fix bugs

examples/mrc/README.md

-## Examples 4: Machine Reading Comprehension
+## Example 4: Machine Reading Comprehension
 This task is a machine reading comprehension task. The following sections detail model preparation, dataset preparation, and how to run the task.
 
 ### Step 1: Prepare Pre-trained Models & Datasets

examples/multi-task/README.md

-## Examples 6: Multi-Task Slot Filling
+## Example 6: joint training in dialogue
 This task is a slot filling task. During training, the task uses intent determination task to assist in training slot filling model. The following sections detail model preparation, dataset preparation, and how to run the task.
 
 ### Step 1: Prepare Pre-trained Models & Datasets
@@ -73,27 +73,37 @@ global step: 20, intent: step 7/311 (epoch 0), loss: 3.487, speed: 10.28 steps/s
 After the run, you can view the saved models in the `outputs/` folder.
 
 
-If you want to use the trained model to predict the `atis_slot` data, run:
+If you want to use the trained model to predict the `atis_slot & atis_intent` data, run:
 
 ```shell
-python predict.py
+python predict-slot.py
+python predict-intent.py
 ```
 
 If you want to specify a specific gpu or use multiple gpus for predict, please use **`CUDA_VISIBLE_DEVICES`**, for example:
 
 ```shell
-CUDA_VISIBLE_DEVICES=0,1,2 python predict.py
+CUDA_VISIBLE_DEVICES=0,1,2 python predict-slot.py
+CUDA_VISIBLE_DEVICES=0,1,2 python predict-slot.py
 ```
 
+After the run, you can view the predictions in the `outputs/predict-slot` folder and `outputs/predict-intent` folder. Here are some examples of predictions:
 
