Work on mtj

aiserver.py

@@ -4841,23 +4841,25 @@ def calcsubmit(txt):
         # Send it!
         ikrequest(subtxt)
 
-def __debug(text):
-    print(f"[DBG] {text}")
+def __debug(*args):
+    print("[DBG] ", *args)
 
 def core_generate(text: list, min: int, max: int, found_entries: set):
     # This generation function is tangled with koboldai_vars intentionally. It
     # is meant for the story and nothing else.
 
-    if koboldai_vars.full_determinism:
-        torch.manual_seed(koboldai_vars.seed)
+    if koboldai_vars.is_model_torch():
+        # Torch stuff
+        if koboldai_vars.full_determinism:
+            torch.manual_seed(koboldai_vars.seed)
 
-    gen_in = torch.tensor(text, dtype=torch.long)[None]
-    if koboldai_vars.sp is not None:
-        soft_tokens = torch.arange(
-            model.config.vocab_size,
-            model.config.vocab_size + koboldai_vars.sp.shape[0],
-        )
-        gen_in = torch.cat((soft_tokens[None], gen_in), dim=-1)
+        gen_in = torch.tensor(text, dtype=torch.long)[None]
+        if koboldai_vars.sp is not None:
+            soft_tokens = torch.arange(
+                model.config.vocab_size,
+                model.config.vocab_size + koboldai_vars.sp.shape[0],
+            )
+            gen_in = torch.cat((soft_tokens[None], gen_in), dim=-1)
 
     assert gen_in.shape[-1] + koboldai_vars.genamt <= koboldai_vars.max_length
 

koboldai_settings.py

@@ -287,6 +287,15 @@ class koboldai_vars(object):
         
         self.context = context
         return tokens, used_tokens, used_tokens+self.genamt
+
+    def is_model_torch(self) -> bool:
+        if self.use_colab_tpu:
+            return False
+
+        if self.model in ["Colab", "API", "CLUSTER", "ReadOnly", "OAI"]:
+            return False
+
+        return True
     
     def __setattr__(self, name, value):
         if name[0] == "_" or name == "tokenizer":
@@ -443,7 +452,7 @@ class model_settings(settings):
         self.uid_presets = []
         self.default_preset = {}
         self.cluster_requested_models = [] # The models which we allow to generate during cluster mode
-        
+
         
     #dummy class to eat the tqdm output
     class ignore_tqdm(object):