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scripts/nlp_language_modeling/convert_nemo_mistral_7b_to_hf.py

@@ -26,28 +26,19 @@
 import torch
 import torch.nn
 from pytorch_lightning.trainer.trainer import Trainer
-from transformers import AutoModelForCausalLM
+from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
 
 from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
 from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy
 from nemo.utils import logging
-from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
 
 
 def get_args():
     parser = ArgumentParser()
-    parser.add_argument(
-        "--in-file", type=str, default=None, required=True, help="Path to NeMo Mistral-7B checkpoint"
-    )
-    parser.add_argument(
-        "--out-file", type=str, default=None, required=True, help="Path to output HF checkpoint."
-    )
-    parser.add_argument(
-        '--hf-model-name', type=str, default="mistralai/Mistral-7B-v0.1", help="Name of HF checkpoint"
-    )
-    parser.add_argument(
-        "--precision", type=str, default="32", help="Model precision"
-    )
+    parser.add_argument("--in-file", type=str, default=None, required=True, help="Path to NeMo Mistral-7B checkpoint")
+    parser.add_argument("--out-file", type=str, default=None, required=True, help="Path to output HF checkpoint.")
+    parser.add_argument('--hf-model-name', type=str, default="mistralai/Mistral-7B-v0.1", help="Name of HF checkpoint")
+    parser.add_argument("--precision", type=str, default="32", help="Model precision")
     args = parser.parse_args()
     return args
 
@@ -106,7 +97,6 @@ def convert(in_file, precision=None) -> None:
     head_size = hidden_size // head_num
     num_layers = nemo_config.num_layers
 
-
     if precision is None:
         precision = model.cfg.precision
     if precision in [32, "32"]:
@@ -226,6 +216,7 @@ def convert(in_file, precision=None) -> None:
     state_dict[hf_output_layer_weight_name] = param_to_weights(ckpt[output_layer_base_name])
     return state_dict, nemo_config
 
+
 if __name__ == '__main__':
     args = get_args()
     hf_state_dict, nemo_config = convert(args.in_file, args.precision)