inference: remove unused _validate_args function

deepspeed/inference/engine.py

@@ -84,7 +84,6 @@ def __init__(self, model, config):
         self.mp_group = config.tensor_parallel.tp_group
         self.mpu = config.tensor_parallel.mpu
 
-        #self._validate_args(self.mpu, config.replace_with_kernel_inject)
         self.quantize_merge_count = 1
         self.quantization_scales = None
 
@@ -308,29 +307,6 @@ def _init_quantization_setting(self, quantization_setting):
             f"mlp_extra_grouping = {self.mlp_extra_grouping}, "
             f"quantize_groups = {self.quantize_groups}", [0])
 
-    # TODO: remove this function and add this functionality to pydantic config checking
-    def _validate_args(self, mpu, replace_with_kernel_inject):
-        # TODO: to support SD pipeline we need to avoid this check for now
-        if replace_with_kernel_inject and not isinstance(self.module, Module):
-            raise ValueError(f"model must be a torch.nn.Module, got {type(self.module)}")
-        if not isinstance(self._config.tensor_parallel.tp_size, int) or self._config.tensor_parallel.tp_size < 1:
-            raise ValueError(f"mp_size must be an int >= 1, got {self._config.tensor_parallel.tp_size}")
-
-        if mpu:
-            methods = ["get_model_parallel_group", "get_data_parallel_group"]
-            for method in methods:
-                if not hasattr(mpu, method):
-                    raise ValueError(f"mpu is missing {method}")
-        if self._config.checkpoint is not None and not isinstance(self._config.checkpoint, (str, dict)):
-            raise ValueError(f"checkpoint must be None, str or dict, got {type(self._config.checkpoint)}")
-
-        supported_dtypes = [None, torch.half, torch.int8, torch.float]
-        if self._config.dtype not in supported_dtypes:
-            raise ValueError(f"{self._config.dtype} not supported, valid dtype: {supported_dtypes}")
-
-        if self.injection_dict is not None and not isinstance(self.injection_dict, dict):
-            raise ValueError(f"injection_dict must be None or a dict, got: {self.injection_dict}")
-
     def load_model_with_checkpoint(self, r_module):
         self.mp_replace = ReplaceWithTensorSlicing(
             mp_group=self.mp_group, mp_size=self._config.tensor_parallel.tp_size)  #, out_dim=0, in_dim=1)