Merge pull request #4814 from chengduoZH/Add_sequence_project_op

Add sequence_conv_op  and sequence_projection functor

paddle/operators/CMakeLists.txt

@@ -123,6 +123,7 @@ set(DEPS_OPS
     sum_op
     pool_op
     pool_with_index_op
+    sequence_conv_op
     lstm_op)
 
 
@@ -134,6 +135,7 @@ op_library(softmax_with_cross_entropy_op DEPS cross_entropy softmax)
 op_library(sum_op DEPS net_op)
 op_library(pool_op DEPS pooling)
 op_library(pool_with_index_op DEPS pooling)
+op_library(sequence_conv_op DEPS context_project)
 op_library(lstm_op DEPS sequence2batch lstm_compute)
 
 list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})

paddle/operators/math/CMakeLists.txt

@@ -9,6 +9,7 @@ if(WITH_GPU)
     nv_library(cross_entropy SRCS cross_entropy.cc cross_entropy.cu DEPS operator)
     nv_library(pooling SRCS pooling.cc pooling.cu DEPS device_context)
     nv_library(vol2col SRCS vol2col.cc vol2col.cu DEPS device_context)
+    nv_library(context_project SRCS context_project.cc context_project.cu DEPS device_context)
     nv_library(sequence2batch SRCS sequence2batch.cc sequence2batch.cu DEPS device_context)
     nv_library(lstm_compute SRCS lstm_compute.cc lstm_compute.cu DEPS device_context activation_functions)
 else()
@@ -18,6 +19,7 @@ else()
     cc_library(cross_entropy SRCS cross_entropy.cc DEPS operator)
     cc_library(pooling SRCS pooling.cc DEPS device_context)
     cc_library(vol2col SRCS vol2col.cc DEPS device_context)
+    cc_library(context_project SRCS context_project.cc DEPS device_context)
     cc_library(sequence2batch SRCS sequence2batch.cc DEPS device_context)
     cc_library(lstm_compute SRCS lstm_compute.cc DEPS device_context activation_functions)
 endif()

