hidden the dataset call of pipeline to train_from_dataset (#25834)

* hidden the explicit setting of dataset for pipeline training.

python/paddle/fluid/executor.py

@@ -1334,15 +1334,26 @@ class Executor(object):
                           fetch_info=None,
                           print_period=100,
                           fetch_handler=None):
+        if program._pipeline_opt is not None:
+            import paddle
+            if dataset is not None:
+                raise RuntimeError("dataset should be None for pipeline mode")
+            # The following fake dataset is created to call 
+            # the _prepare_trainer api, and it is meaningless.
+            data_vars = []
+            for var in program.global_block().vars.values():
+                if var.is_data:
+                    data_vars.append(var)
+            dataset = paddle.fluid.DatasetFactory().create_dataset(
+                'FileInstantDataset')
+            dataset.set_batch_size(1)
+            dataset.set_thread(1)
+            dataset.set_filelist(['None'])
+            dataset.set_use_var(data_vars)
+        else:
             if dataset is None:
                 raise RuntimeError("dataset is need and should be initialized")
 
-        if program._pipeline_opt is not None and program._pipeline_opt[
-                "sync_steps"] != -1:
-            # hack for paddlebox: sync_steps(-1) denotes paddlebox
-            thread = self._adjust_pipeline_resource(program._pipeline_opt,
-                                                    dataset, thread)
-
         dataset._prepare_to_run()
 
         scope, trainer = self._prepare_trainer(

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

@@ -186,18 +186,10 @@ class TestPipeline(unittest.TestCase):
         data_loader.set_sample_generator(train_reader, batch_size=1)
         place = fluid.CPUPlace()
 
-        # The following dataset is only used for the 
-        # interface 'train_from_dataset'.
-        # And it has no actual meaning.
-        dataset = fluid.DatasetFactory().create_dataset('FileInstantDataset')
-        dataset.set_batch_size(1)
-        dataset.set_thread(1)
-        dataset.set_filelist(['/tmp/tmp_2.txt'])
-        dataset.set_use_var([image, label])
         exe = fluid.Executor(place)
         exe.run(startup_prog)
         data_loader.start()
-        exe.train_from_dataset(main_prog, dataset, debug=debug)
+        exe.train_from_dataset(main_prog, debug=debug)
 
     def test_pipeline(self):
         self._run(False)