-After the run, you can view the predictions in the `outputs/predict` folder. Here are some examples of predictions:
-
+`atis_slot`:
 ```
-
 [129, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 5, 19, 1, 1, 1, 1, 1, 21, 21, 68, 129]
 [129, 1, 39, 37, 1, 1, 1, 1, 1, 2, 1, 5, 19, 1, 23, 3, 4, 129, 129, 129, 129, 129]
 [129, 1, 39, 37, 1, 1, 1, 1, 1, 1, 2, 1, 5, 19, 129, 129, 129, 129, 129, 129, 129, 129]
-[129, 1, 1, 1, 1, 1, 1, 14, 15, 1, 2, 1, 5, 19, 1, 39, 37, 129, 129, 129, 129, 129]
+[129, 1, 1, 1, 1, 1, 1, 14, 15, 1, 2, 1, 5, 19, 1, 39, 37, 129, 24, 129, 129, 129]
+```
+
+`atis_intent`:
+```
+{"index": 0, "logits": [9.938603401184082, -0.3914794623851776, -0.050973162055015564, -1.0229418277740479, 0.04799401015043259, -0.9632213115692139, -0.6427211761474609, -1.337939739227295, -0.7969412803649902, -1.4441455602645874, -0.6339573264122009, -1.0393054485321045, -0.9242327213287354, -1.9637483358383179, 0.16733427345752716, -0.5280354619026184, -1.7195699214935303, -2.199411630630493, -1.2833174467086792, -1.3081035614013672, -1.6036226749420166, -1.8527079820632935, -2.289180040359497, -2.267214775085449, -2.2578916549682617, -2.2010505199432373], "probs": [0.999531626701355, 3.26210938510485e-05, 4.585415081237443e-05, 1.7348344044876285e-05, 5.06243304698728e-05, 1.8415948943584226e-05, 2.5373808966833167e-05, 1.266065828531282e-05, 2.174747896788176e-05, 1.1384962817828637e-05, 2.5597169951652177e-05, 1.7066764485207386e-05, 1.914815220516175e-05, 6.771284006390488e-06, 5.70411684748251e-05, 2.8457265216275118e-05, 8.644025911053177e-06, 5.349628736439627e-06, 1.3371440218179487e-05, 1.3044088518654462e-05, 9.706698619993404e-06, 7.5665011536329985e-06, 4.890325726591982e-06, 4.99892985317274e-06, 5.045753368904116e-06, 5.340866664482746e-06], "label": 0}
+{"index": 1, "logits": [0.8863624930381775, -2.232290506362915, 8.191509246826172, -0.03161466494202614, -0.9149583578109741, -2.172696352005005, -0.3937145471572876, -0.3954394459724426, 1.5333592891693115, 0.8630291223526001, -0.9684226512908936, -2.722721815109253, -0.0060247331857681274, -0.9865402579307556, 1.6328885555267334, 0.3972966969013214, 0.27919167280197144, -1.4911551475524902, -0.9552251696586609, -0.9169244170188904, -0.810670793056488, -1.5118697881698608, -2.0140435695648193, -1.6299077272415161, -1.8589974641799927, -2.07601261138916], "probs": [0.0006675600307062268, 2.9517297662096098e-05, 0.9932880997657776, 0.0002665741485543549, 0.0001102013120544143, 3.132982965325937e-05, 0.00018559220188762993, 0.00018527248175814748, 0.0012749042361974716, 0.0006521637551486492, 0.00010446414671605453, 1.8075270418194123e-05, 0.0002734838053584099, 0.00010258861584588885, 0.0014083238784223795, 0.00040934717981144786, 0.00036374686169438064, 6.193659646669403e-05, 0.00010585198469925672, 0.00010998480865964666, 0.0001223145518451929, 6.0666847275570035e-05, 3.671637750812806e-05, 5.391232480178587e-05, 4.287416595616378e-05, 3.4510172554291785e-05], "label": 0}
+{"index": 2, "logits": [9.789957046508789, -0.1730862706899643, -0.7198237776756287, -1.0460278987884521, 0.23521068692207336, -0.5075851678848267, -0.44724929332733154, -1.2945927381515503, -0.6984466314315796, -1.8749892711639404, -0.4631594121456146, -0.6256799697875977, -1.0252169370651245, -1.951456069946289, -0.17572557926177979, -0.6771697402000427, -1.7992591857910156, -2.1457295417785645, -1.4203097820281982, -1.4963451623916626, -1.692310094833374, -1.9219486713409424, -2.2533645629882812, -2.430952310562134, -2.3094685077667236, -2.2399914264678955], "probs": [0.9994625449180603, 4.708383130491711e-05, 2.725377635215409e-05, 1.9667899323394522e-05, 7.082601223373786e-05, 3.3697724575176835e-05, 3.579350595828146e-05, 1.5339375750045292e-05, 2.784266871458385e-05, 8.58508519741008e-06, 3.522853512549773e-05, 2.9944207199150696e-05, 2.0081495677004568e-05, 7.953084605105687e-06, 4.695970710599795e-05, 2.8441407266655006e-05, 9.26048778637778e-06, 6.548832516273251e-06, 1.3527245755540207e-05, 1.2536826943687629e-05, 1.030578732752474e-05, 8.19125762063777e-06, 5.880556273041293e-06, 4.923717369820224e-06, 5.559719284065068e-06, 5.9597273320832755e-06], "label": 0}
+{"index": 3, "logits": [9.787659645080566, -0.6223222017288208, -0.03971472755074501, -1.038114070892334, 0.24018540978431702, -0.8904737830162048, -0.7114139795303345, -1.2315020561218262, -0.5120854377746582, -1.4273980855941772, -0.44618460536003113, -1.0241562128067017, -0.9727545380592346, -1.8587366342544556, 0.020689941942691803, -0.6228570342063904, -1.6020199060440063, -2.130260467529297, -1.370570421218872, -1.40530526638031, -1.6782578229904175, -1.94076669216156, -2.2038567066192627, -2.336832284927368, -2.268157720565796, -2.140028953552246], "probs": [0.9994485974311829, 3.0113611501292326e-05, 5.392447565100156e-05, 1.986949791898951e-05, 7.134198676794767e-05, 2.303065048181452e-05, 2.7546762794372626e-05, 1.6375688574044034e-05, 3.362310235388577e-05, 1.3462414244713727e-05, 3.591357381083071e-05, 2.0148761905147694e-05, 2.12115264730528e-05, 8.74570196174318e-06, 5.728216274292208e-05, 3.0097504350123927e-05, 1.1305383850412909e-05, 6.666126409982098e-06, 1.4249604646465741e-05, 1.3763145034317859e-05, 1.0475521776243113e-05, 8.056933438638225e-06, 6.193143690325087e-06, 5.422014055511681e-06, 5.807448815176031e-06, 6.601325367228128e-06], "label": 0}
+
 ```
 
 ### Step 3: Evaluate
@@ -101,11 +111,19 @@ After the run, you can view the predictions in the `outputs/predict` folder. Her
 Once you have the prediction, you can run the evaluation script to evaluate the model:
 
 ```shell
-python evaluate.py
+python evaluate-slot.py
+python evaluate-intent.py
 ```
 