paddle/operators/math/context_project.cc

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

paddle/operators/math/context_project.cu

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

paddle/operators/math/context_project.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/lod_tensor.h"
+#include "paddle/framework/tensor.h"
+#include "paddle/operators/math/im2col.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
+/*
+ * \brief Context projection concatenate features in adjacent time steps in
+ * a sequence. The i-th row of the output is the concatenation of
+ * context_length rows of the input. The context_length rows are the
+ * consecutive rows from the i+shift_start row.
+
+ * \param in            Input data.
+ * \param Shape         The shape of Input data,
+ *                      [minibatch, number_of_input_features].
+ * \param type          A float LoDTensor.
+ *
+ * \param padding_data  Padding data.
+ * \param Shape         The shape of Padding data,
+ *                      [up_pad + down_pad, number_of_input_features].
+ * \param type          A float Tensor.
+ *
+ * \param col           Col data.
+ * \param Shape         The shape of Col data,
+ *                      [minibatch, context_length * number_of_input_features].
+ * \param type           A float Tensor.
+ *
+ * For a mini-batch of 2 variable lengths sentences, containing 3, and 1
+ * time-steps:
+ *
+ * Assumed input (X) is a [4, M, N] float LoDTensor, and X->lod()[0] = [0, 3,
+ * 4].
+ * Besides, for the sake of simplicity, we assume M=1 and N=2.
+ *
+ * X = [[a1, a2;
+ *       b1, b2;
+ *       c1, c2]
+ *      [d1, d2]]
+ *
+ * This is to say that input (X) has 4 words and the dimension of each word
+ * representation is 2.
+ *
+ * - Case1:
+ * If context_start is -1 and padding_trainable is false, we use zero to pad
+ * instead of learned weight to pad,
+ * and the context_lenth is 3, the output (Out) is:
+ *
+ * Out =[[0,  0,  a1, a2, b1, b2;
+ *        a1, a2, b1, b2, c1, c2;
+ *        b1, b2, c1, c2, 0,  0 ]
+ *       [0,  0,  d1, d2, 0,  0 ]]
+ *
+ * - Case2:
+ * If context_start is -1 and padding_trainable is true, we use learned weight
+ * to pad,
+ * and the context_lenth is 3, the output (Out) is:
+ *
+ * Out = [[w1, w2, a1, a2, b1, b2;
+ *         a1, a2, b1, b2, c1, c2;
+ *         b1, b2, c1, c2, w3, w4]
+ *        [w1, w2, d1, d2, w3, w4]]
+ *
+ */
+
+template <typename Place, typename T>
+class ContextProjectFunctor {
+ public:
+  void operator()(const platform::DeviceContext& context,
+                  framework::LoDTensor& in, framework::Tensor& padding_data,
+                  framework::Tensor& col, bool padding_trainable,
+                  int context_start, int context_length, int context_stride,
+                  int up_pad, int down_pad, bool gradient, bool input_grad,
+                  bool pad_grad) {
+    auto lod_level_0 = in.lod()[0];
+
+    paddle::operators::math::Im2ColFunctor<
+        paddle::operators::math::ColFormat::kOCF, Place, float>
+        im2col_ocf;
+    paddle::operators::math::Col2ImFunctor<
+        paddle::operators::math::ColFormat::kOCF, Place, float>
+        col2im_ocf;
+
+    int input_row_begin, input_row_end;
+    int sequence_height, sequence_width;
+    sequence_width = in.dims()[1];
+    input_grad = gradient && input_grad;
+    pad_grad = gradient && pad_grad;
+
+    if (!gradient || input_grad) {
+      for (int i = 0; i < static_cast<int>(lod_level_0.size()) - 1; ++i) {
+        input_row_begin = (context_start > 0)
+                              ? static_cast<int>(lod_level_0[i]) + context_start
+                              : static_cast<int>(lod_level_0[i]);
+        input_row_end = static_cast<int>(lod_level_0[i + 1]);
+
+        framework::Tensor out_t =
+            col.Slice(static_cast<int>(lod_level_0[i]),
+                      static_cast<int>(lod_level_0[i + 1]));
+
+        sequence_height = static_cast<int>(out_t.dims()[0]);
+
+        if (input_row_begin < input_row_end) {
+          framework::Tensor in_t = in.Slice(input_row_begin, input_row_end);
+
+          std::vector<int64_t> output_shape(
+              {sequence_height, 1, 1, context_length,
+               sequence_width});  // output_height, output_width,
+          // input_channels, filter_height, filter_width
+
+          out_t.Resize(framework::make_ddim(output_shape));
+
+          std::vector<int64_t> input_shape(
+              {1, input_row_end - input_row_begin,
+               sequence_width});  // input_channels, input_height, input_width
+          in_t.Resize(framework::make_ddim(input_shape));
+
+          if (gradient) {
+            col2im_ocf(context, in_t, out_t,
+                       /*stride_height*/ context_stride, /*stride_width*/ 1,
+                       up_pad, down_pad, 0, 0);
+          } else {
+            im2col_ocf(context, in_t, out_t,
+                       /*stride_height*/ context_stride, /*stride_width*/ 1,
+                       up_pad, down_pad, 0, 0);
+          }
+          out_t.Resize({sequence_height, context_length * sequence_width});
+        }
+      }
+    }
+    if (!gradient || pad_grad) {
+      if (padding_trainable) {
+        for (int i = 0; i < static_cast<int>(lod_level_0.size()) - 1; ++i) {
+          framework::Tensor out_t =
+              col.Slice(static_cast<int>(lod_level_0[i]),
+                        static_cast<int>(lod_level_0[i + 1]));
+
+          sequence_height = static_cast<int>(out_t.dims()[0]);
+
+          // add up trainable data
+          out_t.Resize({sequence_height * context_length, sequence_width});
+
+          if (up_pad > 0) {  // add up pad
+            int padding_rows = std::min(
+                up_pad, static_cast<int>(lod_level_0[i + 1] - lod_level_0[i]));
+
+            for (int k = 0; k < padding_rows; ++k) {
+              int padding_size =
+                  k + context_length < up_pad ? context_length : up_pad - k;
+              framework::Tensor out_t_sub = out_t.Slice(
+                  k * context_length, k * context_length + padding_size);
+              framework::Tensor w_sub = padding_data.Slice(k, k + padding_size);
+              // in this block, using EigenVector<T>::Flatten is ok too.
+              auto out_t_sub_e = EigenMatrix<T>::From(out_t_sub);
+              auto w_sub_e = EigenMatrix<T>::From(w_sub);
+              if (gradient) {
+                w_sub_e.device(*context.GetEigenDevice<Place>()) =
+                    w_sub_e + out_t_sub_e;
+              } else {
+                out_t_sub_e.device(*context.GetEigenDevice<Place>()) = w_sub_e;
+              }
+            }
+          }
+          if (down_pad > 0) {  // add down pad
+            int down_pad_begin_row =
+                std::max(
+                    0, (sequence_height - context_start - context_length) + 1) +
+                1;
+            int padding_begin = std::max(0, context_start - sequence_height);
+            int padding_size =
+                sequence_height - context_start >= context_length
+                    ? 1
+                    : context_length - (sequence_height - context_start);
+            if (context_start >= sequence_height) padding_size = context_length;
+            int padding_idx = padding_begin;
+            for (int t = 0; t + down_pad_begin_row <= sequence_height;
+                 ++t, ++padding_size) {
+              if (context_start >= sequence_height)
+                padding_size = context_length;
+              if (padding_size > context_length) {
+                padding_size = context_length;
+                padding_idx++;
+              }
+              if (padding_begin > 0 || sequence_height == context_start)
+                padding_idx = padding_begin + t;
+              framework::Tensor out_t_sub = out_t.Slice(
+                  (down_pad_begin_row + t) * context_length - padding_size,
+                  (down_pad_begin_row + t) * context_length);
+              framework::Tensor w_sub = padding_data.Slice(
+                  up_pad + padding_idx, up_pad + padding_idx + padding_size);
+              auto out_t_sub_e = EigenMatrix<T>::From(out_t_sub);
+              auto w_sub_e = EigenMatrix<T>::From(w_sub);
+              if (gradient) {
+                w_sub_e.device(*context.GetEigenDevice<Place>()) =
+                    w_sub_e + out_t_sub_e;
+              } else {
+                out_t_sub_e.device(*context.GetEigenDevice<Place>()) = w_sub_e;
+              }
+            }
+          }
+          out_t.Resize({sequence_height, context_length * sequence_width});
+        }
+      }
+    }
+  }
+};
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/operators/sequence_conv_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/operators/sequence_conv_op.h"
+
+namespace paddle {
+namespace operators {
+
+class SequenceConvOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of SequenceConvOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Filter"),
+                   "Input(Filter) of SequenceConvOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of SequenceConvOp should not be null.");
+
+    int context_length = ctx->Attrs().Get<int>("context_length");
+    bool padding_trainable = ctx->Attrs().Get<bool>("padding_trainable");
+    int context_start = ctx->Attrs().Get<int>("context_start");
+
+    auto in_dims = ctx->GetInputDim("X");
+    auto filter_dims = ctx->GetInputDim("Filter");
+    PADDLE_ENFORCE(in_dims.size() == 2 && filter_dims.size() == 2,
+                   "Input(X, Filter) should be 2-D tensor.");
+    PADDLE_ENFORCE(filter_dims[0] == context_length * in_dims[1],
+                   "Filter's height should be context_length * "
+                   "number_of_input_features .");
+
+    if (padding_trainable) {
+      PADDLE_ENFORCE(
+          ctx->HasInput("PaddingData"),
+          "Input(PaddingData) of SequenceConvOp should not be null.");
+      framework::DDim padding_dim = ctx->GetInputDim("PaddingData");
+      int up_pad = std::max(0, -context_start);
+      int down_pad = std::max(0, context_start + context_length - 1);
+      int total_pad = up_pad + down_pad;
+      int input_width = static_cast<int>(in_dims[1]);
