
+        output, final_state = dynamic_rnn(
+            cell=cell,
+            inputs=inputs_dynamic_rnn,
+            sequence_length=sequence_length,
+            is_reverse=False)
+        output_new = layers.transpose(output, perm=[1, 0, 2])
+
+        rnn_out, last_hidden, last_cell = basic_lstm(inputs_basic_lstm, None, None, self.hidden_size, num_layers=1, \
+                batch_first = False, bidirectional=False, sequence_length=sequence_length, forget_bias = 1.0)
+
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+        else:
+            place = core.CPUPlace()
+        exe = Executor(place)
+        exe.run(framework.default_startup_program())
+
+        inputs_basic_lstm_np = np.random.uniform(
+            -0.1, 0.1,
+            (self.seq_len, self.batch_size, self.input_size)).astype('float32')
+        sequence_length_np = np.ones(
+            self.batch_size, dtype='int64') * self.seq_len
+
+        inputs_np = np.random.uniform(
+            -0.1, 0.1, (self.batch_size, self.input_size)).astype('float32')
+        pre_hidden_np = np.random.uniform(
+            -0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32')
+        pre_cell_np = np.random.uniform(
+            -0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32')
+
+        param_names = [[
+            "LSTMCell_for_rnn/BasicLSTMUnit_0.w_0",
+            "basic_lstm_layers_0/BasicLSTMUnit_0.w_0"
+        ], [
+            "LSTMCell_for_rnn/BasicLSTMUnit_0.b_0",
+            "basic_lstm_layers_0/BasicLSTMUnit_0.b_0"
+        ]]
+
+        for names in param_names:
+            param = np.array(fluid.global_scope().find_var(names[0]).get_tensor(
+            ))
+            param = np.random.uniform(
+                -0.1, 0.1, size=param.shape).astype('float32')
+            fluid.global_scope().find_var(names[0]).get_tensor().set(param,
+                                                                     place)
+            fluid.global_scope().find_var(names[1]).get_tensor().set(param,
+                                                                     place)
+
+        out = exe.run(feed={
+            'inputs_basic_lstm': inputs_basic_lstm_np,
+            'sequence_length': sequence_length_np,
+            'inputs': inputs_np,
+            'pre_hidden': pre_hidden_np,
+            'pre_cell': pre_cell_np
+        },
+                      fetch_list=[output_new, rnn_out])
+
+        self.assertTrue(np.allclose(out[0], out[1], rtol=1e-4))
+
+
+class TestRnnUtil(unittest.TestCase):
+    """
+    Test cases for rnn apis' utility methods for coverage.
+    """
+
+    def test_case(self):
+        inputs = {"key1": 1, "key2": 2}
+        func = lambda x: x + 1
+        outputs = utils.map_structure(func, inputs)
+        utils.assert_same_structure(inputs, outputs)
+        try:
+            inputs["key3"] = 3
+            utils.assert_same_structure(inputs, outputs)
+        except ValueError as identifier:
+            pass
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/test_rnn_decode_api.py

+# Copyright (c) 2019 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy
+
+import paddle.fluid as fluid
+import paddle.fluid.layers as layers
+import paddle.fluid.core as core
+
+from paddle.fluid.executor import Executor
+from paddle.fluid import framework
+
+from paddle.fluid.layers.rnn import LSTMCell, GRUCell, RNNCell, BeamSearchDecoder, dynamic_decode
+from paddle.fluid.layers import rnn as dynamic_rnn
+from paddle.fluid import contrib
+from paddle.fluid.contrib.layers import basic_lstm
+
+import numpy as np
+
+
+class EncoderCell(RNNCell):
+    def __init__(self, num_layers, hidden_size, dropout_prob=0.):
+        self.num_layers = num_layers
+        self.hidden_size = hidden_size
+        self.dropout_prob = dropout_prob
+        self.lstm_cells = []
+        for i in range(num_layers):
+            self.lstm_cells.append(LSTMCell(hidden_size))
+
+    def call(self, step_input, states):
+        new_states = []
+        for i in range(self.num_layers):
+            out, new_state = self.lstm_cells[i](step_input, states[i])
+            step_input = layers.dropout(
+                out, self.dropout_prob) if self.dropout_prob > 0 else out
+            new_states.append(new_state)
+        return step_input, new_states
+
+    @property
+    def state_shape(self):
+        return [cell.state_shape for cell in self.lstm_cells]
+
+
+class DecoderCell(RNNCell):
+    def __init__(self, num_layers, hidden_size, dropout_prob=0.):
+        self.num_layers = num_layers
+        self.hidden_size = hidden_size
+        self.dropout_prob = dropout_prob
+        self.lstm_cells = []
+        for i in range(num_layers):
+            self.lstm_cells.append(LSTMCell(hidden_size))
+
+    def attention(self, hidden, encoder_output, encoder_padding_mask):
+        query = layers.fc(hidden,
+                          size=encoder_output.shape[-1],
+                          bias_attr=False)
+        attn_scores = layers.matmul(
+            layers.unsqueeze(query, [1]), encoder_output, transpose_y=True)
+        if encoder_padding_mask is not None:
+            attn_scores = layers.elementwise_add(attn_scores,
+                                                 encoder_padding_mask)
+        attn_scores = layers.softmax(attn_scores)
+        attn_out = layers.squeeze(
+            layers.matmul(attn_scores, encoder_output), [1])