 The evaluation results are as follows:
 
+`atis_slot`:
+```
+precision: 0.894397728514, recall: 0.894104803493, f1: 0.894251242016
+```
+
+`atis_intent`:
 ```
-precision: 0.894518453811, recall: 0.894323144105, f1: 0.894420788296
+data num: 893
+precision: 0.708846584546, recall: 1.0, f1: 0.999999995
 ```

examples/multi-task/evaluate-intent.py

+#  -*- coding: utf-8 -*-
+
+import json
+import numpy as np
+
+def accuracy(preds, labels):
+    preds = np.array(preds)
+    labels = np.array(labels) 
+    return (preds == labels).mean()
+
+def f1(preds, labels):
+    preds = np.array(preds)
+    labels = np.array(labels)
+    tp = np.sum((labels == '1') & (preds == '1'))
+    tn = np.sum((labels == '0') & (preds == '0'))
+    fp = np.sum((labels == '0') & (preds == '1'))
+    fn = np.sum((labels == '1') & (preds == '0'))
+    p = tp * 1.0 / (tp + fp) 
+    r = tp * 1.0 / (tp + fn) * 1.0
+    f1 = (2 * p * r) / (p + r + 1e-8)
+    return f1
+  
+def recall(preds, labels):
+    preds = np.array(preds)
+    labels = np.array(labels)
+    # recall=TP/(TP+FN)
+    tp = np.sum((labels == '1') & (preds == '1'))
+    fn = np.sum((labels == '1') & (preds == '0'))
+    re = tp * 1.0 / (tp + fn)
+    return re
+
+
+def res_evaluate(res_dir="./outputs/predict-intent/predictions.json", eval_phase='test'):
+    if eval_phase == 'test':
+        data_dir="./data/atis/atis_intent/test.tsv"
+    elif eval_phase == 'dev':
+        data_dir="./data/dev.tsv"
+
+    else:
+        assert eval_phase in ['dev', 'test'], 'eval_phase should be dev or test'
+    
+    labels = []
+    with open(data_dir, "r") as file:
+        first_flag = True
+        for line in file:
+            line = line.split("\t")
+            label = line[0]
+            if label=='label':
+                continue
+            labels.append(str(label))
+    file.close()
+
+    preds = []
+    with open(res_dir, "r") as file:
+        for line in file.readlines():
+            line = json.loads(line)
+            pred = line['label']
+            preds.append(str(pred))
+    file.close()
+    assert len(labels) == len(preds), "prediction result doesn't match to labels"
+    print('data num: {}'.format(len(labels)))
+    print("precision: {}, recall: {}, f1: {}".format(accuracy(preds, labels), recall(preds, labels), f1(preds, labels)))
+
+res_evaluate()

examples/multi-task/evaluate.py

-#  -*- coding: utf-8 -*-
-
-import json
-
-
-def load_label_map(map_dir="./data/atis/atis_slot/label_map.json"):
-    """
-    :param map_dir: dict indictuing chunk type
-    :return:
-    """
-    return json.load(open(map_dir, "r"))
-
-
-def cal_chunk(total_res, total_label):
-    assert len(total_label) == len(total_res), 'prediction result doesn\'t match to labels'
-    num_labels = 0
-    num_corr = 0
-    num_infers = 0
-    for res, label in zip(total_res, total_label):
-        assert len(res) == len(label), "prediction result doesn\'t match to labels"
-        num_labels += sum([0 if i == 6 else 1 for i in label])
-        num_corr += sum([1 if label[i] == res[i] and label[i] != 6 else 0 for i in range(len(label))])
-        num_infers += sum([0 if i == 6 else 1 for i in res])
-
-    precision = num_corr * 1.0 / num_infers if num_infers > 0 else 0.0
-    recall = num_corr * 1.0 / num_labels if num_labels > 0 else 0.0
-    f1 = 2 * precision * recall / (precision + recall) if precision + recall > 0 else 0.0
-
-    return precision, recall, f1
-
-
-def res_evaluate(res_dir="./outputs/predict/predictions.json", data_dir="./data/atis/atis_slot/test.tsv"):
-    label_map = load_label_map()
-
-    total_label = []
-    with open(data_dir, "r") as file:
-        first_flag = True
-        for line in file:
-            if first_flag:
-                first_flag = False
-                continue
-            line = line.strip("\n")
-            if len(line) == 0:
-                continue
-            line = line.split("\t")
-            if len(line) < 2:
-                continue
-            labels = line[1][:-1].split("\x02")
-            total_label.append(labels)
-    total_label = [[label_map[j] for j in i] for i in total_label]
-
-    total_res = []
-    with open(res_dir, "r") as file:
-        cnt = 0
-        for line in file:
-            line = line.strip("\n")
-            if len(line) == 0:
-                continue
-            try:
-                res_arr = json.loads(line)
-
-                if len(total_label[cnt]) < len(res_arr):
-                    total_res.append(res_arr[1: 1 + len(total_label[cnt])])
-                elif len(total_label[cnt]) == len(res_arr):
-                    total_res.append(res_arr)
-                else:
-                    total_res.append(res_arr)
-                    total_label[cnt] = total_label[cnt][: len(res_arr)]
-            except:
-                print("json format error: {}".format(cnt))
-                print(line)
-
-            cnt += 1
-
-    precision, recall, f1 = cal_chunk(total_res, total_label)
-    print("precision: {}, recall: {}, f1: {}".format(precision, recall, f1))
-
-res_evaluate()