+
+      if (context_start == 0 && context_length == 1) {
+        PADDLE_THROW(
+            "If context_start is 0 and context_length is 1, padding_trainable "
+            "should be false.");
+      }
+      PADDLE_ENFORCE(padding_dim.size() == 2,
+                     "Input(PaddingData) should be 2-D tensor.");
+      PADDLE_ENFORCE(
+          padding_dim[0] == total_pad && padding_dim[1] == input_width,
+          "Input(PaddingData)'s shape is not consistent with 'context_start' "
+          "and 'context_length'.");
+    }
+
+    in_dims[1] = filter_dims[1];
+    ctx->SetOutputDim("Out", in_dims);
+    ctx->ShareLoD("X", "Out");
+  }
+};
+
+class SequenceConvGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
+                   "Gradient of output(Out) should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("X"), "The input(X) should not be null.");
+
+    if (ctx->Attrs().Get<bool>("padding_trainable") &&
+        ctx->HasOutput(framework::GradVarName("PaddingData"))) {
+      ctx->SetOutputDim(framework::GradVarName("PaddingData"),
+                        ctx->GetInputDim("PaddingData"));
+    }
+    if (ctx->HasOutput(framework::GradVarName("X"))) {
+      ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
+    }
+    if (ctx->HasOutput(framework::GradVarName("Filter"))) {
+      ctx->SetOutputDim(framework::GradVarName("Filter"),
+                        ctx->GetInputDim("Filter"));
+    }
+  }
+};
+
+class SequenceConvOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  SequenceConvOpMaker(framework::OpProto* proto,
+                      framework::OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput(
+        "X",
+        "(LoDTensor) the input(X) is a LodTensor, which support "
+        "variable-time length input sequence. The underlying tensor in "
+        "this LoDTensor is a matrix with shape (T, D), where, T is the "
+        "total time steps in this mini-batch, D is the input feature size.");
+    AddInput("PaddingData",
+             "(Tensor, optional) the input(PaddingData) is an optional "
+             "parameter, and it is learnable. "
+             "This is a tensor with shape (N, D), where N is the "
+             "top_pad + bottom_pad, D is the input feature size. In order to "
+             "ensure the equal length of sequence before and after "
+             "convolution, it is necessary to fill the top and bottom of each "
+             "sequence according to context_length, context_stride and "
+             "context_start")
+        .AsDispensable();
+    AddInput("Filter",
+             "(Tensor) the input(Filter) is an learnable parameter."
+             "This is a tensor with shape (N, D), where N is the "
+             "context_length, D is the output feature size.");
+    AddOutput(
+        "Out",
+        "(LoDTensor) the output(Out) is a LodTensor, which support "
+        "variable-time length output sequence. The underlying tensor in "
+        "this LoDTensor is a matrix with shape (T, D), where, T is the "
+        "total time steps in this mini-batch, D is the output feature size.");
+
+    AddAttr<bool>("padding_trainable",
+                  "(bool, default false) the padding data of SequenceConvOp "
+                  "is trainable or not.")
+        .SetDefault(false);
+    AddAttr<int>("context_length",
+                 "(int, default 3) the context_length of SequenceConvOp is the "
+                 "height of the convolution kernel.")
+        .SetDefault(3)
+        .GreaterThan(0);
+    AddAttr<int>("context_start",
+                 "(int, default 0) the context_start of SequenceConvOp "
+                 "represents the beginning of the convolution of the number of "
+                 "rows of sequence, which can be negative.")
+        .SetDefault(0);
+    AddAttr<int>("context_stride",
+                 "(int, default 1) the context_stride of SequenceConvOp "
+                 "represents the step length of convolution. "
+                 "Currently, SequenceConvOp only supports"
+                 "context_stride=1.")
+        .SetDefault(1)
+        .GreaterThan(0);
+
+    AddComment(R"DOC(
+    SequenceConvOp performs convolution operation on features of
+    context_length time-steps of each instance.
+    The convolution operation calculates the output based on the input, filter
+    and strides, paddings parameters. The size of each dimension of the
+    parameters is checked in the infer-shape. In order to ensure the equal
+    length of sequence before and after convolution, it is necessary to fill
+    the top and bottom of each sequence according to context_length,
+    context_stride and context_start.
+    )DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(sequence_conv, ops::SequenceConvOp, ops::SequenceConvOpMaker,
+            sequence_conv_grad, ops::SequenceConvGradOp);
+
+REGISTER_OP_CPU_KERNEL(
+    sequence_conv, ops::SequenceConvKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    sequence_conv_grad,
+    ops::SequenceConvGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/sequence_conv_op.cu

