Merge pull request #4841 from QiJune/pybind_selected_rows

export SelectedRows to Python

paddle/pybind/pybind.cc

@@ -18,6 +18,7 @@ limitations under the License. */
 #include "paddle/framework/executor.h"
 #include "paddle/framework/feed_fetch_method.h"
 #include "paddle/framework/lod_tensor.h"
+#include "paddle/framework/selected_rows.h"
 #include "paddle/framework/tensor_array.h"
 #include "paddle/operators/cond_op.h"
 #include "paddle/operators/dynamic_recurrent_op.h"
@@ -139,6 +140,32 @@ PYBIND11_PLUGIN(core) {
 #endif
       });
 
+  py::class_<SelectedRows>(m, "SelectedRows")
+      .def("__init__",
+           [](SelectedRows &instance) { new (&instance) SelectedRows(); })
+      .def("__init__",
+           [](SelectedRows &instance, const std::vector<int64_t> rows,
+              const int64_t &height) {
+             new (&instance) SelectedRows(rows, height);
+           })
+      .def("get_tensor",
+           [](SelectedRows &self) { return self.mutable_value(); },
+           py::return_value_policy::reference)
+      .def("set_height", &SelectedRows::set_height)
+      .def("height", &SelectedRows::height)
+      .def("set_rows", &SelectedRows::set_rows)
+      .def("rows", [](SelectedRows &self) {
+#ifndef PADDLE_WITH_CUDA
+        return self.rows();
+#else
+         auto rows = self.rows();
+         std::vector<int64_t> new_rows;
+         new_rows.reserve(rows.size());
+         std::copy(rows.begin(), rows.end(), std::back_inserter(new_rows));
+         return new_rows;
+#endif
+      });
+
   py::class_<Variable>(m, "Variable", R"DOC(Variable Class.
 
 All parameter, weight, gradient are variables in Paddle.

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

+import paddle.v2.framework.core as core
+import unittest
+import numpy as np
+
+
+class TestSelectedRows(unittest.TestCase):
+    def test_selected_rows(self):
+        place = core.CPUPlace()
+        height = 10
+        rows = [0, 4, 7]
+        row_numel = 10
+        selcted_rows = core.SelectedRows(rows, row_numel)
+        np_array = np.ones((len(rows), height)).astype("float32")
+        np_array[0, 0] = 2.0
+        np_array[2, 8] = 4.0
+        tensor = selcted_rows.get_tensor()
+        tensor.set(np_array, place)
+
+        # compare rows
+        self.assertEqual(0, selcted_rows.rows()[0])
+        self.assertEqual(4, selcted_rows.rows()[1])
+        self.assertEqual(7, selcted_rows.rows()[2])
+
+        # compare height
+        self.assertEqual(10, selcted_rows.height())
+
+        # compare tensor
+        self.assertAlmostEqual(2.0,
+                               selcted_rows.get_tensor().get_float_element(0))
+        self.assertAlmostEqual(1.0,
+                               selcted_rows.get_tensor().get_float_element(1))
+        self.assertAlmostEqual(
+            4.0, selcted_rows.get_tensor().get_float_element(2 * row_numel + 8))
+
+
+if __name__ == "__main__":
+    unittest.main()