examples/multi-task/predict-intent.py

+# coding=utf-8
+import paddlepalm as palm
+import json
+from paddlepalm.distribute import gpu_dev_count
+
+
+if __name__ == '__main__':
+
+    # configs
+    max_seqlen = 256
+    batch_size = 16
+    num_epochs = 6 
+    print_steps = 5
+    num_classes = 26
+    vocab_path = './pretrain/ernie-en-base/vocab.txt'
+    predict_file = './data/atis/atis_intent/test.tsv'
+    save_path = './outputs/'
+    pred_output = './outputs/predict-intent/'
+    save_type = 'ckpt'
+    random_seed = 0
+
+    pre_params = './pretrain/ernie-en-base/params'
+    config = json.load(open('./pretrain/ernie-en-base/ernie_config.json'))
+    input_dim = config['hidden_size']
+
+    # -----------------------  for prediction ----------------------- 
+
+    # step 1-1: create readers for prediction
+    print('prepare to predict...')
+    predict_cls_reader = palm.reader.ClassifyReader(vocab_path, max_seqlen, seed=random_seed, phase='predict')
+    # step 1-2: load the training data
+    predict_cls_reader.load_data(predict_file, batch_size)
+    
+    # step 2: create a backbone of the model to extract text features
+    pred_ernie = palm.backbone.ERNIE.from_config(config, phase='predict')
+
+    # step 3: register the backbone in reader
+    predict_cls_reader.register_with(pred_ernie)
+    
+    # step 4: create the task output head
+    cls_pred_head = palm.head.Classify(num_classes, input_dim, phase='predict')
+    
+    # step 5-1: create a task trainer
+    trainer = palm.Trainer("intent")
+    # step 5-2: build forward graph with backbone and task head
+    trainer.build_predict_forward(pred_ernie, cls_pred_head)
+ 
+    # step 6: load pretrained model
+    pred_model_path = './outputs/ckpt.step9282'
+    pred_ckpt = trainer.load_ckpt(pred_model_path)
+
+    # step 7: fit prepared reader and data
+    trainer.fit_reader(predict_cls_reader, phase='predict')
+
+    # step 8: predict
+    print('predicting..')
+    trainer.predict(print_steps=print_steps, output_dir=pred_output)
\ No newline at end of file

examples/multi-task/predict.py → examples/multi-task/predict-slot.py

@@ -11,15 +11,14 @@ if __name__ == '__main__':
     batch_size = 16
     num_epochs = 6 
     print_steps = 5
-    lr = 5e-5
     num_classes = 130
-    random_seed = 1
     label_map = './data/atis/atis_slot/label_map.json'
     vocab_path = './pretrain/ernie-en-base/vocab.txt'
     predict_file = './data/atis/atis_slot/test.tsv'
     save_path = './outputs/'
-    pred_output = './outputs/predict/'
+    pred_output = './outputs/predict-slot/'
     save_type = 'ckpt'
+    random_seed = 0
 
     pre_params = './pretrain/ernie-en-base/params'
     config = json.load(open('./pretrain/ernie-en-base/ernie_config.json'))
@@ -29,7 +28,7 @@ if __name__ == '__main__':
 
