Trainer (#3800)

* doc

* [tests] Add sample files for a regression task

* [HUGE] Trainer

* Feedback from @sshleifer

* Feedback from @thomwolf + logging tweak

* [file_utils] when downloading concurrently, get_from_cache will use the cached file for subsequent processes

* [glue] Use default max_seq_length of 128 like before

* [glue] move DataTrainingArguments around

* [ner] Change interface of InputExample, and align run_{tf,pl}

* Re-align the pl scripts a little bit

* ner

* [ner] Add integration test

* Fix language_modeling with API tweak

* [ci] Tweak loss target

* Don't break console output

* amp.initialize: model must be on right device before

* [multiple-choice] update for Trainer

* Re-align to 827d6d6ef0

examples/transformer_base.py

@@ -8,7 +8,6 @@ import pytorch_lightning as pl
 import torch
 
 from transformers import (
-    ALL_PRETRAINED_MODEL_ARCHIVE_MAP,
     AdamW,
     AutoConfig,
     AutoModel,
@@ -20,15 +19,11 @@ from transformers import (
     AutoTokenizer,
     get_linear_schedule_with_warmup,
 )
-from transformers.modeling_auto import MODEL_MAPPING
 
 
 logger = logging.getLogger(__name__)
 
 
-ALL_MODELS = tuple(ALL_PRETRAINED_MODEL_ARCHIVE_MAP)
-MODEL_CLASSES = tuple(m.model_type for m in MODEL_MAPPING)
-
 MODEL_MODES = {
     "base": AutoModel,
     "sequence-classification": AutoModelForSequenceClassification,
@@ -51,28 +46,25 @@ class BaseTransformer(pl.LightningModule):
     def __init__(self, hparams: argparse.Namespace, num_labels=None, mode="base", **config_kwargs):
         "Initialize a model."
 
-        super(BaseTransformer, self).__init__()
+        super().__init__()
         self.hparams = hparams
         cache_dir = self.hparams.cache_dir if self.hparams.cache_dir else None
-        self.hparams.model_type = self.hparams.model_type.lower()
-        config = AutoConfig.from_pretrained(
+        self.config = AutoConfig.from_pretrained(
             self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path,
             **({"num_labels": num_labels} if num_labels is not None else {}),
             cache_dir=cache_dir,
             **config_kwargs,
         )
-        tokenizer = AutoTokenizer.from_pretrained(
+        self.tokenizer = AutoTokenizer.from_pretrained(
             self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path,
-            do_lower_case=self.hparams.do_lower_case,
             cache_dir=cache_dir,
         )
-        model = MODEL_MODES[mode].from_pretrained(
+        self.model = MODEL_MODES[mode].from_pretrained(
             self.hparams.model_name_or_path,
             from_tf=bool(".ckpt" in self.hparams.model_name_or_path),
-            config=config,
+            config=self.config,
             cache_dir=cache_dir,
         )
-        self.config, self.tokenizer, self.model = config, tokenizer, model
 
     def is_logger(self):
         return self.trainer.proc_rank <= 0
@@ -148,19 +140,12 @@ class BaseTransformer(pl.LightningModule):
 
     @staticmethod
     def add_model_specific_args(parser, root_dir):
-        parser.add_argument(
-            "--model_type",
-            default=None,
-            type=str,
-            required=True,
-            help="Model type selected in the list: " + ", ".join(MODEL_CLASSES),
-        )
         parser.add_argument(
             "--model_name_or_path",
             default=None,
             type=str,
             required=True,
-            help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS),
+            help="Path to pretrained model or model identifier from huggingface.co/models",
         )
         parser.add_argument(
             "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name"
@@ -177,9 +162,6 @@ class BaseTransformer(pl.LightningModule):
             type=str,
             help="Where do you want to store the pre-trained models downloaded from s3",
         )
-        parser.add_argument(
-            "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model."
-        )
         parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
         parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
         parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
@@ -252,8 +234,6 @@ def add_generic_args(parser, root_dir):
         help="Number of updates steps to accumulate before performing a backward/update pass.",
     )
 
-    parser.add_argument("--server_ip", type=str, default="", help="For distant debugging.")
-    parser.add_argument("--server_port", type=str, default="", help="For distant debugging.")
     parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
 
 
@@ -261,15 +241,6 @@ def generic_train(model: BaseTransformer, args: argparse.Namespace):
     # init model
     set_seed(args)
 
-    # Setup distant debugging if needed
-    if args.server_ip and args.server_port:
-        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
-        import ptvsd
-
-        print("Waiting for debugger attach")
-        ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
-        ptvsd.wait_for_attach()
-
     if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train:
         raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))