add xglm conversion script (#16305)

* add xglm conversion script

* style

* update script

src/transformers/models/xglm/convert_xglm_original_ckpt_to_trfms.py

+import argparse
+from argparse import Namespace
+
+import torch
+from torch import nn
+
+from transformers import XGLMConfig, XGLMForCausalLM
+
+
+def remove_ignore_keys_(state_dict):
+    ignore_keys = [
+        "decoder.version",
+        "decoder.output_projection.weight",
+        "_float_tensor",
+        "decoder.embed_positions._float_tensor",
+    ]
+    for k in ignore_keys:
+        state_dict.pop(k, None)
+
+
+def make_linear_from_emb(emb):
+    vocab_size, emb_size = emb.weight.shape
+    lin_layer = nn.Linear(vocab_size, emb_size, bias=False)
+    lin_layer.weight.data = emb.weight.data
+    return lin_layer
+
+
+def convert_fairseq_xglm_checkpoint_from_disk(checkpoint_path):
+    checkpoint = torch.load(checkpoint_path, map_location="cpu")
+    args = Namespace(**checkpoint["cfg"]["model"])
+    state_dict = checkpoint["model"]
+    remove_ignore_keys_(state_dict)
+    vocab_size = state_dict["decoder.embed_tokens.weight"].shape[0]
+
+    state_dict = {key.replace("decoder", "model"): val for key, val in state_dict.items()}
+
+    config = XGLMConfig(
+        vocab_size=vocab_size,
+        max_position_embeddings=args.max_target_positions,
+        num_layers=args.decoder_layers,
+        attention_heads=args.decoder_attention_heads,
+        ffn_dim=args.decoder_ffn_embed_dim,
+        d_model=args.decoder_embed_dim,
+        layerdrop=args.decoder_layerdrop,
+        dropout=args.dropout,
+        attention_dropout=args.attention_dropout,
+        activation_dropout=args.activation_dropout,
+        activation_function="gelu",
+        scale_embedding=not args.no_scale_embedding,
+        tie_word_embeddings=args.share_decoder_input_output_embed,
+    )
+
+    model = XGLMForCausalLM(config)
+    missing = model.load_state_dict(state_dict, strict=False)
+    print(missing)
+    model.lm_head = make_linear_from_emb(model.model.embed_tokens)
+
+    return model
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    # Required parameters
+    parser.add_argument("fairseq_path", type=str, help="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.")
+    args = parser.parse_args()
+    model = convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
+    model.save_pretrained(args.pytorch_dump_folder_path)