Merge pull request PaddlePaddle#663 from zhanghan1992/develop

change the path of ernie-gram and fix mrc

ernie_gram/README.en.md → ernie-gram/README.en.md

@@ -36,7 +36,7 @@ wget https://ernie-github.cdn.bcebos.com/data-xnli.tar.gz
 tar xf data-xnli.tar.gz
 cd ..
 #demo for NLI task
-sh ernie_gram/run_cls.sh ernie_gram/task_configs/xnli_conf
+sh run_cls.sh task_configs/xnli_conf
 ```
 
 ### Setup

ernie_gram/README.zh.md → ernie-gram/README.zh.md

@@ -38,7 +38,7 @@ wget https://ernie-github.cdn.bcebos.com/data-xnli.tar.gz
 tar xf data-xnli.tar.gz
 cd ..
 #demo for NLI task
-sh ernie_gram/run_cls.sh ernie_gram/task_configs/xnli_conf
+sh run_cls.sh task_configs/xnli_conf
 ```
 
 

ernie_gram/finetune_classifier_distributed.py → ernie-gram/finetune_classifier_distributed.py

@@ -23,6 +23,8 @@
 import numpy as np
 import logging
 #from visualdl import LogWriter
+import sys
+sys.path.append("../")
 
 from pathlib import Path
 import paddle as P
@@ -32,8 +34,8 @@
 #from model.bert import BertConfig, BertModelLayer
 from ernie.modeling_ernie import ErnieModel, ErnieModelForSequenceClassification
 from ernie.tokenizing_ernie import ErnieTokenizer, ErnieTinyTokenizer
-from ernie_gram.optimization import AdamW
-from ernie_gram.utils import create_if_not_exists, get_warmup_and_linear_decay
+from optimization import AdamW
+from utils import create_if_not_exists, get_warmup_and_linear_decay
 
 log.setLevel(logging.DEBUG)
 logging.getLogger().setLevel(logging.DEBUG)

ernie_gram/finetune_mrc.py → ernie-gram/finetune_mrc.py

@@ -30,6 +30,8 @@
 import argparse
 from functools import partial
 from io import open
+import sys
+sys.path.append("../")
 
 import numpy as np
 import logging
@@ -38,22 +40,23 @@
 
 from propeller import log
 import propeller.paddle as propeller
-from ernie_gram.optimization import AdamW
+from optimization import AdamW
 
 from ernie.modeling_ernie import ErnieModel, ErnieModelForQuestionAnswering
 from ernie.tokenizing_ernie import ErnieTokenizer, ErnieTinyTokenizer
 #from ernie.optimization import AdamW, LinearDecay
 
-from ernie_gram.mrc import mrc_reader
-from ernie_gram.mrc import mrc_metrics
-from ernie_gram.utils import create_if_not_exists, get_warmup_and_linear_decay
+from mrc import mrc_reader
+from mrc import mrc_metrics
+from utils import create_if_not_exists, get_warmup_and_linear_decay
 
 log.setLevel(logging.DEBUG)
 logging.getLogger().setLevel(logging.DEBUG)
 
 
-def evaluate(model, ds, all_examples, all_features, tokenizer, args):
-    dev_file = json.loads(open(args.dev_file, encoding='utf8').read())
+def evaluate(model, ds, all_examples, all_features, tokenizer, args, is_test=False):
+    dev_file = args.dev_file if not is_test else args.test_file
+    dev_file = json.loads(open(dev_file, encoding='utf8').read())
     with P.no_grad():
         log.debug('start eval')
         model.eval()
@@ -84,8 +87,8 @@ def evaluate(model, ds, all_examples, all_features, tokenizer, args):
         return f1, em
 
 
-def train(model, train_dataset, dev_dataset, dev_examples, dev_features,
-          tokenizer, args):
+def train(model, train_dataset, dev_dataset, dev_examples, dev_features, 
+          tokenizer, args, test_dataset=None, test_examples=None, test_features=None, do_test=False):
     model = P.DataParallel(model)
 
     max_steps = args.max_steps
@@ -142,10 +145,14 @@ def train(model, train_dataset, dev_dataset, dev_examples, dev_features,
                 log.debug(msg)
 
             if env.dev_id == 0 and step % 100==0 and step:
-                print(step)
                 f1, em = evaluate(model, dev_dataset, dev_examples,
                                   dev_features, tokenizer, args)
-                log.debug('[step %d] eval result: f1 %.5f em %.5f' %
+                log.debug('[step %d] dev eval result: f1 %.5f em %.5f' %
+                          (step, f1, em))
+                if do_test:
+                    f1, em = evaluate(model, test_dataset, test_examples,
+                                  test_features, tokenizer, args, True)
+                    log.debug('[step %d] test eval result: f1 %.5f em %.5f' %
                           (step, f1, em))
                 if env.dev_id == 0 and args.save_dir is not None:
                     P.save(model.state_dict(), args.save_dir / 'ckpt.bin')
@@ -177,7 +184,12 @@ def train(model, train_dataset, dev_dataset, dev_examples, dev_features,
         type=str,
         required=True,
         help='data directory includes train / develop data')
-    parser.add_argument('--warmup_proportion', type=float, default=0.0)
+    parser.add_argument(
+        '--test_file',
+        type=str,
+        default=None,
+        help='data directory includes train / develop data')
+    parser.add_argument('--warmup_proportion', type=float, default=0.1)
     parser.add_argument('--lr', type=float, default=3e-5, help='learning rate')
     parser.add_argument(
         '--save_dir', type=Path, required=True, help='model output directory')
@@ -216,6 +228,10 @@ def train(model, train_dataset, dev_dataset, dev_examples, dev_features,
     dev_examples = mrc_reader.read_files(args.dev_file, is_training=False)
     dev_features = mrc_reader.convert_example_to_features(
         dev_examples, args.max_seqlen, tokenizer, is_training=False)
+    if args.test_file:
+        test_examples = mrc_reader.read_files(args.test_file, is_training=False)
+        test_features = mrc_reader.convert_example_to_features(
+            test_examples, args.max_seqlen, tokenizer, is_training=False)
 
     log.info('train examples: %d, features: %d' %
              (len(train_examples), len(train_features)))
@@ -235,16 +251,28 @@ def map_fn(unique_id, example_index, doc_span_index, tokens,
 
     dev_dataset = propeller.data.Dataset.from_list(dev_features).map(
         map_fn).padded_batch(args.bsz)
-
     model = ErnieModelForQuestionAnswering.from_pretrained(
         args.from_pretrained, name='')
 
