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paddle/operators/CMakeLists.txt

@@ -128,7 +128,7 @@ 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 sequence_project)
+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

@@ -9,7 +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(sequence_project SRCS sequence_project.cc sequence_project.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()
@@ -19,7 +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(sequence_project SRCS sequence_project.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

@@ -12,14 +12,14 @@ 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/sequence_project.h"
+#include "paddle/operators/math/context_project.h"
 
 namespace paddle {
 namespace operators {
 namespace math {
 
-template class SequenceProjectFunctor<platform::CPUPlace, float>;
-template class SequenceProjectFunctor<platform::CPUPlace, double>;
+template class ContextProjectFunctor<platform::CPUPlace, float>;
+template class ContextProjectFunctor<platform::CPUPlace, double>;
 
 }  // namespace math
 }  // namespace operators

paddle/operators/math/context_project.cu

@@ -14,14 +14,14 @@ limitations under the License. */
 
 #define EIGEN_USE_GPU
 
-#include "paddle/operators/math/sequence_project.h"
+#include "paddle/operators/math/context_project.h"
 
 namespace paddle {
 namespace operators {
 namespace math {
 
-template class SequenceProjectFunctor<platform::GPUPlace, float>;
-template class SequenceProjectFunctor<platform::GPUPlace, double>;
+template class ContextProjectFunctor<platform::GPUPlace, float>;
+template class ContextProjectFunctor<platform::GPUPlace, double>;
 
 }  // namespace math
 }  // namespace operators

paddle/operators/math/context_project.h

@@ -23,31 +23,29 @@ namespace paddle {
 namespace operators {
 namespace math {
 
-//    template <typename T, int MajorType = Eigen::RowMajor,
-//            typename IndexType = Eigen::DenseIndex>
-//    using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
-
 template <typename T, int MajorType = Eigen::RowMajor,
           typename IndexType = Eigen::DenseIndex>
 using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
 /*
- * \brief SequenceProject projects features of context_length time-steps of each
- * instance.
- *
+ * \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 inShape       The shape of Input data,
+ * \param Shape         The shape of Input data,
  *                      [minibatch, number_of_input_features].
- * \param inShape       A float LoDTensor.
+ * \param type          A float LoDTensor.
  *
  * \param padding_data  Padding data.
- * \param inShape       The shape of Padding data,
+ * \param Shape         The shape of Padding data,
  *                      [up_pad + down_pad, number_of_input_features].
- * \param inShape       A float LoDTensor.
+ * \param type          A float Tensor.
  *
  * \param col           Col data.
- * \param inShape       The shape of Col data,
- *                      [minibatch, 1].
- * \param inShape       A float LoDTensor.
+ * \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:
@@ -87,7 +85,7 @@ using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
  */
 
 template <typename Place, typename T>
-class SequenceProjectFunctor {
+class ContextProjectFunctor {
  public:
   void operator()(const platform::DeviceContext& context,
                   framework::LoDTensor& in, framework::Tensor& padding_data,
@@ -147,8 +145,7 @@ class SequenceProjectFunctor {
                        /*stride_height*/ context_stride, /*stride_width*/ 1,
                        up_pad, down_pad, 0, 0);
           }
-          out_t.Resize(framework::make_ddim(
-              {sequence_height, context_length * sequence_width}));
+          out_t.Resize({sequence_height, context_length * sequence_width});
         }
       }
     }
@@ -162,8 +159,7 @@ class SequenceProjectFunctor {
           sequence_height = static_cast<int>(out_t.dims()[0]);
 
