mBART Conversion script (#6230)

src/transformers/convert_bart_original_pytorch_checkpoint_to_pytorch.py

@@ -78,19 +78,6 @@ def load_xsum_checkpoint(checkpoint_path):
     return hub_interface
 
 
-def convert_checkpoint_from_disk(checkpoint_path, **config_kwargs):
-    state_dict = torch.load(checkpoint_path, map_location="cpu")["model"]
-    remove_ignore_keys_(state_dict)
-    vocab_size = state_dict["encoder.embed_tokens.weight"].shape[0]
-    state_dict["shared.weight"] = state_dict["decoder.embed_tokens.weight"]
-    mbart_config = BartConfig(vocab_size=vocab_size, **config_kwargs)
-    model = BartForConditionalGeneration(mbart_config)
-    model.model.load_state_dict(state_dict)
-    if hasattr(model, "lm_head"):
-        model.lm_head = _make_linear_from_emb(model.model.shared)
-    return model
-
-
 @torch.no_grad()
 def convert_bart_checkpoint(checkpoint_path, pytorch_dump_folder_path, hf_checkpoint_name=None):
     """

src/transformers/convert_mbart_original_checkpoint_to_pytorch.py

+import argparse
+
+import torch
+
+from transformers import BartForConditionalGeneration, MBartConfig
+
+from .convert_bart_original_pytorch_checkpoint_to_pytorch import remove_ignore_keys_
+
+
+def convert_fairseq_mbart_checkpoint_from_disk(checkpoint_path, hf_config_path="facebook/mbart-large-en-ro"):
+    state_dict = torch.load(checkpoint_path, map_location="cpu")["model"]
+    remove_ignore_keys_(state_dict)
+    vocab_size = state_dict["encoder.embed_tokens.weight"].shape[0]
+    mbart_config = MBartConfig.from_pretrained(hf_config_path, vocab_size=vocab_size)
+    state_dict["shared.weight"] = state_dict["decoder.embed_tokens.weight"]
+    model = BartForConditionalGeneration(mbart_config)
+    model.model.load_state_dict(state_dict)
+    return model
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    # Required parameters
+    parser.add_argument(
+        "fairseq_path", type=str, help="bart.large, bart.large.cnn or a path to a model.pt on local filesystem."
+    )
+    parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
+    parser.add_argument(
+        "--hf_config",
+        default="facebook/mbart-large-cc25",
+        type=str,
+        help="Which huggingface architecture to use: bart-large-xsum",
+    )
+    args = parser.parse_args()
+    model = convert_fairseq_mbart_checkpoint_from_disk(args.fairseq_path, hf_config_path=args.hf_config)
+    model.save_pretrained(args.pytorch_dump_folder_path)