bugfix: predict_hangs

+ sanity check in `in tokens` mode

finetune_args.py

@@ -78,7 +78,7 @@ data_g.add_arg("dev_set",             str,  None,  "Path to validation data.")
 data_g.add_arg("vocab_path",          str,  None,  "Vocabulary path.")
 data_g.add_arg("max_seq_len",         int,  512,   "Number of words of the longest seqence.")
 data_g.add_arg("batch_size",          int,  32,    "Total examples' number in batch for training. see also --in_tokens.")
-data_g.add_arg("predict_batch_size",  int,  8,    "Total examples' number in batch for predict. see also --in_tokens.")
+data_g.add_arg("predict_batch_size",  int,  None,    "Total examples' number in batch for predict. see also --in_tokens.")
 data_g.add_arg("in_tokens",           bool, False,
               "If set, the batch size will be the maximum number of tokens in one batch. "
               "Otherwise, it will be the maximum number of examples in one batch.")

run_classifier.py

@@ -92,6 +92,8 @@ def main(args):
         num_train_examples = reader.get_num_examples(args.train_set)
 
         if args.in_tokens:
+            if args.batch_size < args.max_seq_len:
+                raise ValueError('if in_tokens=True, batch_size should greater than max_sqelen, got batch_size:%d seqlen:%d' % (args.batch_size, args.max_seq_len))
             max_train_steps = args.epoch * num_train_examples // (
                 args.batch_size // args.max_seq_len) // dev_count
         else:
@@ -376,11 +378,12 @@ def main(args):
 def evaluate_wrapper(args, reader, exe, test_prog, test_pyreader, graph_vars,
                      epoch, steps):
     # evaluate dev set
+    batch_size = args.batch_size if args.predict_batch_size is None else args.predict_batch_size
     for ds in args.dev_set.split(','):
         test_pyreader.decorate_tensor_provider(
             reader.data_generator(
                 ds,
-                batch_size=args.predict_batch_size,
+                batch_size=batch_size,
                 epoch=1,
                 dev_count=1,
                 shuffle=False))
@@ -403,12 +406,13 @@ def predict_wrapper(args, reader, exe, test_prog, test_pyreader, graph_vars,
     test_sets = args.test_set.split(',')
     save_dirs = args.test_save.split(',')
     assert len(test_sets) == len(save_dirs)
+    batch_size = args.batch_size if args.predict_batch_size is None else args.predict_batch_size
 
     for test_f, save_f in zip(test_sets, save_dirs):
         test_pyreader.decorate_tensor_provider(
             reader.data_generator(
                 test_f,
-                batch_size=args.predict_batch_size,
+                batch_size=batch_size,
                 epoch=1,
                 dev_count=1,
                 shuffle=False))

run_mrc.py

@@ -95,6 +95,8 @@ def main(args):
         num_train_examples = reader.get_num_examples("train")
 
         if args.in_tokens:
+            if args.batch_size < args.max_seq_len:
+                raise ValueError('if in_tokens=True, batch_size should greater than max_sqelen, got batch_size:%d seqlen:%d' % (args.batch_size, args.max_seq_len))
             max_train_steps = args.epoch * num_train_examples // (
                 args.batch_size // args.max_seq_len) // dev_count
         else:

run_sequence_labeling.py

@@ -85,6 +85,9 @@ def main(args):
         num_train_examples = reader.get_num_examples(args.train_set)
 
         if args.in_tokens:
+            if args.batch_size < args.max_seq_len:
+                raise ValueError('if in_tokens=True, batch_size should greater than max_sqelen, got batch_size:%d seqlen:%d' % (args.batch_size, args.max_seq_len))
+
             max_train_steps = args.epoch * num_train_examples // (
                 args.batch_size // args.max_seq_len) // dev_count
         else:
@@ -297,11 +300,12 @@ def main(args):
 def evaluate_wrapper(reader, exe, test_prog, test_pyreader, graph_vars,
                      epoch, steps):
     # evaluate dev set
+    batch_size = args.batch_size if args.predict_batch_size is None else args.predict_batch_size
     for ds in args.dev_set.split(','): #single card eval
         test_pyreader.decorate_tensor_provider(
             reader.data_generator(
                 ds,
-                batch_size=args.predict_batch_size,
+                batch_size=batch_size,
                 epoch=1,
                 dev_count=1,
                 shuffle=False))
@@ -318,10 +322,11 @@ def predict_wrapper(reader, exe, test_prog, test_pyreader, graph_vars,
     save_dirs = args.test_save.split(',')
     assert len(test_sets) == len(save_dirs), 'number of test_sets & test_save not match, got %d vs %d' % (len(test_sets), len(save_dirs))
 
+    batch_size = args.batch_size if args.predict_batch_size is None else args.predict_batch_size
     for test_f, save_f in zip(test_sets, save_dirs):
         test_pyreader.decorate_tensor_provider(reader.data_generator(
                     test_f,
-                    batch_size=args.predict_batch_size,
+                    batch_size=batch_size,
                     epoch=1,
                     dev_count=1,
                     shuffle=False))