           // add up trainable data
-          out_t.Resize(framework::make_ddim(
-              {sequence_height * context_length, sequence_width}));
+          out_t.Resize({sequence_height * context_length, sequence_width});
 
           if (up_pad > 0) {  // add up pad
             int padding_rows = std::min(
@@ -223,8 +219,7 @@ class SequenceProjectFunctor {
               }
             }
           }
-          out_t.Resize(framework::make_ddim(
-              {sequence_height, context_length * sequence_width}));
+          out_t.Resize({sequence_height, context_length * sequence_width});
         }
       }
     }

paddle/operators/sequence_conv_op.cc

@@ -38,10 +38,9 @@ class SequenceConvOp : public framework::OperatorWithKernel {
     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 && filter_dims[1] == in_dims[1],
-        "Filter's shape should be (context_length x "
-        "number_of_input_features).");
+    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(
@@ -66,8 +65,9 @@ class SequenceConvOp : public framework::OperatorWithKernel {
           "and 'context_length'.");
     }
 
-    in_dims[1] = 1;
+    in_dims[1] = filter_dims[1];
     ctx->SetOutputDim("Out", in_dims);
+    ctx->ShareLoD("X", "Out");
   }
 };
 
@@ -101,35 +101,51 @@ class SequenceConvOpMaker : public framework::OpProtoAndCheckerMaker {
   SequenceConvOpMaker(framework::OpProto* proto,
                       framework::OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X",
-             "(A float LoDTensor) the input of SequenceConvOp, a vector of "
-             "2-D matrix of size (minibatch, number_of_input_features).");
+    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) the input of SequenceConvOp, a vector of "
-             "2-D matrix of size (up_pad + down_pad, "
-             "number_of_input_features). ")
+             "(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 of SequenceConvOp, a vector of "
-             "2-D matrix of size (context_length x number_of_input_features).");
-    AddOutput("Out",
-              "(A float LoDTensor) the output of SequenceConvOp, a vector "
-              "of 2-D matrix of size (minibatch, 1).");
+             "(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.")
+                 "(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.")
+                 "(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. "
-                 "Currently, sequence_project_op only support "
+                 "(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);
@@ -139,14 +155,10 @@ class SequenceConvOpMaker : public framework::OpProtoAndCheckerMaker {
     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.
-
-Example:
-  Input:
-       X shape: (minibatch, number_of_input_features)
-       Filter shape: (context_length, number_of_input_features)
-  Output:
-       Out shape: (minibatch, 1)
+    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");
   }
 };

paddle/operators/sequence_conv_op.h

@@ -15,20 +15,14 @@ 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"
-#include "paddle/operators/math/sequence_project.h"
 
 namespace paddle {
 namespace operators {
 
 using Tensor = framework::Tensor;
 using LoDTensor = framework::LoDTensor;
-// template <typename T, int MajorType = Eigen::RowMajor,
-//          typename IndexType = Eigen::DenseIndex>
-// using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
-template <typename T, int MajorType = Eigen::RowMajor,
-          typename IndexType = Eigen::DenseIndex>
-using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
 
 template <typename Place, typename T>
 class SequenceConvKernel : public framework::OpKernel<T> {
@@ -39,7 +33,7 @@ class SequenceConvKernel : public framework::OpKernel<T> {
     auto filter = *context.Input<Tensor>("Filter");
 
     out->mutable_data<T>(context.GetPlace());
-    //  out->set_lod(in->lod());
+    context.ShareLoD("X", "Out");
 
     int context_start = context.Attr<int>("context_start");
     int context_length = context.Attr<int>("context_length");
@@ -60,17 +54,16 @@ class SequenceConvKernel : public framework::OpKernel<T> {
     int sequence_width;
     sequence_width = static_cast<int>(in->dims()[1]);
 
-    // use col_shape in the im2col calculation
+    // 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.
-    auto temp = framework::EigenVector<T>::Flatten(col);
-    temp.device(context.GetEigenDevice<Place>()) =
-        temp.constant(static_cast<T>(0));
+    set_zero(context.device_context(), &col, static_cast<T>(0));
 
-    paddle::operators::math::SequenceProjectFunctor<Place, T>
+    paddle::operators::math::ContextProjectFunctor<Place, T>
         seq_project_functor;
     LoDTensor* input = const_cast<LoDTensor*>(in);
     Tensor* pad_data = const_cast<Tensor*>(padding_data);
@@ -79,9 +72,8 @@ class SequenceConvKernel : public framework::OpKernel<T> {
                         padding_trainable, context_start, context_length,
                         context_stride, up_pad, down_pad, false, false, false);
 