-    train(model, train_dataset, dev_dataset, dev_examples, dev_features,
+    if args.test_file:
+        test_dataset = propeller.data.Dataset.from_list(test_features).map(
+            map_fn).padded_batch(args.bsz)
+        train(model, train_dataset, dev_dataset, dev_examples, dev_features,
+          tokenizer, args, test_dataset, test_examples, test_features, True)
+
+    else:
+        train(model, train_dataset, dev_dataset, dev_examples, dev_features,
           tokenizer, args)
 
+
+
     if env.dev_id == 0:
         f1, em = evaluate(model, dev_dataset, dev_examples, dev_features,
                           tokenizer, args)
-        log.debug('final eval result: f1 %.5f em %.5f' % (f1, em))
+        log.debug('final dev eval result: f1 %.5f em %.5f' % (f1, em))
+        if args.test_file:
+            f1, em = evaluate(model, test_dataset, test_examples, test_features,
+                          tokenizer, args, True)
+            log.debug('final test eval result: f1 %.5f em %.5f' % (f1, em))
     if env.dev_id == 0 and args.save_dir is not None:
         P.save(model.state_dict(), args.save_dir / 'ckpt.bin')

ernie_gram/finetune_ner.py → ernie-gram/finetune_ner.py

@@ -29,6 +29,8 @@
 import multiprocessing
 import pickle
 import logging
+import sys
+sys.path.append("../")
 
 from sklearn.metrics import f1_score
 import paddle as P
@@ -39,10 +41,10 @@
 log.setLevel(logging.DEBUG)
 logging.getLogger().setLevel(logging.DEBUG)
 
-from ernie_gram.utils import create_if_not_exists, get_warmup_and_linear_decay
+from utils import create_if_not_exists, get_warmup_and_linear_decay
 from ernie.modeling_ernie import ErnieModel, ErnieModelForSequenceClassification, ErnieModelForTokenClassification
 from ernie.tokenizing_ernie import ErnieTokenizer
-from ernie_gram.optimization import AdamW
+from optimization import AdamW
 
 parser = propeller.ArgumentParser('NER model with ERNIE')
 parser.add_argument('--max_seqlen', type=int, default=256)

ernie_gram/mrc/mrc_metrics.py → ernie-gram/mrc/mrc_metrics.py

@@ -349,7 +349,10 @@ def make_results(vocab, all_examples, all_features, all_results, n_best_size,
 
     unique_id_to_result = {}
     for result in all_results:
-        unique_id_to_result[result.unique_id] = result
+        try:
+        	unique_id_to_result[result.unique_id] = result
+        except:
+            continue
 
     _PrelimPrediction = collections.namedtuple(  # pylint: disable=invalid-name
         "PrelimPrediction", [

ernie_gram/optimization.py → ernie-gram/optimization.py

@@ -25,11 +25,13 @@
 import numpy as np
 import paddle as P
 import paddle.distributed.fleet as fleet
+import sys
+sys.path.append("../") 
 from propeller.paddle.train.hooks import RunHook
 import paddle.fluid as F
 log = logging.getLogger(__name__)
 
-from ernie_gram.utils import create_if_not_exists, get_warmup_and_linear_decay
+from utils import create_if_not_exists, get_warmup_and_linear_decay
 
 class AdamW(P.optimizer.AdamW):
     """AdamW object for dygraph"""

ernie_gram/run_cls.sh → ernie-gram/run_cls.sh

@@ -1,6 +1,6 @@
 source $1
 
-python3 -m paddle.distributed.launch ./ernie_gram/finetune_classifier_distributed.py \
+python3 -m paddle.distributed.launch ./finetune_classifier_distributed.py \
     --data_dir $data_dir \
     --max_steps $max_steps \
     --bsz $bsz \

ernie_gram/run_mrc.sh → ernie-gram/run_mrc.sh

@@ -1,6 +1,6 @@
 source $1
 export CUDA_VISIBLE_DEVICES=0
-python3 -m paddle.distributed.launch ./ernie_gram/finetune_mrc.py \
+python3 -m paddle.distributed.launch ./finetune_mrc.py \
     --train_file $train_file \
     --dev_file $dev_file \
     --max_steps $max_steps \

ernie_gram/run_ner.sh → ernie-gram/run_ner.sh

@@ -1,6 +1,6 @@
 source $1
 
-python3 -m paddle.distributed.launch ./ernie_gram/finetune_ner.py \
+python3 -m paddle.distributed.launch ./finetune_ner.py \
     --data_dir $data_dir \
     --max_steps $max_steps \
     --epoch $epoch \

ernie/file_utils.py

@@ -68,12 +68,9 @@ def _fetch_from_remote(url,
             f = done_file.open('wb')
             f.close()
         else:
-            while True:
-                if done_file.exists():
-                    break
-                else:
-                    time.sleep(1)
-
+            while not done_file.exists():
+                time.sleep(1)
+
     log.debug('%s cached in %s' % (url, cached_dir))
     return cached_dir_model