"polish code" (#9318)

* "polish code"

* "fix ci"

* "fix ci"

* "done"

python/paddle/fluid/executor.py

@@ -48,8 +48,7 @@ def as_numpy(tensor):
     assert isinstance(tensor, core.LoDTensor)
     lod = tensor.lod()
     if len(lod) > 0:
-        raise RuntimeError(
-            "Some of your featched tensors hold LoD information. \
+        raise RuntimeError("Some of your fetched tensors hold LoD information. \
             They can not be completely cast to Python ndarray. \
             Please set the parameter 'return_numpy' as 'False' to \
             return LoDTensor itself directly.")
@@ -180,60 +179,24 @@ def get_program_cache_key(feed, fetch_list):
 
 
 class Executor(object):
-    def __init__(self, places):
-        if not isinstance(places, list) and not isinstance(places, tuple):
-            places = [places]
-
-        act_places = []
-        for each in places:
-            p = core.Place()
-            p.set_place(each)
-            act_places.append(p)
-
-        # TODO(dzhwinter) : only use the first place
-        self.executor = core.Executor(act_places[0])
-        self.places = places
+    def __init__(self, place):
+        self.place = place
+        p = core.Place()
+        p.set_place(place)
+        self.executor = core.Executor(p)
         self.program_caches = dict()
 
-    def aslodtensor(self, data):
-        def accumulate(data):
-            if not isinstance(data, list):
-                return 1
-            return sum([accumulate(sub) for sub in data])
-
-        def parselod(data):
-            seq_lens = [accumulate(seq) for seq in data]
-            cur_len = 0
-            lod = [cur_len]
-            for l in seq_lens:
-                cur_len += l
-                lod.append(cur_len)
-            return lod
-
-        assert len(self.places) != 0
-        if not isinstance(data, list):
-            # pure tensor case
-            tensor = core.LoDTensor()
-            tensor.set(data, self.places[0])
-            return tensor
-        else:
-            raise RuntimeError("Current implementation lacks unittests")
-            # lodtensor case
-            lod = []
-            if not isinstance(data[0], list):
-                lod.append(parselod(data))
-                flattened_data = np.concatenate(data, axis=0).astype("int64")
-            else:
-                while isinstance(data[0], list):
-                    lod.append(parselod(seq))
-                    flattened_data = [item for seq in data for item in seq]
-                    data = flattened_data
-                flattened_data = np.concatenate(data, axis=0).astype("int64")
-            flattened_data = flattened_data.reshape([len(flattened_data), 1])
-            tensor = core.LoDTensor()
-            tensor.set(flattened_data, self.places[0])
-            tensor.set_lod(lod)
-            return tensor
+    def as_lodtensor(self, data):
+        if isinstance(data, list):
+            raise RuntimeError("Some of your feed data hold LoD information. \
+                They can not be completely cast from a list of Python \
+                ndarray to LoDTensor. Please convert data to LoDTensor \
+                directly before feeding the data.\
+                ")
+        # single tensor case
+        tensor = core.LoDTensor()
+        tensor.set(data, self.place)
+        return tensor
 
     def _get_program_cache(self, program_cache_key):
         return self.program_caches.get(program_cache_key, None)
@@ -293,7 +256,7 @@ class Executor(object):
                 feed_target_name = op.desc.output('Out')[0]
                 cur_feed = feed[feed_target_name]
                 if not isinstance(cur_feed, core.LoDTensor):
-                    cur_feed = self.aslodtensor(cur_feed)
+                    cur_feed = self.as_lodtensor(cur_feed)
                 idx = op.desc.attr('col')
                 core.set_feed_variable(scope, cur_feed, feed_var_name, idx)
             else: