Refactor/Pydantify main deepspeed config

deepspeed/inference/config.py

@@ -1,39 +1,12 @@
 import torch
-from deepspeed.runtime.config_utils import DeepSpeedConfigModel
+from deepspeed.runtime.config_utils import DeepSpeedConfigModel, DtypeEnum
 from deepspeed.runtime.zero.config import DeepSpeedZeroConfig
 from pydantic import Field
 from pydantic import validator
 from typing import Dict, Union
 from enum import Enum
 
 
-class DtypeEnum(Enum):
-    # The torch dtype must always be the first value (so we return torch.dtype)
-    fp16 = torch.float16, "torch.float16", "fp16", "float16", "half"
-    bf16 = torch.bfloat16, "torch.bfloat16", "bf16", "bfloat16"
-    fp32 = torch.float32, "torch.float32", "fp32", "float32", "float"
-    int8 = torch.int8, "torch.int8", "int8"
-
-    # Copied from https://stackoverflow.com/a/43210118
-    # Allows us to use multiple values for each Enum index and returns first
-    # listed value when Enum is called
-    def __new__(cls, *values):
-        obj = object.__new__(cls)
-        # first value is canonical value
-        obj._value_ = values[0]
-        for other_value in values[1:]:
-            cls._value2member_map_[other_value] = obj
-        obj._all_values = values
-        return obj
-
-    def __repr__(self):
-        return "<%s.%s: %s>" % (
-            self.__class__.__name__,
-            self._name_,
-            ", ".join([repr(v) for v in self._all_values]),
-        )
-
-
 class MoETypeEnum(str, Enum):
     residual = "residual"
     standard = "standard"