Merge pull request #58 from lliimsft/master

Bug fix in examples;correct t_total for distributed training;run pred…

examples/run_classifier.py

@@ -33,6 +33,7 @@ from torch.utils.data.distributed import DistributedSampler
 from pytorch_pretrained_bert.tokenization import BertTokenizer
 from pytorch_pretrained_bert.modeling import BertForSequenceClassification
 from pytorch_pretrained_bert.optimization import BertAdam
+from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
 
 logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s', 
                     datefmt = '%m/%d/%Y %H:%M:%S',
@@ -155,8 +156,8 @@ class MnliProcessor(DataProcessor):
             if i == 0:
                 continue
             guid = "%s-%s" % (set_type, line[0])
-            text_a = line[8])
-            text_b = line[9])
+            text_a = line[8]
+            text_b = line[9]
             label = line[-1]
             examples.append(
                 InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
@@ -482,7 +483,7 @@ def main():
             len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs)
 
     # Prepare model
-    model = BertForSequenceClassification.from_pretrained(args.bert_model, len(label_list),
+    model = BertForSequenceClassification.from_pretrained(args.bert_model, 
                 cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank))
     if args.fp16:
         model.half()
@@ -507,10 +508,13 @@ def main():
         {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.01},
         {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.0}
         ]
+    t_total = num_train_steps
+    if args.local_rank != -1:
+        t_total = t_total // torch.distributed.get_world_size()
     optimizer = BertAdam(optimizer_grouped_parameters,
                          lr=args.learning_rate,
                          warmup=args.warmup_proportion,
-                         t_total=num_train_steps)
+                         t_total=t_total)
 
     global_step = 0
     if args.do_train:
@@ -571,7 +575,7 @@ def main():
                     model.zero_grad()
                     global_step += 1
 
-    if args.do_eval:
+    if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
         eval_examples = processor.get_dev_examples(args.data_dir)
         eval_features = convert_examples_to_features(
             eval_examples, label_list, args.max_seq_length, tokenizer)
@@ -583,10 +587,8 @@ def main():
         all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
         all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)
         eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
-        if args.local_rank == -1:
-            eval_sampler = SequentialSampler(eval_data)
-        else:
-            eval_sampler = DistributedSampler(eval_data)
+        # Run prediction for full data
+        eval_sampler = SequentialSampler(eval_data)
         eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
 
         model.eval()

examples/run_squad.py

@@ -25,6 +25,7 @@ import json
 import math
 import os
 import random
+import pickle
 from tqdm import tqdm, trange
 
 import numpy as np
@@ -35,6 +36,7 @@ from torch.utils.data.distributed import DistributedSampler
 from pytorch_pretrained_bert.tokenization import whitespace_tokenize, BasicTokenizer, BertTokenizer
 from pytorch_pretrained_bert.modeling import BertForQuestionAnswering
 from pytorch_pretrained_bert.optimization import BertAdam
+from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
 
 logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s', 
                     datefmt = '%m/%d/%Y %H:%M:%S',
@@ -749,6 +751,10 @@ def main():
                         type=int,
                         default=1,
                         help="Number of updates steps to accumulate before performing a backward/update pass.")
+    parser.add_argument("--do_lower_case",
+                        default=True,
+                        action='store_true',
+                        help="Whether to lower case the input text. True for uncased models, False for cased models.")
     parser.add_argument("--local_rank",
                         type=int,
                         default=-1,
@@ -845,20 +851,34 @@ def main():
         {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.01},
         {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.0}
         ]
+    t_total = num_train_steps
+    if args.local_rank != -1:
+        t_total = t_total // torch.distributed.get_world_size()
     optimizer = BertAdam(optimizer_grouped_parameters,
                          lr=args.learning_rate,
                          warmup=args.warmup_proportion,
-                         t_total=num_train_steps)
+                         t_total=t_total)
 
     global_step = 0
     if args.do_train:
-        train_features = convert_examples_to_features(
-            examples=train_examples,
-            tokenizer=tokenizer,
-            max_seq_length=args.max_seq_length,
-            doc_stride=args.doc_stride,
-            max_query_length=args.max_query_length,
-            is_training=True)
+        cached_train_features_file = args.train_file+'_{0}_{1}_{2}_{3}'.format(
+            args.bert_model, str(args.max_seq_length), str(args.doc_stride), str(args.max_query_length))
+        train_features = None
+        try:
+            with open(cached_train_features_file, "rb") as reader:
+                train_features = pickle.load(reader)
+        except:
+            train_features = convert_examples_to_features(
+                examples=train_examples,
+                tokenizer=tokenizer,
+                max_seq_length=args.max_seq_length,
+                doc_stride=args.doc_stride,
+                max_query_length=args.max_query_length,
+                is_training=True)
+            if args.local_rank == -1 or torch.distributed.get_rank() == 0:
+                logger.info("  Saving train features into cached file %s", cached_train_features_file)
+                with open(cached_train_features_file, "wb") as writer:
+                    train_features = pickle.dump(train_features, writer)
         logger.info("***** Running training *****")
         logger.info("  Num orig examples = %d", len(train_examples))
         logger.info("  Num split examples = %d", len(train_features))
@@ -913,7 +933,7 @@ def main():
                     model.zero_grad()
                     global_step += 1
 
-    if args.do_predict:
+    if args.do_predict and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
         eval_examples = read_squad_examples(
             input_file=args.predict_file, is_training=False)
         eval_features = convert_examples_to_features(
@@ -934,10 +954,8 @@ def main():
         all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
         all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
         eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index)
-        if args.local_rank == -1:
-            eval_sampler = SequentialSampler(eval_data)
-        else:
-            eval_sampler = DistributedSampler(eval_data)
+        # Run prediction for full data
+        eval_sampler = SequentialSampler(eval_data)
         eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.predict_batch_size)
 
         model.eval()