Merge pull request #13424 from chengduoZH/refine_seq_concat

Refine seq_concat

paddle/fluid/API.spec

@@ -175,6 +175,7 @@ paddle.fluid.layers.stack ArgSpec(args=['x', 'axis'], varargs=None, keywords=Non
 paddle.fluid.layers.pad2d ArgSpec(args=['input', 'paddings', 'mode', 'pad_value', 'data_format', 'name'], varargs=None, keywords=None, defaults=([0, 0, 0, 0], 'constant', 0.0, 'NCHW', None))
 paddle.fluid.layers.unstack ArgSpec(args=['x', 'axis', 'num'], varargs=None, keywords=None, defaults=(0, None))
 paddle.fluid.layers.sequence_enumerate ArgSpec(args=['input', 'win_size', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0, None))
+paddle.fluid.layers.sequence_concat ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,))
 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))
 paddle.fluid.layers.open_recordio_file ArgSpec(args=['filename', 'shapes', 'lod_levels', 'dtypes', 'pass_num', 'for_parallel'], varargs=None, keywords=None, defaults=(1, True))
 paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))

paddle/fluid/framework/op_desc.cc

@@ -441,7 +441,10 @@ static void InitInferShapeFuncs() {
 
     for (auto &kern_pair : OperatorWithKernel::AllOpKernels()) {
       auto op_type = kern_pair.first;
-      auto &op_info = info_map.at(op_type);
+      auto it = info_map.find(op_type);
+      PADDLE_ENFORCE(it != info_map.end(), "%s has not been registered",
+                     op_type);
+      auto &op_info = it->second;
       auto op = static_cast<OperatorWithKernel *>(op_info.Creator()(
           "", VariableNameMap{}, VariableNameMap{}, AttributeMap{}));
       if (op_info.infer_shape_) {  // infer_shape has been registered.

paddle/fluid/operators/concat_op.cc

@@ -95,6 +95,7 @@ class ConcatOpGrad : public framework::OperatorWithKernel {
 
   void InferShape(framework::InferShapeContext *ctx) const override {
     ctx->SetOutputsDim(framework::GradVarName("X"), ctx->GetInputsDim("X"));
+    ctx->ShareLoD("X", framework::GradVarName("X"));
   }
 };
 

paddle/fluid/operators/concat_op.h

@@ -109,8 +109,9 @@ class ConcatGradKernel : public framework::OpKernel<T> {
       auto& dev_ctx = ctx.template device_context<DeviceContext>();
       paddle::operators::math::ConcatGradFunctor<DeviceContext, T>
           concat_grad_functor;
-      concat_grad_functor(dev_ctx, *out_grad, ins, static_cast<int>(axis),
-                          &outputs);
+      concat_grad_functor(dev_ctx, *out_grad,
+                          ctx.MultiInput<framework::Tensor>("X"),
+                          static_cast<int>(axis), &outputs);
     }
   }
 };

paddle/fluid/operators/detail/safe_ref.h

@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
-
+#include <vector>
 #include "paddle/fluid/platform/enforce.h"
 
 namespace paddle {
@@ -24,10 +24,22 @@ namespace detail {
  * and passed by `args`
  */
 template <typename T, typename... ARGS>
-inline T &Ref(T *ptr, ARGS &&... args) {
+inline T& Ref(T* ptr, ARGS&&... args) {
   PADDLE_ENFORCE(ptr != nullptr, args...);
   return *ptr;
 }
+
+template <typename T, typename... ARGS>
+inline std::vector<std::reference_wrapper<T>> VectorRef(
+    const std::vector<T*>& vec, ARGS&&... args) {
+  std::vector<std::reference_wrapper<T>> result;
+  result.reserve(vec.size());
+  for (auto* ptr : vec) {
+    result.emplace_back(Ref(ptr, args...));
+  }
+  return result;
+}
+
 }  // namespace detail
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/math/concat.cc

@@ -27,7 +27,7 @@ template <typename T>
 class ConcatFunctor<platform::CPUDeviceContext, T> {
  public:
   void operator()(const platform::CPUDeviceContext& context,
-                  const std::vector<framework::Tensor>& input, const int axis,
+                  const std::vector<framework::Tensor>& input, int axis,
                   framework::Tensor* output) {
     // TODO(zcd): Add input data validity checking
     int num = input.size();
@@ -71,7 +71,7 @@ class ConcatGradFunctor<platform::CPUDeviceContext, T> {
  public:
   void operator()(const platform::CPUDeviceContext& context,
                   const framework::Tensor& input,
-                  const std::vector<const framework::LoDTensor*>& ref_inputs,
+                  const std::vector<const framework::Tensor*>& ref_inputs,
                   const int axis, std::vector<framework::Tensor*>* outputs) {
     // TODO(zcd): Add input data validity checking
     size_t num = outputs->size();
@@ -109,16 +109,11 @@ class ConcatGradFunctor<platform::CPUDeviceContext, T> {
     }
   }
 };
+#define DEFINE_FUNCTOR(type)                                      \
+  template class ConcatFunctor<platform::CPUDeviceContext, type>; \
+  template class ConcatGradFunctor<platform::CPUDeviceContext, type>;
 
