update test_adam_op

test=develop

paddle/fluid/operators/optimizers/adam_op.h

@@ -358,6 +358,7 @@ class AdamOpKernel : public framework::OpKernel<T> {
           lr.template data<T>(), grad_data, param.template data<T>(),
           param_out.template mutable_data<T>(ctx.GetPlace()), rows, row_numel,
           grad_merge.rows().size(), lazy_mode);
+      VLOG(3) << "lazy_mode :" << lazy_mode;
       if (lazy_mode) {
         std::vector<int64_t> id_vector;
         size_t row_count = grad_merge.rows().size();

python/paddle/fluid/tests/unittests/test_adam_op.py

@@ -219,14 +219,25 @@ def adam_step_sparse(inputs, attributes, height, rows, row_numel, np_grad,
     moment2_out = np.zeros(shape=[height, row_numel])
     param_out = np.zeros(shape=[height, row_numel])
 
-    for idx, row_id in enumerate(rows):
+    def update_row(row_id, update_value):
         moment1_out[row_id] = beta1 * moment1[row_id] + (1 - beta1
-                                                         ) * np_grad[idx]
+                                                         ) * update_value
         moment2_out[row_id] = beta2 * moment2[row_id] + (
-            1 - beta2) * np.square(np_grad[idx])
+            1 - beta2) * np.square(update_value)
         lr_t = lr * np.sqrt(1 - beta2_pow) / (1 - beta1_pow)
         param_out[row_id] = param[row_id] - lr_t * (moment1_out[row_id] / (
             np.sqrt(moment2_out[row_id]) + epsilon))
+
+    if lazy_mode:
+        for idx, row_id in enumerate(rows):
+            update_row(row_id, np_grad[idx])
+    else:
+        for row_id in range(param_out.shape[0]):
+            update_value = np.zeros(np_grad[0].shape).astype("float32")
+            if row_id in rows:
+                update_value = np_grad[rows.index(row_id)]
+            update_row(row_id, update_value)
+
     return param_out, moment1_out, moment2_out
 
 
@@ -249,6 +260,7 @@ class TestSparseAdamOp(unittest.TestCase):
             'Beta2Pow': np.array([beta2**10]).astype("float32"),
             "LearningRate": np.full((1), 2.0).astype("float32")
         }
+        self.init_output = np.full((height, row_numel), 0.0).astype("float32")
         self.attrs = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}
 
         grad_selected_rows = scope.var('Grad').get_selected_rows()
@@ -286,7 +298,7 @@ class TestSparseAdamOp(unittest.TestCase):
             op_args[s] = s
         for s in self.outputs:
             var = scope.var(s).get_tensor()
-            var.set(self.outputs[s], place)
+            var.set(self.init_output, place)
             op_args[s] = s
         for k in self.attrs:
             op_args[k] = self.attrs[k]
@@ -300,13 +312,9 @@ class TestSparseAdamOp(unittest.TestCase):
             actual = np.array(out_var)
             actual = actual.reshape([actual.size])
             np_array = np_array.reshape([np_array.size])
-            for idx, row_id in enumerate(self.rows):
-                j = 0
-                while j < self.row_numel:
-                    pos = row_id * self.row_numel + j
-                    self.assertLess((actual[pos] - np_array[pos]) / actual[pos],
-                                    0.00001)
-                    j += 1
+
+            for i in range(np_array.size):
+                self.assertLess((actual[i] - np_array[i]), 0.00001)
 
     def test_sparse_adam(self):
         places = [core.CPUPlace()]