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

paddle/operators/sequence_conv_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/operators/math/context_project.h"
+#include "paddle/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+
+template <typename Place, typename T>
+class SequenceConvKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* in = context.Input<LoDTensor>("X");
+    auto* out = context.Output<LoDTensor>("Out");
+    auto filter = *context.Input<Tensor>("Filter");
+
+    out->mutable_data<T>(context.GetPlace());
+    context.ShareLoD("X", "Out");
+
+    int context_start = context.Attr<int>("context_start");
+    int context_length = context.Attr<int>("context_length");
+    int context_stride = context.Attr<int>("context_stride");
+    bool padding_trainable = context.Attr<bool>("padding_trainable");
+
+    // InferShape by in_lod
+    PADDLE_ENFORCE_EQ(in->lod().size(), 1UL,
+                      "Only support one level sequence now.");
+
+    const Tensor* padding_data = nullptr;
+    if (padding_trainable) {
+      padding_data = context.Input<Tensor>("PaddingData");
+    }
+
+    int up_pad = std::max(0, -context_start);
+    int down_pad = std::max(0, context_start + context_length - 1);
+    int sequence_width;
+    sequence_width = static_cast<int>(in->dims()[1]);
+
+    // Use col_shape in the im2col calculation.
+    framework::DDim col_shape = {in->dims()[0],
+                                 sequence_width * context_length};
+    Tensor col;
+    col.mutable_data<T>(col_shape, context.GetPlace());
+    math::SetConstant<Place, T> set_zero;
+    // Because if padding_trainable is false, padding data should be zeros.
+    set_zero(context.device_context(), &col, static_cast<T>(0));
+
+    paddle::operators::math::ContextProjectFunctor<Place, T>
+        seq_project_functor;
+    LoDTensor* input = const_cast<LoDTensor*>(in);
+    Tensor* pad_data = const_cast<Tensor*>(padding_data);
+
+    seq_project_functor(context.device_context(), *input, *pad_data, col,
+                        padding_trainable, context_start, context_length,
+                        context_stride, up_pad, down_pad, false, false, false);
+
+    math::matmul<Place, T>(context.device_context(), col, false, filter, false,
+                           static_cast<T>(1.0), out, static_cast<T>(0.0));
+  }
+};
+
+template <typename Place, typename T>
+class SequenceConvGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* out_g = context.Input<LoDTensor>(framework::GradVarName("Out"));
+    auto* in_g = context.Output<LoDTensor>(framework::GradVarName("X"));
+    auto* filter_g = context.Output<Tensor>(framework::GradVarName("Filter"));
+    auto* padding_data_g =
+        context.Output<Tensor>(framework::GradVarName("PaddingData"));
+    auto* in = context.Input<LoDTensor>("X");
+    auto* filter = context.Input<Tensor>("Filter");
+
+    int context_start = context.Attr<int>("context_start");
+    int context_length = context.Attr<int>("context_length");
+    int context_stride = context.Attr<int>("context_stride");
+    bool padding_trainable = context.Attr<bool>("padding_trainable");
+
+    PADDLE_ENFORCE_EQ(in->lod().size(), 1UL,
+                      "Only support one level sequence now.");
+    auto lod_g_level_0 = in->lod()[0];
+
+    int up_pad = std::max(0, -context_start);
+    int down_pad = std::max(0, context_start + context_length - 1);
+    int sequence_width = static_cast<int>(in->dims()[1]);
+
+    math::SetConstant<Place, T> set_zero;
+    // use col_shape in the im2col calculation
+    framework::DDim col_shape = {in->dims()[0],
+                                 sequence_width * context_length};
+    Tensor col;
+
+    if (in_g || filter_g || (padding_trainable && padding_data_g)) {
+      col.mutable_data<T>(col_shape, context.GetPlace());
+      // Because if padding_trainable is false, padding data should be zeros.
+      set_zero(context.device_context(), &col, static_cast<T>(0));
+      math::matmul<Place, T>(context.device_context(), *out_g, false, *filter,
+                             true, T(1.0), &col, T(1.0));
+    }
+    paddle::operators::math::ContextProjectFunctor<Place, T>
+        seq_project_functor;
+
+    if (in_g) {
+      in_g->mutable_data<T>(context.GetPlace());
+      in_g->set_lod(in->lod());
+      set_zero(context.device_context(), in_g, static_cast<T>(0));
+
+      seq_project_functor(context.device_context(), *in_g, *padding_data_g, col,
+                          padding_trainable, context_start, context_length,
+                          context_stride, up_pad, down_pad, true, true, false);
+    }
+
+    if (padding_trainable && padding_data_g) {
+      padding_data_g->mutable_data<T>(context.GetPlace());
+      set_zero(context.device_context(), padding_data_g, static_cast<T>(0));
+
+      LoDTensor* input = const_cast<LoDTensor*>(in);
+      seq_project_functor(context.device_context(), *input, *padding_data_g,
+                          col, padding_trainable, context_start, context_length,
+                          context_stride, up_pad, down_pad, true, false, true);
+    }
+
+    if (filter_g) {
+      filter_g->mutable_data<T>(context.GetPlace());
+      set_zero(context.device_context(), filter_g, static_cast<T>(0));
+
+      Tensor filter_grad = *filter_g;
+      LoDTensor out_grad = *out_g;
+
+      const Tensor* padding_data = nullptr;
+      if (padding_trainable) {
+        padding_data = context.Input<Tensor>("PaddingData");
+      }
+
+      sequence_width = static_cast<int>(in->dims()[1]);
+
+      LoDTensor* input = const_cast<LoDTensor*>(in);
+      Tensor* pad_data = const_cast<Tensor*>(padding_data);
+
+      seq_project_functor(context.device_context(), *input, *pad_data, col,
+                          padding_trainable, context_start, context_length,
+                          context_stride, up_pad, down_pad, false, false,
+                          false);
+
+      math::matmul<Place, T>(context.device_context(), col, true, out_grad,
+                             false, T(1.0), &filter_grad, T(1.0));
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