-    filter.Resize(framework::make_ddim({context_length * sequence_width, 1}));
     math::matmul<Place, T>(context.device_context(), col, false, filter, false,
-                           T(1.0), out, T(0.0));
+                           static_cast<T>(1.0), out, static_cast<T>(0.0));
   }
 };
 
@@ -102,7 +94,6 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
     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.");
     auto lod_g_level_0 = in->lod()[0];
@@ -111,6 +102,7 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
     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};
@@ -119,22 +111,17 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
     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.
-      auto temp = framework::EigenVector<T>::Flatten(col);
-      temp.device(context.GetEigenDevice<Place>()) =
-          temp.constant(static_cast<T>(0));
-
+      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::SequenceProjectFunctor<Place, T>
+    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());
-
-      math::SetConstant<Place, T> functor;
-      functor(context.device_context(), in_g, 0);
+      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,
@@ -143,9 +130,7 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
 
     if (padding_trainable && padding_data_g) {
       padding_data_g->mutable_data<T>(context.GetPlace());
-
-      math::SetConstant<Place, T> functor;
-      functor(context.device_context(), padding_data_g, 0);
+      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,
@@ -155,12 +140,10 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
 
     if (filter_g) {
       filter_g->mutable_data<T>(context.GetPlace());
+      set_zero(context.device_context(), filter_g, static_cast<T>(0));
 
-      math::SetConstant<Place, T> functor;
-      functor(context.device_context(), filter_g, 0);
-
-      Tensor filter_grad_ = *filter_g;
-      LoDTensor out_grad_ = *out_g;
+      Tensor filter_grad = *filter_g;
+      LoDTensor out_grad = *out_g;
 
       const Tensor* padding_data = nullptr;
       if (padding_trainable) {
@@ -177,11 +160,8 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
                           context_stride, up_pad, down_pad, false, false,
                           false);
 
-      filter_grad_.Resize(
-          framework::make_ddim({context_length * sequence_width, 1}));
-
-      math::matmul<Place, T>(context.device_context(), col, true, out_grad_,
-                             false, T(1.0), &filter_grad_, T(1.0));
+      math::matmul<Place, T>(context.device_context(), col, true, out_grad,
+                             false, T(1.0), &filter_grad, T(1.0));
     }
   }
 };

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

@@ -20,8 +20,9 @@ class TestSeqProject(OpTest):
         # 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]]).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])
@@ -49,7 +50,8 @@ class TestSeqProject(OpTest):
             'padding_trainable': self.padding_trainable,
             'context_stride': self.context_stride
         }
-        out = np.zeros((self.input_size[0], 1)).astype('float32')
+        out = np.zeros(
+            (self.input_size[0], self.output_represention)).astype('float32')
         self.outputs = {'Out': out}
         self.compute()
 
@@ -95,13 +97,7 @@ class TestSeqProject(OpTest):
                 out[out_begin:out_end, j * self.input_size[1]:(j + 1) *
                     self.input_size[1]] += in_sub
 
-        filter_dim = filter.shape
-        output_dim = self.outputs['Out'].shape
-        filter.shape = filter_dim[0] * filter_dim[1]
-        self.outputs['Out'].shape = (output_dim[0], )
         np.dot(out, filter, out=self.outputs['Out'])
-        filter.shape = filter_dim
-        self.outputs['Out'].shape = output_dim
 
     def test_check_output(self):
         self.check_output()
@@ -166,6 +162,7 @@ class TestSeqProject(OpTest):
 
         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):
@@ -178,6 +175,7 @@ class TestSeqProjectCase1(TestSeqProject):
 
         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):
@@ -193,6 +191,7 @@ class TestSeqProjectCase2(TestSeqProject):
         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__':