+        attn_out = layers.concat([attn_out, hidden], 1)
+        attn_out = layers.fc(attn_out, size=self.hidden_size, bias_attr=False)
+        return attn_out
+
+    def call(self,
+             step_input,
+             states,
+             encoder_output,
+             encoder_padding_mask=None):
+        lstm_states, input_feed = states
+        new_lstm_states = []
+        step_input = layers.concat([step_input, input_feed], 1)
+        for i in range(self.num_layers):
+            out, new_lstm_state = self.lstm_cells[i](step_input, lstm_states[i])
+            step_input = layers.dropout(
+                out, self.dropout_prob) if self.dropout_prob > 0 else out
+            new_lstm_states.append(new_lstm_state)
+        out = self.attention(step_input, encoder_output, encoder_padding_mask)
+        return out, [new_lstm_states, out]
+
+
+class TestDynamicDecode(unittest.TestCase):
+    def setUp(self):
+        self.batch_size = 4
+        self.input_size = 16
+        self.hidden_size = 16
+        self.seq_len = 4
+
+    def test_run(self):
+        start_token = 0
+        end_token = 1
+        src_vocab_size = 10
+        trg_vocab_size = 10
+        num_layers = 1
+        hidden_size = self.hidden_size
+        beam_size = 8
+        max_length = self.seq_len
+
+        src = layers.data(name="src", shape=[-1, 1], dtype='int64')
+        src_len = layers.data(name="src_len", shape=[-1], dtype='int64')
+
+        trg = layers.data(name="trg", shape=[-1, 1], dtype='int64')
+        trg_len = layers.data(name="trg_len", shape=[-1], dtype='int64')
+
+        src_embeder = lambda x: fluid.embedding(
+            x,
+            size=[src_vocab_size, hidden_size],
+            param_attr=fluid.ParamAttr(name="src_embedding"))
+
+        trg_embeder = lambda x: fluid.embedding(
+            x,
+            size=[trg_vocab_size, hidden_size],
+            param_attr=fluid.ParamAttr(name="trg_embedding"))
+
+        # use basic_lstm
+        encoder_cell = EncoderCell(num_layers, hidden_size)
+        encoder_output, encoder_final_state = dynamic_rnn(
+            cell=encoder_cell,
+            inputs=src_embeder(src),
+            sequence_length=src_len,
+            is_reverse=False)
+
+        src_mask = layers.sequence_mask(
+            src_len, maxlen=layers.shape(src)[1], dtype='float32')
+        encoder_padding_mask = (src_mask - 1.0) * 1000000000
+        encoder_padding_mask = layers.unsqueeze(encoder_padding_mask, [1])
+
+        decoder_cell = DecoderCell(num_layers, hidden_size)
+        decoder_initial_states = [
+            encoder_final_state, decoder_cell.get_initial_states(
+                batch_ref=encoder_output, shape=[hidden_size])
+        ]
+
+        decoder_output, _ = dynamic_rnn(
+            cell=decoder_cell,
+            inputs=trg_embeder(trg),
+            initial_states=decoder_initial_states,
+            sequence_length=None,
+            encoder_output=encoder_output,
+            encoder_padding_mask=encoder_padding_mask)
+
+        output_layer = lambda x: layers.fc(x,
+                                           size=trg_vocab_size,
+                                           num_flatten_dims=len(x.shape) - 1,
+                                           param_attr=fluid.ParamAttr(
+                                               name="output_w"),
+                                           bias_attr=False)
+
+        # inference
+        encoder_output = BeamSearchDecoder.tile_beam_merge_with_batch(
+            encoder_output, beam_size)
+        encoder_padding_mask = BeamSearchDecoder.tile_beam_merge_with_batch(
+            encoder_padding_mask, beam_size)
+        beam_search_decoder = BeamSearchDecoder(
+            decoder_cell,
+            start_token,
+            end_token,
+            beam_size,
+            embedding_fn=trg_embeder,
+            output_fn=output_layer)
+        outputs, _ = dynamic_decode(
+            beam_search_decoder,
+            inits=decoder_initial_states,
+            max_step_num=max_length,
+            encoder_output=encoder_output,
+            encoder_padding_mask=encoder_padding_mask)
+
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+        else:
+            place = core.CPUPlace()
+        exe = Executor(place)
+        exe.run(framework.default_startup_program())
+
+        src_np = np.random.randint(
+            0, src_vocab_size, (self.batch_size, max_length)).astype('int64')
+        src_len_np = np.ones(self.batch_size, dtype='int64') * max_length
+        trg_np = np.random.randint(
+            0, trg_vocab_size, (self.batch_size, max_length)).astype('int64')
+        trg_len_np = np.ones(self.batch_size, dtype='int64') * max_length
+
+        out = exe.run(feed={
+            'src': src_np,
+            'src_len': src_len_np,
+            'trg': trg_np,
+            'trg_len': trg_len_np
+        },
+                      fetch_list=[outputs])
+
+        self.assertTrue(out[0].shape[0] == self.batch_size)
+        self.assertTrue(out[0].shape[1] <= max_length + 1)
+        self.assertTrue(out[0].shape[2] == beam_size)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/test_tensor_array_to_tensor.py

@@ -23,6 +23,8 @@ from paddle.fluid.executor import Executor
 
 
 class TestLoDTensorArrayConcat(unittest.TestCase):
+    """Test case for concat mode of tensor_array_to_tensor."""