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

+import unittest
+import numpy as np
+import random
+from op_test import OpTest
+
+
+class TestSeqProject(OpTest):
+    def setUp(self):
+        self.init_test_case()
+        self.op_type = 'sequence_conv'
+
+        if self.context_length == 1 \
+                and self.context_start == 0 \
+                and self.padding_trainable:
+            print "If context_start is 0 " \
+                  "and context_length is 1," \
+                  " padding_trainable should be false."
+            return
+
+        # one level, batch size
+        x = np.random.uniform(0.1, 1, [self.input_size[0],
+                                       self.input_size[1]]).astype('float32')
+        w = np.random.uniform(0.1, 1, [
+            self.context_length * self.input_size[1], self.output_represention
+        ]).astype('float32')
+
+        begin_pad = np.max([0, -self.context_start])
+        end_pad = np.max([0, self.context_start + self.context_length - 1])
+        total_pad = begin_pad + end_pad
+        padding_data = np.random.uniform(
+            0.1, 1, [total_pad, self.input_size[1]]).astype('float32')
+        self.pad_data = padding_data
+        self.inputs = {
+            'X': (x, self.lod),
+            'Filter': w,
+        }
+        self.inputs_val = ['X', 'Filter']
+        self.inputs_val_no_x = ['Filter']
+        self.inputs_val_no_f = ['X']
+
+        if total_pad != 0:
+            self.inputs['PaddingData'] = padding_data
+            self.inputs_val = ['X', 'PaddingData', 'Filter']
+            self.inputs_val_no_x = ['PaddingData', 'Filter']
+            self.inputs_val_no_f = ['PaddingData', 'X']
+
+        self.attrs = {
+            'context_start': self.context_start,
+            'context_length': self.context_length,
+            'padding_trainable': self.padding_trainable,
+            'context_stride': self.context_stride
+        }
+        out = np.zeros(
+            (self.input_size[0], self.output_represention)).astype('float32')
+        self.outputs = {'Out': out}
+        self.compute()
+
+    def compute(self):
+        x, lod = self.inputs['X']
+        filter = self.inputs['Filter']
+        pading_data = self.pad_data
+        out = np.zeros((self.input_size[0], self.context_length *
+                        self.input_size[1])).astype('float32')
+        lod = lod[0]
+        begin_pad = np.max([0, -self.context_start])
+
+        for i in range(len(lod) - 1):
+            for j in range(self.context_length):
+                in_begin = lod[i] + self.context_start + j
+                in_end = lod[i + 1] + self.context_start + j
+                out_begin = lod[i]
+                out_end = lod[i + 1]
+                if in_begin < lod[i]:
+                    pad_size = np.min([lod[i] - in_begin, lod[i + 1] - lod[i]])
+                    if self.padding_trainable:
+                        sub_w = pading_data[j:j + pad_size, :]
+                        out[lod[i]:lod[i] + pad_size, j * self.input_size[1]:(
+                            j + 1) * self.input_size[1]] = sub_w
+                    out_begin = lod[i] + pad_size
+                    in_begin = lod[i]
+
+                if in_end > lod[i + 1]:
+                    pad_size = np.min(
+                        [in_end - lod[i + 1], lod[i + 1] - lod[i]])
+                    if self.padding_trainable:
+                        sub_w = pading_data[begin_pad + self.context_start + j -
+                                            pad_size:begin_pad +
