Add sequence_project_functor

paddle/operators/math/CMakeLists.txt

@@ -7,6 +7,7 @@ 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)
 else()
     cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context operator)
     cc_library(selected_rows_functor SRCS selected_rows_functor.cc DEPS selected_rows math_function)
@@ -14,6 +15,7 @@ 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)
+    nv_library(sequence_project SRCS sequence_project.cc DEPS device_context)
 endif()
 
 cc_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)

paddle/operators/math/sequence_project.cc

@@ -0,0 +1,26 @@
+/* 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/sequence_project.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+template class SequenceProjectFunctor<platform::CPUPlace, float>;
+template class SequenceProjectFunctor<platform::CPUPlace, double>;
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/operators/math/sequence_project.cu

@@ -0,0 +1,28 @@
+/* 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/sequence_project.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+template class SequenceProjectFunctor<platform::GPUPlace, float>;
+template class SequenceProjectFunctor<platform::GPUPlace, double>;
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/operators/math/sequence_project.h

@@ -0,0 +1,178 @@
+/* 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"
+#include "paddle/operators/math/math_function.h"
+
+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 Converts the feature data of four dimensions(CDHW) into a colData of
+ *        seven dimensions in the Vol2ColFunctor calculation,
+ *        And in the Col2VolFunctor calculation, it is reversed.
+ *
+ * \param volData   Vol data.
+ * \param volShape  The shape of volData,
+ *                 [input_channels, input_depth, input_height, input_width].
+ * \param colData  Column data.
+ * \param colShape The shape of colData.
+ *
+ * The shape of colData is:
+ * [input_channels, filter_depth, filter_height, filter_width, output_depth,
+ * output_height, output_width]
+ * So, it is easy to reshape into a convolution matrix for convolution
+ * calculation based on matrix multiplication.
+ * The shape of convolution matrix is [height, width], where the height is equal
+ * input_channels * filter_depth * filter_height * filter_width, and the width
+ * is equal output_depth * output_height * output_width.
+ *
+ * Reshape:
+ *     shape of colData           shape of convolution matrix
+ *     [input_channels,
+ *      filter_depth,
+ *      filter_height,
+ *      filter_width,      ======>      [height, width]
+ *      output_depth,
+ *      output_height,
+ *      output_width]
+ *
+ * \note The caller needs to ensure that volShape.inputChannels is equal to
+ *       colShape.inputChannels.
+ */
+
+template <typename Place, typename T>
+class SequenceProjectFunctor {
+ public:
+  void operator()(const platform::DeviceContext& context,
+                  const framework::LoDTensor*& in,
+                  const framework::LoDTensor* padding_data,
+                  framework::LoDTensor* col, bool padding_trainable,
+                  int context_start, int context_length, int context_stride,
+                  int up_pad, int down_pad) {
+    auto lod_level_0 = in->lod()[0];
+
+    paddle::operators::math::Im2ColFunctor<
+        paddle::operators::math::ColFormat::kOCF, Place, float>
+        im2col_ocf;
+
+    int input_row_begin, input_row_end;
+    int sequence_height, sequence_width;
+    sequence_width = in->dims()[1];
+
+    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]);
+
+      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));
+
+      if (input_row_begin < input_row_end) {
+        framework::Tensor in_t = in->Slice(input_row_begin, input_row_end);
+        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));
+
+        im2col_ocf(context, in_t, out_t,
+                   /*stride_height*/ context_stride, /*stride_width*/ 0, up_pad,
+                   down_pad);
+      }
+
+      if (padding_trainable) {
+        // add up trainable data
+        out_t.Resize(framework::make_ddim(
+            {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);
+            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);
+            out_t_sub_e.device(*context.GetEigenDevice<Place>()) = w_sub_e;
+          }
+        }
+      }
+      out_t.Resize(framework::make_ddim(
+          {sequence_height, context_length * sequence_width}));
+    }
+  }
+};
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle