discard task_id

demo/multi-label-classification/multi_label_classifier.py

@@ -45,8 +45,7 @@ if __name__ == '__main__':
         # Setup feed list for data feeder
         feed_list = [
             inputs["input_ids"].name, inputs["position_ids"].name,
-            inputs["segment_ids"].name, inputs["input_mask"].name,
-            inputs["task_ids"].name
+            inputs["segment_ids"].name, inputs["input_mask"].name
         ]
     else:
         module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
@@ -68,8 +67,7 @@ if __name__ == '__main__':
     reader = hub.reader.MultiLabelClassifyReader(
         dataset=dataset,
         vocab_path=module.get_vocab_path(),
-        max_seq_len=args.max_seq_len,
-        use_task_id=args.use_taskid)
+        max_seq_len=args.max_seq_len)
 
     # Construct transfer learning network
     # Use "pooled_output" for classification tasks on an entire sentence.

demo/multi-label-classification/predict.py

@@ -50,9 +50,10 @@ if __name__ == '__main__':
 
         # Setup feed list for data feeder
         feed_list = [
-            inputs["input_ids"].name, inputs["position_ids"].name,
-            inputs["segment_ids"].name, inputs["input_mask"].name,
-            inputs["task_ids"].name
+            inputs["input_ids"].name,
+            inputs["position_ids"].name,
+            inputs["segment_ids"].name,
+            inputs["input_mask"].name,
         ]
     else:
         module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
@@ -74,8 +75,7 @@ if __name__ == '__main__':
     reader = hub.reader.MultiLabelClassifyReader(
         dataset=dataset,
         vocab_path=module.get_vocab_path(),
-        max_seq_len=args.max_seq_len,
-        use_task_id=args.use_taskid)
+        max_seq_len=args.max_seq_len)
 
     # Construct transfer learning network
     # Use "pooled_output" for classification tasks on an entire sentence.

demo/regression/regression.py

@@ -55,8 +55,7 @@ if __name__ == '__main__':
     reader = hub.reader.RegressionReader(
         dataset=dataset,
         vocab_path=module.get_vocab_path(),
-        max_seq_len=args.max_seq_len,
-        use_task_id=args.use_taskid)
+        max_seq_len=args.max_seq_len)
 
     # Construct transfer learning network
     # Use "pooled_output" for classification tasks on an entire sentence.
@@ -72,9 +71,6 @@ if __name__ == '__main__':
         inputs["input_mask"].name,
     ]
 
-    if args.use_taskid:
-        feed_list.append(inputs["task_ids"].name)
-
     # Select finetune strategy, setup config and finetune
     strategy = hub.AdamWeightDecayStrategy(
         weight_decay=args.weight_decay,

demo/sequence-labeling/sequence_label.py

@@ -51,7 +51,6 @@ if __name__ == '__main__':
         dataset=dataset,
         vocab_path=module.get_vocab_path(),
         max_seq_len=args.max_seq_len,
-        use_task_id=use_taskid,
         sp_model_path=module.get_spm_path(),
         word_dict_path=module.get_word_dict_path())
 

demo/text-classification/README.md

@@ -40,7 +40,6 @@
 --max_seq_len: ERNIE/BERT模型使用的最大序列长度，最大不能超过512, 若出现显存不足，请适当调低这一参数
 --use_data_parallel: 是否使用并行计算，默认False。打开该功能依赖nccl库。
 --use_pyreader: 是否使用pyreader，默认False。
---use_taskid: 是否使用taskid，taskid是ERNIE 2.0特有的，use_taskid=True表示使用ERNIE 2.0；如果想使用ERNIE 1.0 或者BERT等module，use_taskid应该设置为False。
 
