Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into multithread-sparse-adam

python/paddle/fluid/executor.py

@@ -382,9 +382,11 @@ class Executor(object):
         """
         Close this executor.
 
-        You can no long use this executor after calling this method.
+        You can no longer use this executor after calling this method.
         For the distributed training, this method would free the resource on PServers related to
         the current Trainer.
+        TODO(typhoonzero): Define "no longer use" meaning? Can user create
+        a new Executor for the same program and run?
         TODO(panyx0718): Why ParallelExecutor doesn't have close?
 
         Example:
@@ -397,7 +399,7 @@ class Executor(object):
             self.executor.close()
             self._closed = True
 
-    def _run_parallel(self, scope, feed, fetch_list, fetch_var_name,
+    def _run_parallel(self, program, scope, feed, fetch_list, fetch_var_name,
                       return_numpy):
         if isinstance(feed, dict):
             feed_tensor_dict = dict()
@@ -413,7 +415,7 @@ class Executor(object):
             self.executor.feed_and_split_tensor_into_local_scopes(
                 feed_tensor_dict)
         elif isinstance(feed, list) or isinstance(feed, tuple):
-            if len(feed) != len(self._places):
+            if len(feed) != len(program._places):
                 raise ValueError(
                     "Feed a list of tensor, the list should be the same size as places"
                 )
@@ -428,7 +430,7 @@ class Executor(object):
                     tensor = each[feed_name]
                     if not isinstance(tensor, core.LoDTensor):
                         tmp = core.LoDTensor()
-                        tmp.set(tensor, self._places[i])
+                        tmp.set(tensor, program._places[i])
                         tensor = tmp
                     res_dict[feed_name] = tensor
                 res.append(res_dict)
@@ -462,7 +464,7 @@ class Executor(object):
 
         Args:
             program(Program|CompiledProgram): the program that need to run,
-                if not provided, then default_main_program will be used.
+                if not provided, then default_main_program (not compiled) will be used.
             feed(dict): feed variable map, e.g. {"image": ImageData, "label": LabelData}
             fetch_list(list): a list of variable or variable names that user want to get, run will return them according to this list.
             feed_var_name(str): the name for the input variable of feed Operator.
@@ -525,6 +527,7 @@ class Executor(object):
         self.executor = program._executor
         if program._is_data_parallel:
             return self._run_parallel(
+                program,
                 scope=scope,
                 feed=feed,
                 fetch_list=fetch_list,

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

@@ -22,6 +22,7 @@ import unittest
 import paddle
 import paddle.fluid.core as core
 import paddle.fluid as fluid
+from paddle.fluid import compiler
 
 
 def train(network, use_cuda, use_parallel_executor, batch_size=32, pass_num=2):
@@ -57,18 +58,18 @@ def train(network, use_cuda, use_parallel_executor, batch_size=32, pass_num=2):
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
 
+    train_cp = compiler.CompiledProgram(fluid.default_main_program())
     if use_parallel_executor:
-        train_exe = fluid.ParallelExecutor(
-            use_cuda=use_cuda, loss_name=cost.name)
+        train_cp = train_cp.with_data_parallel(loss_name=cost.name)
         fetch_list = [cost.name]
     else:
-        train_exe = exe
         fetch_list = [cost]
 
     for pass_id in six.moves.xrange(pass_num):
         batch_id = 0
         for data in reader():
-            train_exe.run(feed=data,
+            exe.run(train_cp,
+                    feed=data,
                     fetch_list=fetch_list if batch_id % 4 == 0 else [])
             batch_id += 1
             if batch_id > 16:

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

@@ -16,6 +16,7 @@ from __future__ import print_function
 
 import paddle.dataset.conll05 as conll05
 import paddle.fluid as fluid
+from paddle.fluid import compiler
 import paddle.fluid.core as core
 import unittest
 import paddle
@@ -157,10 +158,8 @@ class TestCRFModel(unittest.TestCase):
             exe = fluid.Executor(place)
             exe.run(startup)
 
