add roberta option (#135)

ch06/03_bonus_imdb-classification/README.md

@@ -97,6 +97,15 @@ Test accuracy: 90.81%
 
 ---
 
+A 355M parameter encoder-style [RoBERTa](https://arxiv.org/abs/1907.11692) model, starting for the pretrained weights and only training the last transformer block plus output layers:
+
+
+```bash
+python train-bert-hf.py --bert_model roberta
+```
+
+---
+
 A scikit-learn Logistic Regression model as a basline.
 
 ```bash

ch06/03_bonus_imdb-classification/train-bert-hf.py

@@ -164,32 +164,71 @@ if __name__ == "__main__":
             "Which layers to train. Options: 'all', 'last_block', 'last_layer'."
         )
     )
+    parser.add_argument(
+        "--bert_model",
+        type=str,
+        default="distilbert",
+        help=(
+            "Which layers to train. Options: 'all', 'last_block', 'last_layer'."
+        )
+    )
     args = parser.parse_args()
 
     ###############################
     # Load model
     ###############################
-    model = AutoModelForSequenceClassification.from_pretrained(
-        "distilbert-base-uncased", num_labels=2
-    )
-    torch.manual_seed(123)
-    model.out_head = torch.nn.Linear(in_features=768, out_features=2)
 
-    if args.trainable_layers == "last_layer":
+    torch.manual_seed(123)
-        pass
+    if args.bert_model == "distilbert":
-    elif args.trainable_layers == "last_block":
+
-        for param in model.pre_classifier.parameters():
+        model = AutoModelForSequenceClassification.from_pretrained(
-            param.requires_grad = True
+            "distilbert-base-uncased", num_labels=2
-        for param in model.distilbert.transformer.layer[-1].parameters():
+        )
-            param.requires_grad = True
+        model.out_head = torch.nn.Linear(in_features=768, out_features=2)
-    elif args.trainable_layers == "all":
+
-        for param in model.parameters():
+        if args.trainable_layers == "last_layer":
-            param.requires_grad = True
+            pass
+        elif args.trainable_layers == "last_block":
+            for param in model.pre_classifier.parameters():
+                param.requires_grad = True
+            for param in model.distilbert.transformer.layer[-1].parameters():
+                param.requires_grad = True
+        elif args.trainable_layers == "all":
+            for param in model.parameters():
+                param.requires_grad = True
+        else:
+            raise ValueError("Invalid --trainable_layers argument.")
+
+        tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+
+    elif args.bert_model == "roberta":
+
+        model = AutoModelForSequenceClassification.from_pretrained(
+            "FacebookAI/roberta-large", num_labels=2
+        )
+        model.classifier.out_proj = torch.nn.Linear(in_features=1024, out_features=2)
+
+        if args.trainable_layers == "last_layer":
+            pass
+        elif args.trainable_layers == "last_block":
+            for param in model.classifier.parameters():
+                param.requires_grad = True
+            for param in model.roberta.encoder.layer[-1].parameters():
+                param.requires_grad = True
+        elif args.trainable_layers == "all":
+            for param in model.parameters():
+                param.requires_grad = True
+        else:
+            raise ValueError("Invalid --trainable_layers argument.")
+
+        tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-large")
+
     else:
-        raise ValueError("Invalid --trainable_layers argument.")
+        raise ValueError("Selected --bert_model not supported.")
 
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     model.to(device)
+    model.eval()
 
     ###############################
     # Instantiate dataloaders
@@ -204,7 +243,6 @@ if __name__ == "__main__":
     file_names = ["train.csv", "val.csv", "test.csv"]
     all_exist = all((base_path / file_name).exists() for file_name in file_names)
 
-    tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
     pad_token_id = tokenizer.encode(tokenizer.pad_token)
 
     train_dataset = IMDBDataset(base_path / "train.csv", max_length=256, tokenizer=tokenizer, pad_token_id=pad_token_id)