add sparse sgd operator unittest

paddle/operators/sgd_op.h

@@ -37,7 +37,8 @@ class SGDOpKernel : public framework::OpKernel<T> {
     auto* learning_rate = ctx.Input<framework::Tensor>("LearningRate");
 
     auto* grad_var = ctx.InputVar("Grad");
-    if (grad_var->IsType<framework::Tensor>()) {
+    // Actually, all tensors are LoDTensor except SelectedRows.
+    if (grad_var->IsType<framework::LoDTensor>()) {
       param_out->mutable_data<T>(ctx.GetPlace());
       auto* grad = ctx.Input<framework::Tensor>("Grad");
 

paddle/pybind/pybind.cc

@@ -186,6 +186,11 @@ All parameter, weight, gradient are variables in Paddle.
              return self.GetMutable<LoDTensor>();
            },
            py::return_value_policy::reference)
+      .def("get_selected_rows",
+           [](Variable &self) -> SelectedRows * {
+             return self.GetMutable<SelectedRows>();
+           },
+           py::return_value_policy::reference)
       .def("get_net",
            [](Variable &self) -> operators::NetOp * {
              return self.GetMutable<operators::NetOp>();

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

@@ -8,29 +8,30 @@ class TestSelectedRows(unittest.TestCase):
         place = core.CPUPlace()
         height = 10
         rows = [0, 4, 7]
-        row_numel = 10
+        row_numel = 12
-        selcted_rows = core.SelectedRows(rows, row_numel)
+        selected_rows = core.SelectedRows(rows, height)
-        np_array = np.ones((len(rows), height)).astype("float32")
+        np_array = np.ones((len(rows), row_numel)).astype("float32")
         np_array[0, 0] = 2.0
         np_array[2, 8] = 4.0
-        tensor = selcted_rows.get_tensor()
+        tensor = selected_rows.get_tensor()
         tensor.set(np_array, place)
 
         # compare rows
-        self.assertEqual(0, selcted_rows.rows()[0])
+        self.assertEqual(0, selected_rows.rows()[0])
-        self.assertEqual(4, selcted_rows.rows()[1])
+        self.assertEqual(4, selected_rows.rows()[1])
-        self.assertEqual(7, selcted_rows.rows()[2])
+        self.assertEqual(7, selected_rows.rows()[2])
 
         # compare height
-        self.assertEqual(10, selcted_rows.height())
+        self.assertEqual(10, selected_rows.height())
 
         # compare tensor
         self.assertAlmostEqual(2.0,
-                               selcted_rows.get_tensor().get_float_element(0))
+                               selected_rows.get_tensor().get_float_element(0))
         self.assertAlmostEqual(1.0,
-                               selcted_rows.get_tensor().get_float_element(1))
+                               selected_rows.get_tensor().get_float_element(1))
         self.assertAlmostEqual(
-            4.0, selcted_rows.get_tensor().get_float_element(2 * row_numel + 8))
+            4.0,
+            selected_rows.get_tensor().get_float_element(2 * row_numel + 8))
 
 
 if __name__ == "__main__":

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

@@ -1,5 +1,7 @@
 import unittest
 import numpy as np
+import paddle.v2.framework.core as core
+from paddle.v2.framework.op import Operator
 from op_test import OpTest
 
 
@@ -17,5 +19,63 @@ class TestSGDOp(OpTest):
         self.check_output()
 
 
+class TestSparseSGDOp(unittest.TestCase):
+    def test_sparse_sgd(self):
+        scope = core.Scope()
+
+        # create and initialize Grad Variable   
+        place = core.CPUPlace()
+        height = 10
+        rows = [0, 4, 7]
+        row_numel = 12
+
+        grad_selected_rows = scope.var('Grad').get_selected_rows()
+        grad_selected_rows.set_height(height)
+        grad_selected_rows.set_rows(rows)
+        np_array = np.ones((len(rows), row_numel)).astype("float32")
+        np_array[0, 0] = 2.0
+        np_array[2, 8] = 4.0
+        grad_tensor = grad_selected_rows.get_tensor()
+        grad_tensor.set(np_array, place)
+
+        # create and initialize Param Variable
+        param = scope.var('Param').get_tensor()
+        param_array = np.full((height, row_numel), 5.0).astype("float32")
+        param.set(param_array, place)
+
+        # create and initialize LeraningRate Variable
+        lr = scope.var('LearningRate').get_tensor()
+        lr_array = np.full((1), 2.0).astype("float32")
+        lr.set(lr_array, place)
+
+        # create and run sgd operator
+        sgd_op = Operator(
+            "sgd",
+            Param='Param',
+            Grad='Grad',
+            ParamOut='Param',
+            LearningRate='LearningRate')
+        ctx = core.DeviceContext.create(place)
+        sgd_op.run(scope, ctx)
+
+        # get and compare result
+        result_array = np.array(param)
+
+        # rows[0] = 0, 5.0 - 2.0 * 2.0
+        self.assertAlmostEqual(1.0, result_array[rows[0], 0])
+        # rows[0] = 0, 5.0 - 2.0 * 1.0
+        self.assertAlmostEqual(3.0, result_array[rows[0], 2])
+        # 5.0 - 2.0 * 0.0
+        self.assertAlmostEqual(5.0, result_array[1, 0])
+        # rows[1] = 4, 5.0 - 2.0 * 1.0
+        self.assertAlmostEqual(3.0, result_array[rows[1], 10])
+        # 5.0 - 2.0 * 0.0
+        self.assertAlmostEqual(5.0, result_array[5, 8])
+        # rows[2] = 7, 5.0 - 2.0 * 1.0
+        self.assertAlmostEqual(3.0, result_array[rows[2], 1])
+        # rows[2] = 7, 5.0 - 2.0 * 4.0
+        self.assertAlmostEqual(-3.0, result_array[rows[2], 8])
+
+
 if __name__ == "__main__":
     unittest.main()