-template class ConcatFunctor<platform::CPUDeviceContext, int>;
-template class ConcatFunctor<platform::CPUDeviceContext, int64_t>;
-template class ConcatFunctor<platform::CPUDeviceContext, float>;
-template class ConcatFunctor<platform::CPUDeviceContext, double>;
-
-template class ConcatGradFunctor<platform::CPUDeviceContext, int>;
-template class ConcatGradFunctor<platform::CPUDeviceContext, int64_t>;
-template class ConcatGradFunctor<platform::CPUDeviceContext, float>;
-template class ConcatGradFunctor<platform::CPUDeviceContext, double>;
+FOR_ALL_TYPES(DEFINE_FUNCTOR);
 
 }  // namespace math
 }  // namespace operators

paddle/fluid/operators/math/concat.cu

@@ -17,6 +17,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/mixed_vector.h"
 #include "paddle/fluid/operators/math/concat.h"
 #include "paddle/fluid/platform/cuda_primitives.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace paddle {
 namespace operators {
@@ -118,7 +119,7 @@ template <typename T>
 class ConcatFunctor<platform::CUDADeviceContext, T> {
  public:
   void operator()(const platform::CUDADeviceContext& context,
-                  const std::vector<framework::Tensor>& input, const int axis,
+                  const std::vector<framework::Tensor>& input, int axis,
                   framework::Tensor* output) {
     // TODO(zcd): Add input data validity checking
     int in_num = input.size();
@@ -192,8 +193,8 @@ class ConcatGradFunctor<platform::CUDADeviceContext, T> {
  public:
   void operator()(const platform::CUDADeviceContext& context,
                   const framework::Tensor& input,
-                  const std::vector<const framework::LoDTensor*>& ref_inputs,
-                  const int axis, std::vector<framework::Tensor*>* outputs) {
+                  const std::vector<const framework::Tensor*>& ref_inputs,
+                  int axis, std::vector<framework::Tensor*>* outputs) {
     // TODO(zcd): Add input data validity checking
     int o_num = outputs->size();
     int out_row = 1;
@@ -261,15 +262,11 @@ class ConcatGradFunctor<platform::CUDADeviceContext, T> {
   }
 };
 
-template class ConcatFunctor<platform::CUDADeviceContext, int>;
-template class ConcatFunctor<platform::CUDADeviceContext, int64_t>;
-template class ConcatFunctor<platform::CUDADeviceContext, float>;
-template class ConcatFunctor<platform::CUDADeviceContext, double>;
+#define DEFINE_FUNCTOR(type)                                       \
+  template class ConcatFunctor<platform::CUDADeviceContext, type>; \
+  template class ConcatGradFunctor<platform::CUDADeviceContext, type>
 
-template class ConcatGradFunctor<platform::CUDADeviceContext, int>;
-template class ConcatGradFunctor<platform::CUDADeviceContext, int64_t>;
-template class ConcatGradFunctor<platform::CUDADeviceContext, float>;
-template class ConcatGradFunctor<platform::CUDADeviceContext, double>;
+FOR_ALL_TYPES(DEFINE_FUNCTOR);
 
 }  // namespace math
 }  // namespace operators

paddle/fluid/operators/math/concat.h

@@ -37,7 +37,7 @@ template <typename DeviceContext, typename T>
 class ConcatFunctor {
  public:
   void operator()(const DeviceContext& context,
-                  const std::vector<framework::Tensor>& input, const int axis,
+                  const std::vector<framework::Tensor>& input, int axis,
                   framework::Tensor* output);
 };
 
@@ -57,10 +57,21 @@ template <typename DeviceContext, typename T>
 class ConcatGradFunctor {
  public:
   void operator()(const DeviceContext& context, const framework::Tensor& input,
-                  const std::vector<const framework::LoDTensor*>& ref_inputs,
-                  const int axis, std::vector<framework::Tensor*>* outputs);
+                  const std::vector<const framework::Tensor*>& ref_inputs,
+                  int axis, std::vector<framework::Tensor*>* outputs);
 };
 
 }  // namespace math
 }  // namespace operators
 }  // namespace paddle
+
+#define FOR_ALL_TYPES(macro) \
+  macro(int);                \
+  macro(float);              \
+  macro(double);             \
+  macro(bool);               \
+  macro(int64_t);            \
+  macro(int16_t);            \
+  macro(uint8_t);            \
+  macro(int8_t);             \
+  macro(::paddle::platform::float16)

paddle/fluid/operators/sequence_concat_op.cc

-/* 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. */
+// Copyright (c) 2018 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/sequence_concat_op.h"
+#include <vector>
 
 namespace paddle {
 namespace operators {
 
-class SequenceConcatOp : public framework::OperatorWithKernel {
+class SeqConcatOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
-  void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInputs("X"),
-                   "Inputs(X) of SequenceConcatOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                   "Output(Out) of SequenceConcatOp should not be null.");
-    const size_t level = static_cast<size_t>(ctx->Attrs().Get<int>("level"));
-    const size_t axis = static_cast<size_t>(ctx->Attrs().Get<int>("axis"));
-    PADDLE_ENFORCE(level == 0UL || level == 1UL,
-                   "The sequence_concat operator only accepts sequence "
-                   "or a nested sequence as its input.");
-    auto ins_dims = ctx->GetInputsDim("X");
-    framework::DDim out_dims = ins_dims[0];
-    const size_t n = ins_dims.size();
-    for (size_t i = 1; i < n; ++i) {
-      out_dims[axis] += ins_dims[i][axis];
-    }
-    ctx->SetOutputDim("Out", out_dims);
+  void Make() override {
+    AddInput("X", "The inputs of sequence concat op").AsDuplicable();
+    AddOutput("Out", "The output of sequence concat op");
+    AddComment(
+        "Sequence Concat Op\n"
+        "It will concat LoD tensors by its sequence information.\n"
+        "For example:\n"
+        "  LoD of X1 = [0, 3, 7]\n"
+        "  LoD of X2 = [0, 7, 9]\n"
+        "  Result LoD is [0, (3+7), (7+9)]\n"
+        "            i.e.[0, 10, 16]\n");
   }
 };
 
-class SequenceConcatOpMaker : public framework::OpProtoAndCheckerMaker {
+class SeqConcatShapeInferer : public framework::InferShapeBase {
  public:
-  void Make() override {
-    AddInput("X",
-             "(LodTensorArray) Input is a vector of LoDTensor, "
-             "each of which is a variable-length sequence or nested sequence.")
-        .AsDuplicable();
-    AddOutput("Out",
-              "(LoDTensor), Variable-length output of "
-              "sequence_concat Op.");
-    AddAttr<int>("axis",
-                 "(int, default 0) "
-                 "The axis along which the inputs will be joined. "
-                 "If axis is 0, the inputs will be joined with LoD index.")
-        .SetDefault(0);
-    AddAttr<int>("level",
-                 "(int, default 0) "
-                 "The level at which the inputs will be joined. "
-                 "If the level is 0, the inputs will be joined at the nested "
-                 "sequence level. "
-                 "If the level is 1, the inputs will be joined at the "
-                 "sequence level. "
-                 "The level should be less than the level number of inputs.")
-        .SetDefault(0);
-    AddComment(R"DOC(
-The sequence_concat operator concatenates multiple LoDTensors.
-It only supports sequence (LoD Tensor with level number is 1)
-or a nested sequence (LoD tensor with level number is 2) as its input.
-- Case1:
-  If the axis is other than 0(here, axis is 1 and level is 1),
-  each input should have the same LoD information and the LoD
-  information of the output keeps the same as the input.
-
-  LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)
-  LoD(x1) = {{0,2,4}, {0,1,2,3,4}}; Dims(x1) = (4,4,4)
-  LoD(Out) = {{0,2,4}, {0,1,2,3,4}}; Dims(Out) = (4,7,4)
-
-- Case2:
-  If the axis is 0(here, leve is 0), the inputs are concatenated along
-  time steps, the LoD information of the output need to re-compute.
-  The LoD information of level-1 should be same.
-
-  LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)
-  LoD(x1) = {{0,2,4}, {0,1,3,5,7}}; Dims(x1) = (7,3,4)
-  LoD(Out) = {{0,2,4}, {0,2,5,8,11}}; Dims(Out) = (11,3,4)
-
-- Case3:
-  If the axis is 0(here, level is 1).
-
-  LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)
-  LoD(x1) = {{0,3,4}, {0,1,3,5,7}}; Dims(x1) = (7,3,4)
-  LoD(Out) = {{0,5,8}, {0,1,2,3,5,7,8,9,11}}; Dims(Out) = (11,3,4)
-
-- Case4:
-  If the LoD number is 1, axis is 0, level is 0
-
-  LoD(x0) = {{0,1,2,3,4}}; Dims(x0) = (4,3,4)
-  LoD(x1) = {{0,1,3,5,7}}; Dims(x1) = (7,3,4)
-  LoD(Out) = {{0,2,5,8,11}}; Dims(Out) = (11,3,4)
-
-NOTE: The levels of all the inputs should be the same.
-    )DOC");
+  void operator()(framework::InferShapeContext *context) const override {
+    PADDLE_ENFORCE(context->HasInputs("X"),
+                   "Input(X) of Sequence Concat Op should not be null.");
+    PADDLE_ENFORCE(context->HasOutput("Out"),
+                   "Output(Out) of Sequence Concat Op should not be null.");
+
+    PADDLE_ENFORCE_GT(context->Inputs("X").size(), 1,
+                      "The number of input sequences is at least two.");
+    auto x_dims = context->GetInputsDim("X");
+    int64_t batch_size = 0;
+    int64_t feature_size = 0;
+    std::vector<int64_t> out_dims;
+    for (auto &x_dim : x_dims) {
+      if (out_dims.empty()) {
+        out_dims = framework::vectorize(x_dim);
+      }
+      batch_size += x_dim[0];
+      if (feature_size == 0) {
+        feature_size = framework::product(x_dim) / x_dim[0];
+      } else {
+        PADDLE_ENFORCE_EQ(
+            feature_size, framework::product(x_dim) / x_dim[0],
+            "Inputs of sequence concat must have same feature size");
+      }
+    }
+    if (batch_size < 0) {
+      batch_size = -1;  // Normalize batch size for compile time.
+    }
+    out_dims[0] = batch_size;
+    context->SetOutputDim("Out", framework::make_ddim(out_dims));
+    if (!context->IsRuntime()) {  // Runtime LoD infershape will be computed
+      // in Kernel.
+      context->ShareLoD("X", "Out");
+    }
   }
 };
 
