Fix legacy model loading

aiserver.py

@@ -11,6 +11,8 @@ from enum import Enum
 import random
 import shutil
 import eventlet
+
+from modeling.inference_model import SuperLegacyModelError
 eventlet.monkey_patch(all=True, thread=False, os=False)
 import os, inspect
 os.system("")
@@ -1942,24 +1944,31 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
             except:
                 pass
 
-        if koboldai_vars.model_type == "gpt2":
+        try:
-            from modeling.inference_models.legacy_gpt2_hf import CustomGPT2HFTorchInferenceModel
-            model = CustomGPT2HFTorchInferenceModel(
-                koboldai_vars.model,
-                lazy_load=koboldai_vars.lazy_load,
-                low_mem=args.lowmem
-            )
-        else:
             from modeling.inference_models.generic_hf_torch import GenericHFTorchInferenceModel
             model = GenericHFTorchInferenceModel(
                 koboldai_vars.model,
                 lazy_load=koboldai_vars.lazy_load,
                 low_mem=args.lowmem
             )
+
             model.load(
                 save_model=not (args.colab or args.cacheonly) or args.savemodel,
                 initial_load=initial_load,
             )
+        except SuperLegacyModelError:
+            from modeling.inference_models.legacy_gpt2_hf import CustomGPT2HFTorchInferenceModel
+            model = CustomGPT2HFTorchInferenceModel(
+                koboldai_vars.model,
+                lazy_load=koboldai_vars.lazy_load,
+                low_mem=args.lowmem
+            )
+
+            model.load(
+                save_model=not (args.colab or args.cacheonly) or args.savemodel,
+                initial_load=initial_load,
+            )
+
         logger.info(f"Pipeline created: {koboldai_vars.model}")
     else:
         # TPU

modeling/inference_model.py

@@ -17,6 +17,9 @@ from modeling import logits_processors
 
 import utils
 
+class SuperLegacyModelError(RuntimeError):
+    pass
+
 # We only want to use logit manipulations and such on our core text model
 class use_core_manipulations:
     """Use in a `with` block to patch functions for core story model sampling."""

modeling/inference_models/generic_hf_torch.py

@@ -7,6 +7,7 @@ import shutil
 from typing import Union
 
 from transformers import AutoModelForCausalLM, GPTNeoForCausalLM
+from modeling.inference_model import SuperLegacyModelError
 
 import utils
 import modeling.lazy_loader as lazy_loader
@@ -81,7 +82,12 @@ class GenericHFTorchInferenceModel(HFTorchInferenceModel):
                     metamodel = AutoModelForCausalLM.from_config(self.model_config)
                 except Exception as e:
                     logger.error(f"Fell back to neo for metamodel due to {e}")
+                    try:
                         metamodel = GPTNeoForCausalLM.from_config(self.model_config)
+                    except Exception as e:
+                        logger.error(f"Falling back again due to {e}")
+                        raise SuperLegacyModelError
+
                 utils.layers_module_names = utils.get_layers_module_names(metamodel)
                 utils.module_names = list(metamodel.state_dict().keys())
                 utils.named_buffers = list(metamodel.named_buffers(recurse=True))

modeling/inference_models/legacy_gpt2_hf.py

@@ -19,6 +19,7 @@ class CustomGPT2HFTorchInferenceModel(HFTorchInferenceModel):
         for possible_config_path in [
             utils.koboldai_vars.custmodpth,
             os.path.join("models", utils.koboldai_vars.custmodpth),
+            self.model_name
         ]:
             try:
                 with open(
@@ -36,12 +37,13 @@ class CustomGPT2HFTorchInferenceModel(HFTorchInferenceModel):
         with self._maybe_use_float16():
             try:
                 self.model = GPT2LMHeadModel.from_pretrained(
-                    utils.koboldai_vars.custmodpth,
+                    model_path,
                     revision=utils.koboldai_vars.revision,
                     cache_dir="cache",
+                    local_files_only=True
                 )
                 self.tokenizer = GPT2Tokenizer.from_pretrained(
-                    utils.koboldai_vars.custmodpth,
+                    model_path,
                     revision=utils.koboldai_vars.revision,
                     cache_dir="cache",
                 )
@@ -69,4 +71,4 @@ class CustomGPT2HFTorchInferenceModel(HFTorchInferenceModel):
         else:
             self.model = self.model.to("cpu").float()
 
-        self.patch_causal_lm()
+        self.patch_embedding()