102 lines
3.6 KiB
Python
102 lines
3.6 KiB
Python
"""
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A script creating a RAG checkpoint from a generator and a question encoder checkpoints.
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"""
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import argparse
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from pathlib import Path
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from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
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def consolidate(
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model_type,
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generator_name_or_path: str,
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question_encoder_name_or_path: str,
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dest_dir: Path,
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config_name_or_path: str = None,
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generator_tokenizer_name_or_path: str = None,
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question_encoder_tokenizer_name_or_path: str = None,
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):
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if config_name_or_path is None:
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config_name_or_path = "facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"
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if generator_tokenizer_name_or_path is None:
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generator_tokenizer_name_or_path = generator_name_or_path
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if question_encoder_tokenizer_name_or_path is None:
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question_encoder_tokenizer_name_or_path = question_encoder_name_or_path
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model_class = RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration
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# Save model.
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rag_config = RagConfig.from_pretrained(config_name_or_path)
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gen_config = AutoConfig.from_pretrained(generator_name_or_path)
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question_encoder_config = AutoConfig.from_pretrained(question_encoder_name_or_path)
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rag_config.generator = gen_config
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rag_config.question_encoder = question_encoder_config
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rag_model = model_class.from_pretrained_question_encoder_generator(
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question_encoder_name_or_path, generator_name_or_path, config=rag_config
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)
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rag_model.save_pretrained(dest_dir)
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# Sanity check.
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model_class.from_pretrained(dest_dir)
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# Save tokenizers.
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gen_tokenizer = AutoTokenizer.from_pretrained(generator_tokenizer_name_or_path)
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gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/")
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question_encoder_tokenizer = AutoTokenizer.from_pretrained(question_encoder_tokenizer_name_or_path)
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question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model_type",
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choices=["rag_sequence", "rag_token"],
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required=True,
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type=str,
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help="RAG model type: rag_sequence, rag_token",
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)
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parser.add_argument("--dest", type=str, required=True, help="Path to the output checkpoint directory.")
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parser.add_argument("--generator_name_or_path", type=str, required=True, help="Generator model identifier")
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parser.add_argument(
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"--question_encoder_name_or_path", type=str, required=True, help="Question encoder model identifier"
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)
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parser.add_argument(
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"--generator_tokenizer_name_or_path",
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type=str,
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help="Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``",
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)
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parser.add_argument(
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"--question_encoder_tokenizer_name_or_path",
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type=str,
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help="Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``",
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)
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parser.add_argument(
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"--config_name_or_path",
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type=str,
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help=(
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"Identifier of the model config to use, if not provided, resolves to a base config for a given"
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" ``model_type``"
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),
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)
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args = parser.parse_args()
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dest_dir = Path(args.dest)
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dest_dir.mkdir(exist_ok=True)
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consolidate(
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args.model_type,
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args.generator_name_or_path,
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args.question_encoder_name_or_path,
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dest_dir,
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args.config_name_or_path,
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args.generator_tokenizer_name_or_path,
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args.question_encoder_tokenizer_name_or_path,
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)
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