-class SequenceConcatGradOp : public framework::OperatorWithKernel {
+class SeqConcatGradShapeInferer : public framework::InferShapeBase {
  public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
-  void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
-                   "The gradient of Out should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutputs(framework::GradVarName("X")),
-                   "The gradient of X should not be null.");
-    ctx->SetOutputsDim(framework::GradVarName("X"), ctx->GetInputsDim("X"));
+  void operator()(framework::InferShapeContext *context) const override {
+    context->SetOutputsDim(framework::GradVarName("X"),
+                           context->GetInputsDim("X"));
   }
 };
-
 }  // namespace operators
 }  // namespace paddle
 
-namespace ops = paddle::operators;
-REGISTER_OPERATOR(sequence_concat, ops::SequenceConcatOp,
-                  ops::SequenceConcatOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<
-                      false> /* set false to disable empty grad */);
-REGISTER_OPERATOR(sequence_concat_grad, ops::SequenceConcatGradOp);
-REGISTER_OP_CPU_KERNEL(
-    sequence_concat,
-    ops::SequenceConcatOpKernel<paddle::platform::CPUDeviceContext, float>);
-REGISTER_OP_CPU_KERNEL(
-    sequence_concat_grad,
-    ops::SequenceConcatGradOpKernel<paddle::platform::CPUDeviceContext, float>);
+namespace op = paddle::operators;
+
+REGISTER_OPERATOR(sequence_concat, paddle::framework::OperatorWithKernel,
+                  op::SeqConcatOpMaker, op::SeqConcatShapeInferer,
+                  paddle::framework::DefaultGradOpDescMaker<false>);
+template <typename T>
+using Kernel = op::SeqConcatKernel<paddle::platform::CPUDeviceContext, T>;
+REGISTER_OP_CPU_KERNEL(sequence_concat, Kernel<float>, Kernel<double>);
+REGISTER_OPERATOR(sequence_concat_grad, paddle::framework::OperatorWithKernel,
+                  op::SeqConcatGradShapeInferer);
+template <typename T>
+using GradKernel =
+    op::SeqConcatGradKernel<paddle::platform::CPUDeviceContext, T>;
+REGISTER_OP_CPU_KERNEL(sequence_concat_grad, GradKernel<float>,
+                       GradKernel<double>);

paddle/fluid/operators/sequence_concat_op.cu.cc

-/* 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. */
+// Copyright (c) 2018 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/sequence_concat_op.h"
 
