Removing un-used ModelConfig class (NVIDIA#9389)

Co-authored-by: Chen Cui <chcui@nvidia.com>

nemo/collections/llm/gpt/model/base.py

@@ -8,7 +8,6 @@
 from torch.optim import Optimizer
 
 from nemo.lightning import get_vocab_size, io
-from nemo.lightning.base import ModelConfig
 from nemo.lightning.megatron_parallel import MaskedTokenLossReduction
 
 if TYPE_CHECKING:
@@ -18,7 +17,7 @@
 
 
 @dataclass
-class GPTConfig(TransformerConfig, ModelConfig):
+class GPTConfig(TransformerConfig):
     # From megatron.core.models.gpt.gpt_model.GPTModel
     fp16_lm_cross_entropy: bool = False
     parallel_output: bool = True
@@ -126,9 +125,6 @@ def training_loss_reduction(self) -> MaskedTokenLossReduction:
     def validation_loss_reduction(self) -> MaskedTokenLossReduction:
         return MaskedTokenLossReduction(validation_step=True)
 
-    def copy(self) -> "GPTModel":
-        return self.__class__(self.config, self.tokenizer)
-
 
 def gpt_data_step(dataloader_iter) -> Dict[str, torch.Tensor]:
     from megatron.core import parallel_state

nemo/lightning/base.py

@@ -1,15 +1,13 @@
 import gc
-import inspect
 import os
 from pathlib import Path
-from typing import Generic, Optional, Type, TypeVar
+from typing import Optional
 
 import torch
 import torch.distributed
-from pytorch_lightning import LightningModule, Trainer
+from pytorch_lightning import Trainer
 from torch import nn
 
-from nemo.lightning import io
 
 DEFAULT_NEMO_CACHE_HOME = Path.home() / ".cache" / "nemo"
 NEMO_CACHE_HOME = Path(os.getenv("NEMO_HOME", DEFAULT_NEMO_CACHE_HOME))
@@ -19,33 +17,6 @@
 NEMO_MODELS_CACHE = Path(os.getenv("NEMO_MODELS_CACHE", DEFAULT_NEMO_MODELS_CACHE))
 
 
-ModelT = TypeVar("ModelT", bound=LightningModule)
-
-
-class ModelConfig(Generic[ModelT], io.IOMixin):
-    def model_cls(self) -> Type[ModelT]:
-        raise NotImplementedError("Must be implemented by subclass")
-
-    @property
-    def model_type(self) -> Type[ModelT]:
-        return self.model_cls()
-
-    def init(self, *args, data=None, cpu: bool = False, **kwargs) -> ModelT:
-        model_cls = self.model_cls()
-        if data:
-            kwargs.update(data.model_kwargs())
-
-        signature = inspect.signature(model_cls.__init__)
-        filtered_kwargs = {k: v for k, v in kwargs.items() if k in signature.parameters}
-
-        model = model_cls(self, *args, **filtered_kwargs)
-
-        if not cpu:
-            model.cuda(torch.cuda.current_device())
-
-        return model
-
-
 def get_vocab_size(
     config,
     vocab_size: int,