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examples/nlp/language_modeling/megatron_ckpt_to_nemo.py

@@ -42,7 +42,12 @@
 from nemo.collections.nlp.models.language_modeling.megatron_retrieval_model import MegatronRetrievalModel
 from nemo.collections.nlp.models.language_modeling.megatron_t5_model import MegatronT5Model
 from nemo.collections.nlp.models.machine_translation.megatron_nmt_model import MegatronNMTModel
-from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy, NLPSaveRestoreConnector, GradScaler, PipelineMixedPrecisionPlugin
+from nemo.collections.nlp.parts.nlp_overrides import (
+    GradScaler,
+    NLPDDPStrategy,
+    NLPSaveRestoreConnector,
+    PipelineMixedPrecisionPlugin,
+)
 from nemo.utils import AppState, logging
 from nemo.utils.distributed import initialize_distributed
 from nemo.utils.model_utils import inject_model_parallel_rank
@@ -92,7 +97,14 @@ def get_args():
     )
     parser.add_argument("--local_rank", type=int, required=False, default=os.getenv('LOCAL_RANK', -1))
     parser.add_argument("--bcp", action="store_true", help="Whether on BCP platform")
-    parser.add_argument("--precision", type=str, required=False, default='16-mixed', choices=['16-mixed', 'bf16-mixed'], help="Precision value for the trainer that matches with precision of the ckpt")
+    parser.add_argument(
+        "--precision",
+        type=str,
+        required=False,
+        default='16-mixed',
+        choices=['16-mixed', 'bf16-mixed'],
+        help="Precision value for the trainer that matches with precision of the ckpt",
+    )
 
     args = parser.parse_args()
     return args
@@ -110,11 +122,18 @@ def convert(local_rank, rank, world_size, args):
     if args.model_type == 'gpt':
         strategy = NLPDDPStrategy()
 
-    cfg = {'trainer':{'devices':args.gpus_per_node, 'num_nodes':num_nodes, 'accelerator':'gpu', 'precision':args.precision},
-           'model':{'native_amp_init_scale':2 ** 32, 'native_amp_growth_interval':1000, 'hysteresis':2}}
+    cfg = {
+        'trainer': {
+            'devices': args.gpus_per_node,
+            'num_nodes': num_nodes,
+            'accelerator': 'gpu',
+            'precision': args.precision,
+        },
+        'model': {'native_amp_init_scale': 2 ** 32, 'native_amp_growth_interval': 1000, 'hysteresis': 2},
+    }
     cfg = OmegaConf.create(cfg)
 
-    scaler=None
+    scaler = None
     # If FP16 create a GradScaler as the build_model_parallel_config of MegatronBaseModel expects it
     if cfg.trainer.precision == '16-mixed':
         scaler = GradScaler(