-namespace ops = paddle::operators;
-REGISTER_OP_CUDA_KERNEL(
-    sequence_concat,
-    ops::SequenceConcatOpKernel<paddle::platform::CUDADeviceContext, float>);
-REGISTER_OP_CUDA_KERNEL(sequence_concat_grad,
-                        ops::SequenceConcatGradOpKernel<
-                            paddle::platform::CUDADeviceContext, float>);
+template <typename T>
+using Kernel =
+    paddle::operators::SeqConcatKernel<paddle::platform::CUDADeviceContext, T>;
+REGISTER_OP_CUDA_KERNEL(sequence_concat, Kernel<float>, Kernel<double>);
+template <typename T>
+using GradKernel =
+    paddle::operators::SeqConcatGradKernel<paddle::platform::CUDADeviceContext,
+                                           T>;
+REGISTER_OP_CUDA_KERNEL(sequence_concat_grad, GradKernel<float>,
+                        GradKernel<double>);

paddle/fluid/operators/sequence_concat_op.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. */
+// Copyright (c) 2018 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/fluid/framework/op_registry.h"
-#include "paddle/fluid/operators/strided_memcpy.h"
+#include "paddle/fluid/operators/detail/safe_ref.h"
+#include "paddle/fluid/operators/math/concat.h"
 
 namespace paddle {
 namespace operators {
 
-using Tensor = framework::Tensor;
-using LoDTensor = framework::LoDTensor;
-using LoD = framework::LoD;
-
-template <typename T>
-LoD ConcatLoD(const std::vector<const T*> ins, const size_t level) {
-  auto out_lod = ins[0]->lod();
-  auto numLevels = ins[0]->NumLevels();
-  const size_t n = ins.size();
-  const size_t level_idx = ins[0]->NumLevels() - 1 - level;
-  for (size_t i = 1; i < n; ++i) {
-    for (size_t j = 0; j < ins[i]->lod()[level_idx].size(); ++j) {
-      out_lod[level_idx][j] += ins[i]->lod()[level_idx][j];
+namespace detail {
+template <typename Container>
+inline framework::LoD ConcatLoD(const Container &xs,
+                                std::vector<framework::Tensor> *xs_in_order) {
+  std::vector<size_t> result;
+  result.resize(xs[0].get().lod()[0].size());
+
+  for (size_t i = 1; i < result.size(); ++i) {
+    size_t sum = 0;
+    for (size_t j = 0; j < xs.size(); ++j) {
+      auto &x_lod = xs[j].get().lod()[0];
+      const framework::Tensor &tensor = xs[j].get();
+      xs_in_order->emplace_back(tensor.Slice(x_lod[i - 1], x_lod[i]));
+      sum += x_lod[i];
     }
+    result[i] = sum;
   }
-
-  for (size_t i = level_idx; i < numLevels - 1; ++i) {
-    size_t lod_len = 1;
-    for (size_t j = 0; j < n; ++j) {
-      lod_len += ins[j]->lod()[i + 1].size() - 1;
-    }
-    out_lod[i + 1].clear();
-    out_lod[i + 1].resize(lod_len);
-
-    size_t idx = 1;
-    for (size_t j = 0; j < ins[0]->lod()[i].size() - 1; ++j) {
-      for (size_t k = 0; k < n; ++k) {
-        for (size_t m = ins[k]->lod()[i][j]; m < ins[k]->lod()[i][j + 1]; ++m) {
-          out_lod[i + 1][idx] = out_lod[i + 1][idx - 1] +
-                                ins[k]->lod()[i + 1][m + 1] -
-                                ins[k]->lod()[i + 1][m];
-          idx++;
-        }
-      }
-    }
-  }
-
-  return out_lod;
+  framework::LoD lod;
+  lod.emplace_back(result);
+  return lod;
 }
+}  // namespace detail
 
