Delete convert_to_hf.py

convert_to_hf.py

@@ -1,89 +0,0 @@
-import argparse
-import logging
-import os
-
-import torch
-
-from hf_molmo.config_molmo import MolmoConfig
-from hf_molmo.image_preprocessing_molmo import MolmoImageProcessor
-from hf_molmo.modelling_molmo import MOLMoForCausalLM
-from hf_molmo.preprocessing_molmo import MolmoProcessor
-from olmo import ModelConfig
-from olmo.mm_data.data_utils import build_tokenizer
-
-logger = logging.getLogger(__name__)
-
-
-def write_config(checkpoint_dir: str, output_dir: str):
-    # save config as HF config
-
-    logger.info(f"Loading checkpoint from {checkpoint_dir}")
-
-    config_path = os.path.join(checkpoint_dir, "config.yaml")
-    model_config = ModelConfig.load(config_path, key="model")
-    config_kwargs = model_config.asdict()
-    config_kwargs["use_cache"] = True
-    config_kwargs["vit_load_path"] = None
-    config_kwargs["llm_load_path"] = None
-    config = MolmoConfig(
-        vocab_size=model_config.vocab_size,
-        embedding_size=model_config.embedding_size,
-        hidden_size=model_config.d_model,
-        intermediate_size=model_config.mlp_hidden_size,
-        num_hidden_layers=model_config.n_layers,
-        num_attention_heads=model_config.n_heads,
-        num_key_value_heads=model_config.n_kv_heads,
-        max_position_embeddings=model_config.max_position_embeddings or model_config.max_sequence_length,
-        initializer_range=model_config.initializer_range,
-        use_cache=True,
-        layer_norm_eps=model_config.layer_norm_eps,
-        rope_theta=model_config.rope_theta,
-        clip_qkv=model_config.clip_qkv,
-        qkv_bias=model_config.qkv_bias,
-        weight_tying=model_config.weight_tying,
-        use_position_ids=True,
-        tie_word_embeddings=False
-    )
-
-    logger.info(f"Saving HF-compatible config to {os.path.join(checkpoint_dir, 'config.json')}")
-    config.save_pretrained(output_dir)
-
-    preprocessor = MolmoProcessor(
-        MolmoImageProcessor(
-            max_crops=model_config.max_crops
-        ),  # FIXME now just assumes everything if fixed
-        build_tokenizer(model_config.tokenizer.identifier.split("m:")[1]).tokenizer
-    )
-    preprocessor.save_pretrained(output_dir)
-
-
-def write_model(checkpoint_dir: str, output_dir: str, ignore_olmo_compatibility: bool = False):
-    # For device_map = "auto", etc. the models are loaded in a way that start_prefix is not computed correctly.
-    # So, we explicitly store the model with the expected prefix.
-    old_model_path = os.path.join(checkpoint_dir, "model.pt")
-    new_model_path = os.path.join(output_dir, "pytorch_model.bin")
-
-    state_dict = torch.load(old_model_path)
-    new_state_dict = {f"{MOLMoForCausalLM.base_model_prefix}.{key}": val for key, val in state_dict.items()}
-    torch.save(new_state_dict, new_model_path)
-
-
-def convert_checkpoint(checkpoint_dir: str, output_dir: str):
-    os.makedirs(output_dir, exist_ok=True)
-    write_config(checkpoint_dir, output_dir)
-    write_model(checkpoint_dir, output_dir)
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Adds a config.json to the checkpoint directory, and creates pytorch_model.bin, "
-                    "making it easier to load weights as HF models."
-    )
-    parser.add_argument("checkpoint_dir")
-    parser.add_argument("output_dir")
-    args = parser.parse_args()
-    convert_checkpoint(args.checkpoint_dir, args.output_dir)
-
-
-if __name__ == "__main__":
-    main()