+                                            self.context_start + j, :]
+                        out[lod[i + 1] - pad_size:lod[i + 1], j * self.
+                            input_size[1]:(j + 1) * self.input_size[1]] = sub_w
+                    in_end = lod[i + 1]
+                    out_end = lod[i + 1] - pad_size
+                if in_end <= in_begin:
+                    continue
+
+                in_sub = x[in_begin:in_end, :]
+                out[out_begin:out_end, j * self.input_size[1]:(j + 1) *
+                    self.input_size[1]] += in_sub
+
+        np.dot(out, filter, out=self.outputs['Out'])
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        if self.padding_trainable:
+            self.check_grad(
+                set(self.inputs_val), 'Out', max_relative_error=0.05)
+
+    def test_check_grad_input(self):
+        self.check_grad(
+            ['X'],
+            'Out',
+            max_relative_error=0.05,
+            no_grad_set=set(self.inputs_val_no_x))
+
+    def test_check_grad_padding_data(self):
+        if self.padding_trainable:
+            self.check_grad(
+                ['PaddingData'],
+                'Out',
+                max_relative_error=0.05,
+                no_grad_set=set(['X', 'Filter']))
+
+    def test_check_grad_Filter(self):
+        self.check_grad(
+            ['Filter'],
+            'Out',
+            max_relative_error=0.05,
+            no_grad_set=set(self.inputs_val_no_f))
+
+    def test_check_grad_input_filter(self):
+        if self.padding_trainable:
+            self.check_grad(
+                ['X', 'Filter'],
+                'Out',
+                max_relative_error=0.05,
+                no_grad_set=set(['PaddingData']))
+
+    def test_check_grad_padding_input(self):
+        if self.padding_trainable:
+            self.check_grad(
+                self.inputs_val_no_f,
+                'Out',
+                max_relative_error=0.05,
+                no_grad_set=set(['Filter']))
+
+    def test_check_grad_padding_filter(self):
+        if self.padding_trainable:
+            self.check_grad(
+                self.inputs_val_no_x,
+                'Out',
+                max_relative_error=0.05,
+                no_grad_set=set(['X']))
+
+    def init_test_case(self):
+        self.input_row = 11
+        self.context_start = 0
+        self.context_length = 1
+        self.padding_trainable = False
+        self.context_stride = 1
+
+        self.input_size = [self.input_row, 23]
+        self.lod = [[0, 4, 5, 8, self.input_row]]
+        self.output_represention = 8  # output feature size
+
+
+class TestSeqProjectCase1(TestSeqProject):
+    def init_test_case(self):
+        self.input_row = 11
+        self.context_start = -1
+        self.context_length = 3
+        self.padding_trainable = True
+        self.context_stride = 1
+
+        self.input_size = [self.input_row, 23]
+        self.lod = [[0, 4, 5, 8, self.input_row]]
+        self.output_represention = 8  # output feature size
+
+
+class TestSeqProjectCase2(TestSeqProject):
+    def init_test_case(self):
+        self.input_row = 25
+        self.context_start = 2
+        self.context_length = 3
+        self.padding_trainable = True
+        self.context_stride = 1
+
+        self.input_size = [self.input_row, 23]
+        idx = range(self.input_size[0])
+        del idx[0]
+        self.lod = [[0] + np.sort(random.sample(idx, 8)).tolist() +
+                    [self.input_size[0]]]
+        self.output_represention = 8  # output feature size
+
+
+if __name__ == '__main__':
+    unittest.main()