 template <typename DeviceContext, typename T>
-class SequenceConcatOpKernel : public framework::OpKernel<T> {
+class SeqConcatKernel : public framework::OpKernel<T> {
  public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    auto ins = ctx.MultiInput<LoDTensor>("X");
-    auto* out = ctx.Output<LoDTensor>("Out");
-    const size_t axis = static_cast<size_t>(ctx.Attr<int>("axis"));
-    const size_t level = static_cast<size_t>(ctx.Attr<int>("level"));
-    const size_t n = ins.size();
-
-    for (size_t i = 1; i < n; ++i) {
-      PADDLE_ENFORCE_EQ(ins[0]->NumLevels(), ins[i]->NumLevels(),
-                        "The levels of all the input LoDTensors "
-                        "should be the same.");
-      PADDLE_ENFORCE_EQ(ins[0]->dims().size(), ins[i]->dims().size(),
-                        "The dimension size of all the input LoDTensors "
-                        "should be the same.");
-
-      const size_t dims_size = ins[i]->dims().size();
-      for (size_t j = 0; j < dims_size; ++j) {
-        if (j == axis) continue;
-        PADDLE_ENFORCE_EQ(ins[0]->dims()[j], ins[i]->dims()[j],
-                          "Except for the dimension of the specified "
-                          "axis along which all the inputs are concatenated, "
-                          "dimensions of all the other axises of the input "
-                          "LoDTensors should be the same.");
-      }
-    }
-    PADDLE_ENFORCE_GT(ins[0]->NumLevels(), level,
-                      "The levels of all the input LoDTensors "
-                      "should be greater than the specify level");
-
-    out->mutable_data<T>(ctx.GetPlace());
-    auto out_lod = ins[0]->lod();
-    if (axis == 0) {
-      out_lod = ConcatLoD<LoDTensor>(ins, level);
-    }
-    out->set_lod(out_lod);
-
-    const size_t level_idx = out_lod.size() - level - 1;
-    auto out_lod_level = framework::ToAbsOffset(out_lod)[level_idx];
-    for (size_t i = 0; i < out_lod_level.size() - 1; ++i) {
-      Tensor out_t = out->Slice(static_cast<int>(out_lod_level[i]),
-                                static_cast<int>(out_lod_level[i + 1]));
-      auto out_stride = framework::stride(out_t.dims());
-      size_t offset = 0;
-      for (size_t j = 0; j < n; ++j) {
-        auto in_lod_level = framework::ToAbsOffset(ins[j]->lod())[level_idx];
-        auto in_stride = framework::stride(ins[j]->dims());
-        Tensor in_t = ins[j]->Slice(static_cast<int>(in_lod_level[i]),
-                                    static_cast<int>(in_lod_level[i + 1]));
-        size_t axis_dim = in_t.dims()[axis];
-        StridedMemcpy<T>(ctx.device_context(), in_t.data<T>(), in_stride,
-                         in_t.dims(), out_stride, out_t.data<T>() + offset);
-        offset += axis_dim * in_stride[axis];
+  void Compute(const framework::ExecutionContext &context) const override {
+    auto xs = detail::VectorRef(context.MultiInput<framework::LoDTensor>("X"),
+                                "Cannot find multiple input X");
+    auto &out = detail::Ref(context.Output<framework::LoDTensor>("Out"),
+                            "Cannot find output");
+
+    size_t lod_size = 0;
+    for (auto &x : xs) {
+      if (lod_size == 0) {
+        lod_size = x.get().lod()[0].size();
+      } else {
+        PADDLE_ENFORCE_EQ(
+            lod_size, x.get().lod()[0].size(),
+            "The number of sequence must be same between each input");
       }
     }
+    PADDLE_ENFORCE_NE(lod_size, 0, "Each input must have sequence information");
+
+    std::vector<framework::Tensor> x_in_order;
+    out.set_lod(detail::ConcatLoD(xs, &x_in_order));
+    out.mutable_data<T>(context.GetPlace());
+    math::ConcatFunctor<DeviceContext, T> functor;
+    functor(context.template device_context<DeviceContext>(), x_in_order, 0,
+            &out);
   }
 };
 
 template <typename DeviceContext, typename T>
-class SequenceConcatGradOpKernel : public framework::OpKernel<T> {
+class SeqConcatGradKernel : public framework::OpKernel<T> {
  public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    auto ins = ctx.MultiInput<framework::LoDTensor>("X");
-    auto* out_grad =
-        ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"));
-    auto x_grads =
-        ctx.MultiOutput<framework::LoDTensor>(framework::GradVarName("X"));
-    size_t axis = static_cast<size_t>(ctx.Attr<int>("axis"));
-    size_t level = static_cast<size_t>(ctx.Attr<int>("level"));
-    const size_t n = x_grads.size();
-
-    // Set Grad(X) LoD as X
-    for (size_t i = 0; i < n; i++) {
-      x_grads[i]->set_lod(ins[i]->lod());
-      x_grads[i]->mutable_data<T>(ctx.GetPlace());
+  void Compute(const framework::ExecutionContext &context) const override {
+    auto xs = context.MultiInput<framework::LoDTensor>("X");
+    auto dxs =
+        context.MultiOutput<framework::LoDTensor>(framework::GradVarName("X"));
+    PADDLE_ENFORCE_EQ(xs.size(), dxs.size());
+    for (size_t i = 0; i < dxs.size(); ++i) {
+      if (dxs[i] != nullptr) {
+        dxs[i]->set_lod(xs[i]->lod());
+        dxs[i]->mutable_data<T>(context.GetPlace());
+      }
     }
-    auto out_lod = ins[0]->lod();
-    if (axis == 0UL) {
-      out_lod = ConcatLoD<LoDTensor>(ins, level);
+    std::vector<framework::Tensor> sliced_x;
+    std::vector<boost::variant<boost::blank, framework::Tensor>> sliced_dx;
+
+    for (size_t i = 1; i < xs[0]->lod()[0].size(); ++i) {
+      for (size_t j = 0; j < xs.size(); ++j) {
+        const framework::LoDTensor *x = xs[j];
+        framework::LoDTensor *dx = dxs[j];
+        auto &x_lod = x->lod()[0];
+        sliced_x.emplace_back(x->Slice(x_lod[i - 1], x_lod[i]));
+        if (dx != nullptr) {
+          sliced_dx.emplace_back(dx->Slice(x_lod[i - 1], x_lod[i]));
+        } else {
+          sliced_dx.emplace_back(boost::blank());
+        }
+      }
     }
-    const size_t level_idx = out_lod.size() - level - 1;
-    auto out_lod_level = framework::ToAbsOffset(out_lod)[level_idx];
 
