Delete legacy gpt2 custom loader

modeling/inference_models/legacy_gpt2_hf.py

@@ -1,74 +0,0 @@
-from __future__ import annotations
-
-import os
-import json
-import traceback
-
-from transformers import GPT2LMHeadModel, GPT2Tokenizer
-
-import utils
-from modeling.inference_models.hf_torch import HFTorchInferenceModel
-
-
-class CustomGPT2HFTorchInferenceModel(HFTorchInferenceModel):
-    def _load(self, save_model: bool, initial_load: bool) -> None:
-        self.lazy_load = False
-
-        model_path = None
-
-        for possible_config_path in [
-            utils.koboldai_vars.custmodpth,
-            os.path.join("models", utils.koboldai_vars.custmodpth),
-            self.model_name,
-        ]:
-            try:
-                with open(
-                    os.path.join(possible_config_path, "config.json"), "r"
-                ) as file:
-                    self.model_config = json.load(file)
-                model_path = possible_config_path
-                break
-            except FileNotFoundError:
-                pass
-
-        if not model_path:
-            raise RuntimeError("Empty model_path!")
-
-        with self._maybe_use_float16():
-            try:
-                self.model = GPT2LMHeadModel.from_pretrained(
-                    model_path,
-                    revision=utils.koboldai_vars.revision,
-                    cache_dir="cache",
-                    local_files_only=True,
-                )
-                self.tokenizer = GPT2Tokenizer.from_pretrained(
-                    model_path,
-                    revision=utils.koboldai_vars.revision,
-                    cache_dir="cache",
-                )
-            except Exception as e:
-                if "out of memory" in traceback.format_exc().lower():
-                    raise RuntimeError(
-                        "One of your GPUs ran out of memory when KoboldAI tried to load your model."
-                    ) from e
-                raise e
-
-        if save_model:
-            self.model.save_pretrained(
-                self.get_local_model_path(ignore_existance=True),
-                max_shard_size="500MiB",
-            )
-            self.tokenizer.save_pretrained(
-                self.get_local_model_path(ignore_existance=True)
-            )
-
-        utils.koboldai_vars.modeldim = self.get_hidden_size()
-
-        # Is CUDA available? If so, use GPU, otherwise fall back to CPU
-        if utils.koboldai_vars.hascuda and utils.koboldai_vars.usegpu:
-            self.model = self.model.half().to(utils.koboldai_vars.gpu_device)
-        else:
-            self.model = self.model.to("cpu").float()
-
-        self.patch_embedding()