Add parallel executor tests

python/paddle/fluid/parallel_executor.py

@@ -16,6 +16,7 @@
 import multiprocessing
 import framework
 import executor
+import warnings
 import sys
 
 __all__ = ['ParallelExecutor']
@@ -62,8 +63,8 @@ def __init__(self,
                   main_program=test_program,
                   share_vars_from=train_exe)
 
-              train_loss, = train_exe.run([loss.name], feed_dict=feed_dict)
-              test_loss, = test_exe.run([loss.name], feed_dict=feed_dict)
+              train_loss, = train_exe.run([loss.name], feed=feed_dict)
+              test_loss, = test_exe.run([loss.name], feed=feed_dict)
         """
 
         self._places = []
@@ -103,8 +104,8 @@ def __init__(self,
 
         self.persistable_vars = [
             v.name
-            for v in filter(lambda var: \
-                var.persistable and var.type != core.VarDesc.VarType.RAW,
+            for v in filter(
+                lambda var: var.persistable and var.type != core.VarDesc.VarType.RAW,
                 main.list_vars())
         ]
 
@@ -163,7 +164,7 @@ def run(self, fetch_list, feed=None, feed_dict=None):
         Returns: fetched result list.
 
         """
-        if feed is None:
+        if feed is None and feed_dict is not None:
             feed = feed_dict
             print >> sys.stderr, "`feed_dict` is deprecated. Please use `feed=`"
 

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

@@ -200,14 +200,29 @@ class TestParallelExecutorBase(unittest.TestCase):
     def check_network_convergence(self,
                                   method,
                                   memory_opt=True,
-                                  iter=10,
+                                  iter=50,
                                   batch_size=None,
                                   allow_op_delay=False,
-                                  feed_dict=None):
+                                  feed_dict=None,
+                                  seed=None,
+                                  use_parallel_executor=True):
+        def run_executor(exe, feed, fetch_list, program=None):
+            if isinstance(exe, fluid.ParallelExecutor):
+                res = exe.run(fetch_list=fetch_list, feed=feed)
+            elif isinstance(exe, fluid.Executor):
+                if program is None:
+                    program = fluid.default_main_program()
+                res = exe.run(program=program, feed=feed, fetch_list=fetch_list)
+            else:
+                raise ValueError('Unkown type exe')
+            return res
+
         main = fluid.Program()
         startup = fluid.Program()
         startup.random_seed = 1  # Fix random seed
         with fluid.program_guard(main, startup):
+            if seed is not None:
+                startup.random_seed = seed
             loss = method(use_feed=feed_dict is not None)
             adam = fluid.optimizer.Adam()
             adam.minimize(loss)
@@ -217,18 +232,24 @@ def check_network_convergence(self,
             startup_exe = fluid.Executor(place)
             startup_exe.run(startup)
 
-            exe = fluid.ParallelExecutor(
-                True, loss_name=loss.name, allow_op_delay=allow_op_delay)
+            if use_parallel_executor:
+                exe = fluid.ParallelExecutor(
+                    True, loss_name=loss.name, allow_op_delay=allow_op_delay)
+            else:
+                exe = fluid.Executor(place=place)
+
             if batch_size is not None:
                 batch_size *= fluid.core.get_cuda_device_count()
             begin = time.time()
-            first_loss, = exe.run([loss.name], feed=feed_dict)
+            first_loss, = run_executor(
+                exe=exe, feed=feed_dict, fetch_list=[loss.name])
             first_loss = numpy.array(first_loss)
 
             for i in xrange(iter):
-                exe.run([], feed=feed_dict)
+                run_executor(exe=exe, feed=feed_dict, fetch_list=[])
 
-            last_loss, = exe.run([loss.name], feed=feed_dict)
+            last_loss, = run_executor(
+                exe=exe, feed=feed_dict, fetch_list=[loss.name])
             end = time.time()
 
             if batch_size is not None:
@@ -239,6 +260,7 @@ def check_network_convergence(self,
 
             print first_loss, last_loss
             # self.assertGreater(first_loss[0], last_loss[0])
+            return first_loss, last_loss
 
 
 class TestMNIST(TestParallelExecutorBase):
@@ -268,6 +290,27 @@ def test_simple_fc(self):
             simple_fc_net, feed_dict={"image": img,
                                       "label": label})
 
+    def test_simple_fc_parallel_accuracy(self):
+        img = numpy.zeros(shape=[32, 784], dtype='float32')
+        label = numpy.ones(shape=[32, 1], dtype='int64')
+        single_first_loss, single_last_loss = self.check_network_convergence(
+            method=simple_fc_net,
+            seed=1000,
+            feed_dict={"image": img,
+                       "label": label},
+            use_parallel_executor=False)
+        parallel_first_loss, parallel_last_loss = self.check_network_convergence(
+            method=simple_fc_net,
+            seed=1000,
+            feed_dict={"image": img,
+                       "label": label},
+            use_parallel_executor=True)
+
+        for p_f in parallel_first_loss:
+            self.assertAlmostEquals(p_f, single_first_loss[0], delta=1e-6)
+        for p_l in parallel_last_loss:
+            self.assertAlmostEquals(p_l, single_last_loss[0], delta=1e-6)
+
     def test_batchnorm_fc(self):
         self.check_network_convergence(fc_with_batchnorm)
         img = numpy.zeros(shape=[32, 784], dtype='float32')
@@ -496,10 +539,10 @@ def test_parallel_testing(self):
                 share_vars_from=train_exe)
 
             for i in xrange(5):
-                test_loss, = test_exe.run([loss.name], feed_dict=feed_dict)
+                test_loss, = test_exe.run([loss.name], feed=feed_dict)
                 test_loss = numpy.array(test_loss)
 
-                train_loss, = train_exe.run([loss.name], feed_dict=feed_dict)
+                train_loss, = train_exe.run([loss.name], feed=feed_dict)
                 train_loss = numpy.array(train_loss)
                 self.assertTrue(
                     numpy.allclose(