-    for (size_t i = 0; i < out_lod_level.size() - 1; ++i) {
-      Tensor out_grad_t =
-          out_grad->Slice(static_cast<int>(out_lod_level[i]),
-                          static_cast<int>(out_lod_level[i + 1]));
-      auto out_grad_stride = framework::stride(out_grad_t.dims());
-      size_t offset = 0;
+    math::ConcatGradFunctor<DeviceContext, T> functor;
+    std::vector<const framework::Tensor *> sliced_x_ptr;
+    std::vector<framework::Tensor *> sliced_dx_ptr;
+    for (auto &x : sliced_x) {
+      sliced_x_ptr.emplace_back(&x);
+    }
 
-      for (size_t j = 0; j < n; ++j) {
-        auto x_grad_lod_level =
-            framework::ToAbsOffset(x_grads[j]->lod())[level_idx];
-        auto x_grad_stride = framework::stride(x_grads[j]->dims());
-        Tensor x_grad_t =
-            x_grads[j]->Slice(static_cast<int>(x_grad_lod_level[i]),
-                              static_cast<int>(x_grad_lod_level[i + 1]));
-        size_t axis_dim = x_grad_t.dims()[axis];
-        StridedMemcpy<T>(ctx.device_context(), out_grad_t.data<T>() + offset,
-                         out_grad_stride, out_grad_t.dims(), x_grad_stride,
-                         x_grad_t.data<T>());
-        offset += axis_dim * out_grad_stride[axis];
+    for (auto &dx : sliced_dx) {
+      try {
+        sliced_dx_ptr.emplace_back(&boost::get<framework::Tensor>(dx));
+      } catch (boost::bad_get &) {
+        sliced_dx_ptr.emplace_back(nullptr);
       }
     }
+    functor(context.template device_context<DeviceContext>(),
+            detail::Ref(
+                context.Input<framework::Tensor>(framework::GradVarName("Out")),
+                "Sequence Concat OG must be set"),
+            sliced_x_ptr, 0, &sliced_dx_ptr);
   }
 };
 

python/paddle/fluid/layers/nn.py

@@ -113,6 +113,7 @@ __all__ = [
     'pad2d',
     'unstack',
     'sequence_enumerate',
+    'sequence_concat',
 ]
 
 
@@ -1781,6 +1782,31 @@ def sequence_pool(input, pool_type):
     return pool_out
 
 
+@templatedoc()
+def sequence_concat(input, name=None):
+    """
+    ${comment}
+
+    Args:
+        input(list): List of Variables to be concatenated.
+        name(str|None): A name for this layer(optional). If set None, the layer
+                       will be named automatically.
+
+    Returns:
+        Variable: Output variable of the concatenation.
+
+    Examples:
+        .. code-block:: python
+
+           out = fluid.layers.sequence_concat(input=[seq1, seq2, seq3])
+    """
+    helper = LayerHelper('sequence_concat', **locals())
+    out = helper.create_tmp_variable(dtype=helper.input_dtype())
+    helper.append_op(
+        type='sequence_concat', inputs={'X': input}, outputs={'Out': [out]})
+    return out
+
+
 def sequence_first_step(input):
     """
     This function gets the first step of sequence.

