Port transformer

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

@@ -36,6 +36,7 @@ import paddle.fluid as fluid
 import paddle.fluid.layers as layers
 from paddle.fluid import core
 from test_dist_base import TestDistRunnerBase, runtime_main
+import paddle.compat as cpt
 from paddle.compat import long_type
 
 import hashlib
@@ -315,7 +316,8 @@ def pad_batch_data(insts,
     """
     return_list = []
     max_len = max(len(inst) for inst in insts)
-    num_token = reduce(lambda x, y: x + y,
+    num_token = six.moves.reduce(
+        lambda x, y: x + y,
         [len(inst) for inst in insts]) if return_num_token else 0
     # Any token included in dict can be used to pad, since the paddings' loss
     # will be masked out by weights and make no effect on parameter gradients.
@@ -328,7 +330,7 @@ def pad_batch_data(insts,
         return_list += [inst_weight.astype("float32").reshape([-1, 1])]
     else:  # position data
         inst_pos = np.array([
-            range(1, len(inst) + 1) + [0] * (max_len - len(inst))
+            list(range(1, len(inst) + 1)) + [0] * (max_len - len(inst))
             for inst in insts
         ])
         return_list += [inst_pos.astype("int64").reshape([-1, 1])]
@@ -385,10 +387,11 @@ def prepare_batch_input(insts, data_input_names, src_pad_idx, trg_pad_idx,
         return_num_token=True)
 
     data_input_dict = dict(
+        list(
             zip(data_input_names, [
                 src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos,
                 trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight
-        ]))
+            ])))
     return data_input_dict, np.asarray([num_token], dtype="float32")
 
 
@@ -561,7 +564,7 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
                         np.log(TrainTaskConfig.label_smooth_eps / (
                             ModelHyperParams.trg_vocab_size - 1) + 1e-20))
     init = False
-    for pass_id in xrange(TrainTaskConfig.pass_num):
+    for pass_id in six.moves.xrange(TrainTaskConfig.pass_num):
         pass_start_time = time.time()
         for batch_id, data in enumerate(train_data()):
             if batch_id >= 5:
@@ -587,11 +590,11 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
                     ModelHyperParams.eos_idx, ModelHyperParams.n_head,
                     ModelHyperParams.d_model)
                 total_num_token += num_token
-                feed_kv_pairs = data_input_dict.items()
+                feed_kv_pairs = list(data_input_dict.items())
                 if TrainTaskConfig.local:
-                    feed_kv_pairs += {
+                    feed_kv_pairs += list({
                         lr_scheduler.learning_rate.name: lr_rate
-                    }.items()
+                    }.items())
                 feed_list.append(dict(feed_kv_pairs))
 
                 if not init:
@@ -873,6 +876,7 @@ class DataReader(object):
 
             f = tarfile.open(fpaths[0], "r")
             for line in f.extractfile(tar_fname):
+                line = cpt.to_text(line)
                 fields = line.strip("\n").split(self._field_delimiter)
                 if (not self._only_src and len(fields) == 2) or (
                         self._only_src and len(fields) == 1):
@@ -882,8 +886,9 @@ class DataReader(object):
                 if not os.path.isfile(fpath):
                     raise IOError("Invalid file: %s" % fpath)
 
-                with open(fpath, "r") as f:
+                with open(fpath, "rb") as f:
                     for line in f:
+                        line = cpt.to_text(line)
                         fields = line.strip("\n").split(self._field_delimiter)
                         if (not self._only_src and len(fields) == 2) or (
                                 self._only_src and len(fields) == 1):
@@ -892,8 +897,9 @@ class DataReader(object):
     @staticmethod
     def load_dict(dict_path, reverse=False):
         word_dict = {}
-        with open(dict_path, "r") as fdict:
+        with open(dict_path, "rb") as fdict:
             for idx, line in enumerate(fdict):
+                line = cpt.to_text(line)
                 if reverse:
                     word_dict[idx] = line.strip("\n")
                 else:
@@ -1034,7 +1040,7 @@ def multi_head_attention(queries,
         # size of the input as the output dimension size.
         return layers.reshape(
             x=trans_x,
-            shape=map(int, [0, 0, trans_x.shape[2] * trans_x.shape[3]]))
+            shape=list(map(int, [0, 0, trans_x.shape[2] * trans_x.shape[3]])))
 
     def scaled_dot_product_attention(q, k, v, attn_bias, d_model, dropout_rate):
         """