     # step 1-1: create readers for prediction
     print('prepare to predict...')
-    predict_seq_label_reader = palm.reader.SequenceLabelReader(vocab_path, max_seqlen, label_map, phase='predict')
+    predict_seq_label_reader = palm.reader.SequenceLabelReader(vocab_path, max_seqlen, label_map, seed=random_seed, phase='predict')
     # step 1-2: load the training data
     predict_seq_label_reader.load_data(predict_file, batch_size)
    
@@ -48,7 +47,7 @@ if __name__ == '__main__':
     trainer_seq_label.build_predict_forward(pred_ernie, seq_label_pred_head)
     
     # step 6: load pretrained model
-    pred_model_path = './outputs/1580822697.73-ckpt.step9282'
+    pred_model_path = './outputs/ckpt.step9282'
     pred_ckpt = trainer_seq_label.load_ckpt(pred_model_path)
     
     # step 7: fit prepared reader and data

examples/multi-task/run.py

@@ -35,30 +35,30 @@ if __name__ == '__main__':
 
     # step 1-1: create readers for training 
     seq_label_reader = palm.reader.SequenceLabelReader(vocab_path, max_seqlen, label_map, seed=random_seed)
-    match_reader = palm.reader.MatchReader(vocab_path, max_seqlen, seed=random_seed)
+    cls_reader = palm.reader.ClassifyReader(vocab_path, max_seqlen, seed=random_seed)
 
     # step 1-2: load the training data
     seq_label_reader.load_data(train_slot, file_format='tsv', num_epochs=None, batch_size=batch_size)
-    match_reader.load_data(train_intent, file_format='tsv', num_epochs=None, batch_size=batch_size)
- 
+    cls_reader.load_data(train_intent, batch_size=batch_size, num_epochs=None)
+
     # step 2: create a backbone of the model to extract text features
     ernie = palm.backbone.ERNIE.from_config(config)
 
     # step 3: register the backbone in readers
     seq_label_reader.register_with(ernie)
-    match_reader.register_with(ernie)
+    cls_reader.register_with(ernie)
 
     # step 4: create task output heads
     seq_label_head = palm.head.SequenceLabel(num_classes, input_dim, dropout_prob)
-    match_head = palm.head.Match(num_classes_intent, input_dim, dropout_prob)
+    cls_head = palm.head.Classify(num_classes_intent, input_dim, dropout_prob)
    
     # step 5-1: create a task trainer
     trainer_seq_label = palm.Trainer("slot", mix_ratio=1.0)
-    trainer_match = palm.Trainer("intent", mix_ratio=0.5)
-    trainer = palm.MultiHeadTrainer([trainer_seq_label, trainer_match])
+    trainer_cls = palm.Trainer("intent", mix_ratio=1.0)
+    trainer = palm.MultiHeadTrainer([trainer_seq_label, trainer_cls])
     # # step 5-2: build forward graph with backbone and task head
-    loss_var1 = trainer_match.build_forward(ernie, match_head)
-    loss_var2 = trainer_seq_label.build_forward(ernie, seq_label_head)
+    loss1 = trainer_cls.build_forward(ernie, cls_head)
+    loss2 = trainer_seq_label.build_forward(ernie, seq_label_head)
     loss_var = trainer.build_forward()
 
     # step 6-1*: use warmup
@@ -71,13 +71,13 @@ if __name__ == '__main__':
     trainer.build_backward(optimizer=adam, weight_decay=weight_decay)
 
     # step 7: fit prepared reader and data
-    trainer.fit_readers_with_mixratio([seq_label_reader, match_reader], "slot", num_epochs)
+    trainer.fit_readers_with_mixratio([seq_label_reader, cls_reader], "slot", num_epochs)
 
     # step 8-1*: load pretrained parameters
     trainer.load_pretrain(pre_params)
     # step 8-2*: set saver to save model
-    # save_steps = int(n_steps-batch_size)
-    save_steps = 10
-    trainer_seq_label.set_saver(save_path=save_path, save_steps=save_steps, save_type=save_type, is_multi=True)
+    save_steps = int(n_steps-batch_size) // 2
+    # save_steps = 10
+    trainer.set_saver(save_path=save_path, save_steps=save_steps, save_type=save_type, is_multi=True)
     # step 8-3: start training
     trainer.train(print_steps=print_steps)
\ No newline at end of file

examples/tagging/README.md

-## Examples 3: Tagging
+## Example 3: Tagging
 This task is a named entity recognition task. The following sections detail model preparation, dataset preparation, and how to run the task.
 