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

-#   Copyright (c) 2018 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
-import sys
-from op_test import OpTest
-
-
-def to_abs_offset_lod(lod):
-    offset_lod = [[0] for i in lod]
-    for i, level in enumerate(lod):
-        for seq_len in level:
-            offset_lod[i].append(offset_lod[i][-1] + seq_len)
-
-    if len(offset_lod) == 0 or len(offset_lod) == 1:
-        return offset_lod
-    import copy
-    new_offset_lod = copy.deepcopy(offset_lod)
-    for idx, val in enumerate(offset_lod[0]):
-        new_offset_lod[0][idx] = offset_lod[1][val]
-    return new_offset_lod
-
-
-def seq_concat(inputs, level):
-    lod0 = inputs['X'][0][1][1]
-    lod1 = inputs['X'][1][1][1]
-    x0 = inputs['X'][0][1][0]
-    x1 = inputs['X'][1][1][0]
-    level_idx = len(lod0) - level - 1
-    outs = []
-    for i in range(len(lod0[level_idx])):
-        sub_x0 = x0[to_abs_offset_lod(lod0)[level_idx][i]:to_abs_offset_lod(
-            lod0)[level_idx][i + 1], :]
-        sub_x1 = x1[to_abs_offset_lod(lod1)[level_idx][i]:to_abs_offset_lod(
-            lod1)[level_idx][i + 1], :]
-        outs.append(np.concatenate((sub_x0, sub_x1), axis=0))
-    return np.concatenate(outs, axis=0)
-
-
-class TestSeqConcatOp(OpTest):
-    def set_data(self):
-        # two level, batch size is 3
-        x0 = np.random.random((4, 6, 3)).astype('float32')
-        lod0 = [[2, 2], [1, 1, 1, 1]]
-        x1 = np.random.random((4, 8, 3)).astype('float32')
-        lod1 = [[2, 2], [1, 1, 1, 1]]
-        axis = 1
-        level = 1
-        self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
-        self.attrs = {'axis': axis, 'level': level}
-        self.outputs = {'Out': (np.concatenate([x0, x1], axis=1), lod0)}
-
-    def setUp(self):
-        self.op_type = "sequence_concat"
-        self.set_data()
-
-    def test_check_output(self):
-        self.check_output()
-
-    def test_check_grad(self):
-        self.check_grad(['x0'], 'Out')
-
-
-class TestSeqConcatOpLevelZeroNestedSequence(TestSeqConcatOp):
-    def set_data(self):
-        # two level, batch size is 3
-        x0 = np.random.random((4, 6, 3)).astype('float32')
-        lod0 = [[2, 2], [1, 1, 1, 1]]
-        x1 = np.random.random((7, 6, 3)).astype('float32')
-        lod1 = [[2, 2], [1, 2, 2, 2]]
-        axis = 0
-        level = 0
-        self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
-        self.attrs = {'axis': axis, 'level': level}
-        out_lod = [[2, 2], [2, 3, 3, 3]]
-        self.outputs = {'Out': (seq_concat(self.inputs, level), out_lod)}
-
-
-class TestSeqConcatOplevelOneNestedSequence(TestSeqConcatOp):
-    def set_data(self):
-        # two level, batch size is 3
-        x0 = np.random.random((4, 6, 3)).astype('float32')
-        lod0 = [[2, 2], [1, 1, 1, 1]]
-        x1 = np.random.random((7, 6, 3)).astype('float32')
-        lod1 = [[3, 1], [1, 2, 2, 2]]
-        axis = 0
-        level = 1
-        self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
-        self.attrs = {'axis': axis, 'level': level}
-        out_lod = [[5, 3], [1, 1, 1, 2, 2, 1, 1, 2]]
-        self.outputs = {'Out': (seq_concat(self.inputs, level), out_lod)}
-
-
-class TestSeqConcatOpLevelZeroSequence(TestSeqConcatOp):
-    def set_data(self):
-        # two level, batch size is 3
-        x0 = np.random.random((4, 3, 4)).astype('float32')
-        lod0 = [[1, 1, 1, 1]]
-        x1 = np.random.random((7, 3, 4)).astype('float32')
-        lod1 = [[1, 2, 2, 2]]
-        axis = 0
-        level = 0
-        self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
-        self.attrs = {'axis': axis, 'level': level}
-        out_lod = [[2, 3, 3, 3]]
-        self.outputs = {'Out': (seq_concat(self.inputs, level), out_lod)}
-
-
-if __name__ == '__main__':
-    unittest.main()

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

+# Copyright (c) 2018 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
+
+
+class TestSequenceConcat(OpTest):
+    def setUp(self):
+        x1 = np.random.random(size=(10, 80))
+        lod1 = [7, 3]
+        x2 = np.random.random(size=(20, 80))
+        lod2 = [12, 8]
+
+        out = np.concatenate((x1[0:lod1[0]], x2[0:lod2[0]], x1[lod1[0]:],
+                              x2[lod2[0]:]))
+        out_lod = [19, 11]
+
+        self.op_type = "sequence_concat"
+        self.inputs = {'X': [("x1", (x1, [lod1])), ("x2", (x2, [lod2]))]}
+        self.outputs = {"Out": (out, [out_lod])}
+
+    def test_output(self):
+        self.check_output(1e-3)
+
+    def test_dx(self):
+        self.check_grad(inputs_to_check=['x1', 'x2'], output_names="Out")
+
+
+if __name__ == '__main__':
+    unittest.main()