 # 任务相关
 --checkpoint_dir: 模型保存路径，PaddleHub会自动保存验证集上表现最好的模型
@@ -86,8 +85,7 @@ dataset = hub.dataset.ChnSentiCorp()
 reader = hub.reader.ClassifyReader(
     dataset=dataset,
     vocab_path=module.get_vocab_path(),
-    max_seq_len=128,
-    use_task_id=False)
+    max_seq_len=128)
 metrics_choices = ["acc"]
 ```
 
@@ -99,8 +97,6 @@ metrics_choices = ["acc"]
 
 `max_seq_len` 需要与Step1中context接口传入的序列长度保持一致
 
-`use_task_id` 表示是否使用ERNIR 2.0 module
-
 ClassifyReader中的`data_generator`会自动按照模型对应词表对数据进行切词，以迭代器的方式返回ERNIE/BERT所需要的Tensor格式，包括`input_ids`，`position_ids`，`segment_id`与序列对应的mask `input_mask`.
 
 **NOTE**: Reader返回tensor的顺序是固定的，默认按照input_ids, position_ids, segment_id, input_mask这一顺序返回。

demo/text-classification/predict.py

@@ -130,8 +130,7 @@ if __name__ == '__main__':
     reader = hub.reader.ClassifyReader(
         dataset=dataset,
         vocab_path=module.get_vocab_path(),
-        max_seq_len=args.max_seq_len,
-        use_task_id=args.use_taskid)
+        max_seq_len=args.max_seq_len)
 
     # Construct transfer learning network
     # Use "pooled_output" for classification tasks on an entire sentence.
@@ -147,9 +146,6 @@ if __name__ == '__main__':
         inputs["input_mask"].name,
     ]
 
-    if args.use_taskid:
-        feed_list.append(inputs["task_ids"].name)
-
     # Setup runing config for PaddleHub Finetune API
     config = hub.RunConfig(
         use_data_parallel=False,

demo/text-classification/text_classifier.py

@@ -121,27 +121,11 @@ if __name__ == '__main__':
     # Start preparing parameters for reader and task accoring to module
     # For ernie_v2, it has an addition embedding named task_id
     # For ernie_v2_chinese_tiny, it use an addition sentence_piece_vocab to tokenize
-    if module.name.startswith("ernie_v2"):
-        use_taskid = True
-    else:
-        use_taskid = False
-
     inputs, outputs, program = module.context(
         trainable=True, max_seq_len=args.max_seq_len)
     # Construct transfer learning network
     # Use "pooled_output" for classification tasks on an entire sentence.
     # Use "sequence_output" for token-level output.
-
-    check = [inputs["task_ids"].name]
-    global_block = program.global_block()
-    for op in global_block.ops:
-        for input_arg in op.input_arg_names:
-            for ch in check:
-                if ch in input_arg:
-                    print(op)
-                    check.append(input_arg)
-                    break
-    exit(0)
     pooled_output = outputs["pooled_output"]
 
     # Setup feed list for data feeder
@@ -152,8 +136,6 @@ if __name__ == '__main__':
         inputs["segment_ids"].name,
         inputs["input_mask"].name,
     ]
-    if use_taskid:
-        feed_list.append(inputs["task_ids"].name)
     # Finish preparing parameter for reader and task accoring to modul
 
     # Define reader
@@ -161,7 +143,6 @@ if __name__ == '__main__':
         dataset=dataset,
         vocab_path=module.get_vocab_path(),
         max_seq_len=args.max_seq_len,
-        use_task_id=use_taskid,
         sp_model_path=module.get_spm_path(),
         word_dict_path=module.get_word_dict_path())
 

paddlehub/module/module.py

@@ -606,15 +606,11 @@ class Module(object):
                     "task_ids"
                 ]
                 logger.warning(
-                    "%s will exploite task_id, the arguement use_taskid of Reader class must be True."
-                    % self.name)
+                    "For %s, it's no necessary to feed task_ids." % self.name)
             else:
                 feed_list = [
                     "input_ids", "position_ids", "segment_ids", "input_mask"
                 ]
-                logger.warning(
-                    "%s has no task_id, the arguement use_taskid of Reader class must be False."
-                    % self.name)
             for tensor_name in feed_list:
                 seq_tensor_shape = [-1, max_seq_len, 1]
                 logger.info("The shape of input tensor[{}] set to {}".format(