-            pe = fluid.ParallelExecutor(
-                use_cuda=use_cuda,
-                loss_name=avg_cost.name,
-                build_strategy=build_strategy)
+            train_cp = compiler.CompiledProgram(main).with_data_parallel(
+                loss_name=avg_cost.name, build_strategy=build_strategy)
 
             feeder = fluid.DataFeeder(
                 feed_list=[
@@ -172,7 +171,8 @@ class TestCRFModel(unittest.TestCase):
             data = train_data()
             for i in range(10):
                 cur_batch = next(data)
-                print(pe.run(feed=feeder.feed(cur_batch),
+                print(exe.run(train_cp,
+                              feed=feeder.feed(cur_batch),
                               fetch_list=[avg_cost.name])[0])
 
     def _new_build_strategy(self, use_reduce=False):

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

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import paddle.fluid as fluid
+from paddle.fluid import compiler
 import unittest
 import logging
 import six
@@ -36,21 +37,18 @@ class TestBase(unittest.TestCase):
         with fluid.program_guard(main_prog, startup_prog):
             with fluid.scope_guard(scope):
                 loss = network_func()
-                fluid.Executor(
-                    fluid.CUDAPlace(0)
-                    if use_gpu else fluid.CPUPlace()).run(startup_prog)
+                exe = fluid.Executor(
+                    fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace())
+                exe.run(startup_prog)
 
         for _ in six.moves.xrange(iter):
             exe_strategy = fluid.ExecutionStrategy()
             exe_strategy._dry_run = True
             exe_strategy.use_experimental_executor = use_experimental_executor
-            pe = fluid.ParallelExecutor(
-                use_cuda=use_gpu,
-                loss_name=loss.name,
-                main_program=main_prog,
-                exec_strategy=exe_strategy)
+            train_cp = compiler.CompiledProgram(main_prog).with_data_parallel(
+                loss_name=loss.name, exec_strategy=exe_strategy)
             for _ in six.moves.xrange(iter_per_pe):
-                pe.run([])
+                exe.run(train_cp)
 
 
 class TestMNISTDryRun(TestBase):

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

@@ -16,6 +16,7 @@ from __future__ import print_function
 
 import math
 import paddle.fluid as fluid
+from paddle.fluid import compiler
 import paddle.fluid.core as core
 import unittest
 import numpy as np
@@ -58,12 +59,13 @@ class TestFetchAndFeed(unittest.TestCase):
         exe = fluid.Executor(place)
         exe.run(startup)
 
-        pe = fluid.ParallelExecutor(
-            use_cuda=use_cuda, loss_name=loss.name, main_program=main_program)
-        run_parallel_exe(main_program, pe, use_cuda, data, label, loss)
+        train_cp = compiler.CompiledProgram(main_program).with_data_parallel(
+            loss_name=loss.name)
 
-    def run_parallel_exe_with_fetch(self, main, pe, use_cuda, data, label,
-                                    loss):
+        run_parallel_exe(train_cp, exe, use_cuda, data, label, loss)
+
+    def run_parallel_exe_with_fetch(self, compiled_program, exe, use_cuda, data,
+                                    label, loss):
         def get_data(batch_size=8):
             np.random.seed(5)
             while True:
@@ -78,7 +80,7 @@ class TestFetchAndFeed(unittest.TestCase):
         # conv2d_1.b_0@GRAD. Those variables should not be pruned.
         # fluid.memory_optimize(main)
         fetch_list = []
-        all_vars = main.global_block().vars
+        all_vars = compiled_program._program.global_block().vars
 
         for k, v in all_vars.items():
             if ('tmp' not in k) and (
@@ -89,14 +91,18 @@ class TestFetchAndFeed(unittest.TestCase):
         for batch_id, img_label in enumerate(get_data()):
             img, l = img_label
             train_inputs = {data.name: img, label.name: l}
-            ret = pe.run(fetch_list, feed=train_inputs, return_numpy=True)
+            ret = exe.run(compiled_program,
+                          fetch_list=fetch_list,
+                          feed=train_inputs,
+                          return_numpy=True)
             for i in range(len(fetch_list)):
                 assert not math.isnan(np.sum(ret[i])) and \
                        not math.isinf(np.sum(ret[i]))
             if batch_id == 2:
                 break
 
