add distilbert + update run_xnli wrt run_glue

examples/run_xnli.py

@@ -13,7 +13,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-""" Finetuning multi-lingual models on XNLI (Bert, XLM).
+""" Finetuning multi-lingual models on XNLI (Bert, DistilBERT, XLM).
     Adapted from `examples/run_glue.py`"""
 
 from __future__ import absolute_import, division, print_function
@@ -42,7 +42,7 @@ from transformers import (WEIGHTS_NAME,
                           XLMConfig, XLMForSequenceClassification, XLMTokenizer,
                           DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer)
 
-from transformers import AdamW, WarmupLinearSchedule
+from transformers import AdamW, get_linear_schedule_with_warmup
 
 from transformers import xnli_compute_metrics as compute_metrics
 from transformers import xnli_output_modes as output_modes
@@ -52,12 +52,12 @@ from transformers import glue_convert_examples_to_features as convert_examples_t
 
 logger = logging.getLogger(__name__)
 
-ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, XLMConfig)), ())
+ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, DistilBertConfig, XLMConfig)), ())
 
 MODEL_CLASSES = {
     'bert': (BertConfig, BertForSequenceClassification, BertTokenizer),
     'xlm': (XLMConfig, XLMForSequenceClassification, XLMTokenizer),
-    # 'distilbert': (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer)
+    'distilbert': (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer)
 }
 
 
@@ -91,7 +91,7 @@ def train(args, train_dataset, model, tokenizer):
         {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
         ]
     optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
-    scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
+    scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
     if args.fp16:
         try:
             from apex import amp
@@ -149,7 +149,7 @@ def train(args, train_dataset, model, tokenizer):
                 loss.backward()
 
             tr_loss += loss.item()
-            if (step + 1) % args.gradient_accumulation_steps == 0 and not args.tpu:
+            if (step + 1) % args.gradient_accumulation_steps == 0:
                 if args.fp16:
                     torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
                 else:
@@ -180,11 +180,6 @@ def train(args, train_dataset, model, tokenizer):
                     torch.save(args, os.path.join(output_dir, 'training_args.bin'))
                     logger.info("Saving model checkpoint to %s", output_dir)
 
-            if args.tpu:
-                args.xla_model.optimizer_step(optimizer, barrier=True)
-                model.zero_grad()
-                global_step += 1
-
             if args.max_steps > 0 and global_step > args.max_steps:
                 epoch_iterator.close()
                 break
@@ -214,6 +209,10 @@ def evaluate(args, model, tokenizer, prefix=""):
         eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
         eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
 
+        # multi-gpu eval
+        if args.n_gpu > 1:
+            model = torch.nn.DataParallel(model)
+
         # Eval!
         logger.info("***** Running evaluation {} *****".format(prefix))
         logger.info("  Num examples = %d", len(eval_dataset))
@@ -383,15 +382,6 @@ def main():
     parser.add_argument('--seed', type=int, default=42,
                         help="random seed for initialization")
 
-    parser.add_argument('--tpu', action='store_true',
-                        help="Whether to run on the TPU defined in the environment variables")
-    parser.add_argument('--tpu_ip_address', type=str, default='',
-                        help="TPU IP address if none are set in the environment variables")
-    parser.add_argument('--tpu_name', type=str, default='',
-                        help="TPU name if none are set in the environment variables")
-    parser.add_argument('--xrt_tpu_config', type=str, default='',
-                        help="XRT TPU config if none are set in the environment variables")
-
     parser.add_argument('--fp16', action='store_true',
                         help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit")
     parser.add_argument('--fp16_opt_level', type=str, default='O1',
@@ -425,23 +415,6 @@ def main():
         args.n_gpu = 1
     args.device = device
 
-    if args.tpu:
-        if args.tpu_ip_address:
-            os.environ["TPU_IP_ADDRESS"] = args.tpu_ip_address
-        if args.tpu_name:
-            os.environ["TPU_NAME"] = args.tpu_name
-        if args.xrt_tpu_config:
-            os.environ["XRT_TPU_CONFIG"] = args.xrt_tpu_config
-
-        assert "TPU_IP_ADDRESS" in os.environ
-        assert "TPU_NAME" in os.environ
-        assert "XRT_TPU_CONFIG" in os.environ
-
-        import torch_xla
-        import torch_xla.core.xla_model as xm
-        args.device = xm.xla_device()
-        args.xla_model = xm
-
     # Setup logging
     logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
                         datefmt = '%m/%d/%Y %H:%M:%S',
@@ -495,7 +468,7 @@ def main():
 
 
     # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
-    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0) and not args.tpu:
+    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
         # Create output directory if needed
         if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
             os.makedirs(args.output_dir)
@@ -512,7 +485,7 @@ def main():
 
         # Load a trained model and vocabulary that you have fine-tuned
         model = model_class.from_pretrained(args.output_dir)
-        tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
+        tokenizer = tokenizer_class.from_pretrained(args.output_dir)
         model.to(args.device)