From a994327fb158ee7692238fef6e29c7f1ed6dc1ca Mon Sep 17 00:00:00 2001
From: qiaolongfei <qiaolongfei@baidu.com>
Date: Tue, 3 Apr 2018 15:39:46 +0800
Subject: [PATCH] add TestSGDOpOptimizeSelectedRows

---
 .../fluid/tests/unittests/test_sgd_op.py      | 63 ++++++++++---------
 1 file changed, 34 insertions(+), 29 deletions(-)

diff --git a/python/paddle/fluid/tests/unittests/test_sgd_op.py b/python/paddle/fluid/tests/unittests/test_sgd_op.py
index b3fd63611..191f21725 100644
--- a/python/paddle/fluid/tests/unittests/test_sgd_op.py
+++ b/python/paddle/fluid/tests/unittests/test_sgd_op.py
@@ -101,31 +101,50 @@ class TestSGDOpOptimizeSelectedRows(unittest.TestCase):
     def check_with_place(self, place):
         scope = core.Scope()
 
+        row_width = 12
         # create and initialize Grad Variable
-        height = 10
-        rows = [0, 4, 7]
-        row_numel = 12
+        grad_height = 10
+        grad_rows = [0, 4, 7]
 
         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_selected_rows.set_height(grad_height)
+        grad_selected_rows.set_rows(grad_rows)
+        grad_array = np.ones((len(grad_rows), row_width)).astype("float32")
+        grad_array[0, 0] = 2.0
+        grad_array[2, 8] = 4.0
 
         grad_tensor = grad_selected_rows.get_tensor()
-        grad_tensor.set(np_array, place)
+        grad_tensor.set(grad_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 W Variable
+        param_rows = [0, 1, 2, 3, 4, 5, 6, 7]
+
+        # init Param
+        w_selected_rows = scope.var('Param').get_selected_rows()
+        w_selected_rows.set_height(len(param_rows))
+        w_selected_rows.set_rows(param_rows)
+        w_array = np.ones((len(param_rows), row_width)).astype("float32")
+        for i in range(len(param_rows)):
+            w_array[i] *= i
+        w_tensor = w_selected_rows.get_tensor()
+        w_tensor.set(w_array, place)
+
+        w_before_optimize = np.array(w_tensor)
+        print(w_before_optimize)
 
         # create and initialize LeraningRate Variable
+        lr_value = 0.1
         lr = scope.var('LearningRate').get_tensor()
-        lr_array = np.full((1), 2.0).astype("float32")
+        lr_array = np.full((1), lr_value).astype("float32")
         lr.set(lr_array, place)
 
+        # optimize with Python
+        w_after_optimize = np.copy(w_before_optimize)
+        for index, id in enumerate(grad_rows):
+            w_after_optimize[id] = w_before_optimize[
+                id] - lr_value * grad_array[index]
+
         # create and run sgd operator
         sgd_op = Operator(
             "sgd",
@@ -136,22 +155,8 @@ class TestSGDOpOptimizeSelectedRows(unittest.TestCase):
         sgd_op.run(scope, place)
 
         # 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])
+        result_array = np.array(w_tensor)
+        assert (result_array == w_after_optimize).all()
 
     def test_sparse_sgd(self):
         places = [core.CPUPlace()]
-- 
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