-    def run_parallel_exe_with_feed(self, main, pe, use_cuda, data, label, loss):
+    def run_parallel_exe_with_feed(self, compiled_program, exe, use_cuda, data,
+                                   label, loss):
         def get_data(batch_size=8):
             np.random.seed(5)
             while True:
@@ -114,7 +120,9 @@ class TestFetchAndFeed(unittest.TestCase):
         reader = feeder.decorate_reader(get_data, multi_devices=True)
 
         for batch_id, data in enumerate(reader()):
-            loss_np = pe.run(feed=data, fetch_list=[loss.name])[0]
+            loss_np = exe.run(compiled_program,
+                              feed=data,
+                              fetch_list=[loss.name])[0]
             print(batch_id, loss_np)
             if batch_id == 2:
                 break

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

@@ -16,6 +16,7 @@ from __future__ import print_function
 
 import paddle.fluid as fluid
 import paddle.fluid.core as core
+from paddle.fluid import compiler
 import numpy as np
 import unittest
 import os
@@ -61,22 +62,21 @@ class TestPassBuilder(unittest.TestCase):
             exe.run(startup)
             feed_dict = {'image': image, 'label': label}
 
-            train_exe = fluid.ParallelExecutor(
-                use_cuda=use_cuda,
+            train_cp = compiler.CompiledProgram(main).with_data_parallel(
+                loss_name=loss.name, build_strategy=build_strategy)
+            test_cp = compiler.CompiledProgram(test_program).with_data_parallel(
                 loss_name=loss.name,
-                main_program=main,
-                build_strategy=build_strategy)
-
-            test_exe = fluid.ParallelExecutor(
-                use_cuda=use_cuda,
-                main_program=test_program,
-                share_vars_from=train_exe,
-                build_strategy=build_strategy)
+                build_strategy=build_strategy,
+                share_vars_from=train_cp)
 
             for i in range(5):
-                test_loss, = test_exe.run([loss.name], feed=feed_dict)
-
-                train_loss, = train_exe.run([loss.name], feed=feed_dict)
+                _ = exe.run(train_cp, fetch_list=[loss.name], feed=feed_dict)
+                test_loss, = exe.run(test_cp,
+                                     fetch_list=[loss.name],
+                                     feed=feed_dict)
+                train_loss = exe.run(train_cp,
+                                     fetch_list=[loss.name],
+                                     feed=feed_dict)
 
                 avg_test_loss_val = np.array(test_loss).mean()
                 if math.isnan(float(avg_test_loss_val)):

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

@@ -14,6 +14,7 @@
 
 import os
 import paddle.fluid as fluid
+from paddle.fluid import compiler
 import paddle
 import unittest
 import six
@@ -140,9 +141,10 @@ def test_main(use_cuda, use_py_func_op, use_parallel_executor):
 
             exe = fluid.Executor(place)
             exe.run(fluid.default_startup_program())
+
+            train_cp = compiler.CompiledProgram(fluid.default_main_program())
             if use_parallel_executor:
-                exe = fluid.ParallelExecutor(
-                    use_cuda=use_cuda, loss_name=loss.name)
+                train_cp = train_cp.with_data_parallel(loss_name=loss.name)
                 fetch_list = [loss.name]
             else:
                 fetch_list = [loss]
@@ -150,9 +152,10 @@ def test_main(use_cuda, use_py_func_op, use_parallel_executor):
             ret = []
             for epoch_id in six.moves.range(2):
                 for d in r():
-                    L, = exe.run(feed=feeder.feed(d), fetch_list=fetch_list)
+                    L, = exe.run(train_cp,
+                                 feed=feeder.feed(d),
+                                 fetch_list=fetch_list)
                     ret.append(L)
-
             return np.array(ret)
 