+
     def setUp(self):
         self.op_type = "tensor_array_to_tensor"
         self.attrs = {"axis": 0}
@@ -138,5 +140,82 @@ class TestLoDTensorArrayConcat(unittest.TestCase):
                     numpy.array(random_grad[i + 1]))
 
 
+class TestLoDTensorArrayStack(unittest.TestCase):
+    """Test case for stack mode of tensor_array_to_tensor."""
+
+    def setUp(self):
+        self.op_type = "tensor_array_to_tensor"
+        self.attrs = {"axis": 1, "use_stack": True}
+        self.inputs = [
+            numpy.random.rand(2, 3, 4).astype("float32"),
+            numpy.random.rand(2, 3, 4).astype("float32"),
+            numpy.random.rand(2, 3, 4).astype("float32")
+        ]
+        self.outputs = [
+            numpy.stack(
+                self.inputs, axis=self.attrs["axis"]), numpy.array(
+                    [x.shape[self.attrs["axis"]] for x in self.inputs],
+                    dtype="int32")
+        ]
+        self.input_grads = [numpy.ones_like(x) for x in self.inputs]
+        self.set_program()
+        for var in self.program.list_vars():
+            # to avoid scope clearing after execution
+            var.persistable = True
+
+    def set_program(self):
+        self.program = fluid.Program()
+        with fluid.program_guard(self.program):
+            self.array = array = fluid.layers.create_array(dtype='float32')
+            idx = fluid.layers.fill_constant(shape=[1], dtype="int64", value=0)
+            for i, x in enumerate(self.inputs):
+                x = fluid.layers.assign(x)
+                fluid.layers.array_write(x, idx + i, array)
+            output, output_index = fluid.layers.tensor_array_to_tensor(
+                input=array, **self.attrs)
+            loss = fluid.layers.reduce_sum(output)
+            fluid.backward.append_backward(loss)
+        self.output_vars = [output, output_index]
+
+    def run_check(self, executor, scope):
+        executor.run(self.program, scope=scope)
+        for i, output in enumerate(self.outputs):
+            numpy.allclose(
+                numpy.array(scope.var(self.output_vars[i].name).get_tensor()),
+                output,
+                atol=0)
+        tensor_array_grad = scope.var(self.array.name).get_lod_tensor_array()
+        for i, input_grad in enumerate(self.input_grads):
+            numpy.allclose(
+                numpy.array(tensor_array_grad[i]), input_grad, atol=0)
+
+    def test_cpu(self):
+        scope = core.Scope()
+        place = core.CPUPlace()
+        executor = fluid.Executor(place)
+        self.run_check(executor, scope)
+
+    def test_gpu(self):
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+            scope = core.Scope()
+            executor = fluid.Executor(place)
+            self.run_check(executor, scope)
+
+
+class TestTensorArrayToTensorAPI(unittest.TestCase):
+    def test_case(self):
+        x0 = fluid.layers.assign(numpy.random.rand(2, 3, 4).astype("float32"))
+        x1 = fluid.layers.assign(numpy.random.rand(2, 3, 4).astype("float32"))
+        i = fluid.layers.fill_constant(shape=[1], dtype="int64", value=0)
+        array = fluid.layers.create_array(dtype='float32')
+        fluid.layers.array_write(x0, i, array)
+        fluid.layers.array_write(x1, i + 1, array)
+        output, output_index = fluid.layers.tensor_array_to_tensor(
+            input=array, axis=1, use_stack=True)
+        output, output_index = fluid.layers.tensor_array_to_tensor(
+            input=array, axis=1, use_stack=False)
+
+
 if __name__ == '__main__':
     unittest.main()