 ### Step 1: Prepare Pre-trained Models & Datasets

examples/tagging/run.py

@@ -77,7 +77,7 @@ if __name__ == '__main__':
 
     # step 1-1: create readers for prediction
     print('prepare to predict...')
-    predict_seq_label_reader = palm.reader.SequenceLabelReader(vocab_path, max_seqlen, label_map, phase='predict')
+    predict_seq_label_reader = palm.reader.SequenceLabelReader(vocab_path, max_seqlen, label_map, seed=random_seed, phase='predict')
     # step 1-2: load the training data
     predict_seq_label_reader.load_data(predict_file, batch_size)
    

paddlepalm/head/cls.py

@@ -111,7 +111,7 @@ class Classify(Head):
             with open(os.path.join(output_dir, 'predictions.json'), 'w') as writer:
                 for i in range(len(self._preds)):
                     label = 0 if self._preds[i][0] > self._preds[i][1] else 1
-                    result = {'index': i, 'label': label, 'logits': self._preds[i], 'probs': self._preds[i]}
+                    result = {'index': i, 'label': label, 'logits': self._preds[i], 'probs': self._probs[i]}
                     result = json.dumps(result)
                     writer.write(result+'\n')
             print('Predictions saved at '+os.path.join(output_dir, 'predictions.json'))

paddlepalm/multihead_trainer.py

@@ -52,7 +52,7 @@ class MultiHeadTrainer(Trainer):
             'input_varnames': 'self._pred_input_varname_list',
             'fetch_list': 'self._pred_fetch_name_list'}
 
-        # self._check_save = lambda: False
+        self._check_save = lambda: False
         for t in self._trainers:
             t._set_multitask()
 
@@ -236,7 +236,7 @@ class MultiHeadTrainer(Trainer):
                        loss, print_steps / time_cost))
                 time_begin = time.time()
 
-            # self._check_save()
+            self._check_save()
             finish = self._check_finish(self._trainers[task_id].name)
             if finish:
                 break
@@ -266,7 +266,7 @@ class MultiHeadTrainer(Trainer):
         rt_outputs = self._trainers[task_id].train_one_step(batch)
 
         self._cur_train_step += 1
-        # self._check_save()
+        self._check_save()
         return rt_outputs, task_id
         
         # if dev_count > 1:

paddlepalm/trainer.py

@@ -107,6 +107,7 @@ class Trainer(object):
             'fetch_list': 'self._pred_fetch_name_list'}
 
         self._lock = False
+        self._lock_prog = False
         self._build_forward = False
 
     def build_forward(self, backbone, task_head):
@@ -163,7 +164,7 @@ class Trainer(object):
 
         self._train_prog = train_prog
         self._train_init_prog = train_init_prog
-        if not self._multi_task:
+        if not self._lock_prog:
             with fluid.program_guard(train_prog, train_init_prog):
                 net_inputs = reader_helper.create_net_inputs(input_attrs, async=False)
                 bb_output_vars = backbone.build(net_inputs)
@@ -184,7 +185,7 @@ class Trainer(object):
         task_inputs['reader'] = task_inputs_from_reader
 
         scope = self.name+'.'
-        if not self._multi_task:
+        if not self._lock_prog:
             with fluid.program_guard(train_prog, train_init_prog):
                 with fluid.unique_name.guard(scope):
                     output_vars = self._build_head(task_inputs, phase='train', scope=scope)
@@ -209,7 +210,7 @@ class Trainer(object):
         # task_id_vec = layers.one_hot(task_id_var, num_instances)
         # losses = fluid.layers.concat([task_output_vars[inst.name+'/loss'] for inst in instances], axis=0)
         # loss = layers.reduce_sum(task_id_vec * losses)
-        if not self._multi_task:
+        if not self._lock_prog:
             with fluid.program_guard(train_prog, train_init_prog):
                 loss_var = fluid.layers.reduce_sum(task_output_vars[self.name+'.loss'])
         else:
@@ -548,6 +549,7 @@ class Trainer(object):
                         self._save(save_path, suffix='pred.step'+str(self._cur_train_step))
                         print('predict model has been saved at '+os.path.join(save_path, 'pred.step'+str(self._cur_train_step)))
                 if self._save_ckpt:
+                    print(self._train_prog)
                     if is_multi:
                         fluid.io.save_persistables(self._exe, os.path.join(save_path, 'ckpt.step'+str(self._cur_train_step)), self._train_prog)
                         print('checkpoint has been saved at '+os.path.join(save_path, 'ckpt.step'+str(self._cur_train_step)))