 

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

@@ -16,6 +16,7 @@ from __future__ import print_function
 
 import unittest
 import paddle.fluid as fluid
+from paddle.fluid import compiler
 import paddle.fluid.core as core
 import numpy as np
 import threading
@@ -188,18 +189,18 @@ class TestPyReaderUsingExecutor(unittest.TestCase):
 
             place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
 
-            startup_exe = fluid.Executor(place)
-            startup_exe.run(startup_program)
+            exe = fluid.Executor(place)
+            exe.run(startup_program)
 
+            train_cp = compiler.CompiledProgram(main_program)
             if use_parallel_executor:
-                main_exe = fluid.ParallelExecutor(use_cuda, loss_name=loss.name)
+                train_cp = train_cp.with_data_parallel(loss_name=loss.name)
                 if use_cuda:
                     self.batch_size_times = core.get_cuda_device_count()
                 else:
                     self.batch_size_times = int(
                         os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
             else:
-                main_exe = startup_exe
                 self.batch_size_times = 1
 
             reader = self.tensor_reader(use_decorate_paddle_reader)
@@ -214,7 +215,8 @@ class TestPyReaderUsingExecutor(unittest.TestCase):
 
             self.outputs = []
             for _ in range(self.iterations):
-                fetches = main_exe.run(fetch_list=[in_data.name, label.name])
+                fetches = exe.run(train_cp,
+                                  fetch_list=[in_data.name, label.name])
                 fetches = [as_numpy(fetch) for fetch in fetches]
                 self.outputs.append(fetches)
 

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

@@ -15,6 +15,7 @@
 from __future__ import print_function
 import os
 import paddle.fluid as fluid
+from paddle.fluid import compiler
 import paddle
 import numpy as np
 import unittest
@@ -74,19 +75,12 @@ class TestReaderReset(unittest.TestCase):
         exe = fluid.Executor(place)
         exe.run(startup_prog)
 
-        build_strategy = fluid.BuildStrategy()
-        exec_strategy = fluid.ExecutionStrategy()
-        parallel_exe = fluid.ParallelExecutor(
-            use_cuda=self.use_cuda,
-            main_program=main_prog,
-            build_strategy=build_strategy,
-            exec_strategy=exec_strategy)
-
-        data_appeared = [False] * self.total_ins_num
+        train_cp = compiler.CompiledProgram(main_prog).with_data_parallel()
         pass_count = 0
         while (True):
             try:
-                data_val, label_val = parallel_exe.run(fetch_list,
+                data_val, label_val = exe.run(train_cp,
+                                              fetch_list=fetch_list,
                                               return_numpy=True)
                 ins_num = data_val.shape[0]
                 broadcasted_label = np.ones((ins_num, ) + tuple(

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

@@ -22,6 +22,7 @@ import paddle
 import paddle.fluid.core as core
 
 import paddle.fluid as fluid
+from paddle.fluid import compiler
 
 
 def get_places():
@@ -111,17 +112,17 @@ class TestWeightDecay(unittest.TestCase):
                 if use_reduce else fluid.BuildStrategy.ReduceStrategy.AllReduce
         build_strategy.memory_optimize = use_ir_memory_optimize
 
-        parallel_exe = fluid.ParallelExecutor(
-            use_cuda,
+        train_cp = compiler.CompiledProgram(fluid.default_main_program(
+        )).with_data_parallel(
             loss_name=loss.name,
             exec_strategy=exec_strategy,
             build_strategy=build_strategy)
 
         loss_set = []
         for data in self.train_data:
-            out = parallel_exe.run(feed=feeder.feed(data),
+            out = exe.run(train_cp,
+                          feed=feeder.feed(data),
                           fetch_list=[loss.name])
-            print("loss              %s" % (np.average(out)))
             loss_set.append(np.average(out))
 
         return loss_set