Change soft prompt implementation to a more universal one

aiserver.py

@@ -1247,18 +1247,20 @@ def get_oai_models(key):
 
 
 # Function to patch transformers to use our soft prompt
-def patch_causallm(cls):
-    if(getattr(cls, "_koboldai_patch_causallm_patched", False)):
-        return
-    old_forward = cls.forward
-    def new_causallm_forward(self, *args, **kwargs):
-        input_ids = kwargs.get('input_ids').to(self.device)
+def patch_causallm(model):
+    from torch.nn import Embedding
+    if(getattr(Embedding, "_koboldai_patch_causallm_model", None)):
+        Embedding._koboldai_patch_causallm_model = model
+        return model
+    old_embedding_call = Embedding.__call__
+    def new_embedding_call(self, input_ids, *args, **kwargs):
+        if(Embedding._koboldai_patch_causallm_model.get_input_embeddings() is not self):
+            return old_embedding_call(self, input_ids, *args, **kwargs)
         assert input_ids is not None
-        kwargs['input_ids'] = None
         if(vars.sp is not None):
-            shifted_input_ids = input_ids - self.config.vocab_size
-        input_ids.clamp_(max=self.config.vocab_size-1)
-        inputs_embeds = self.get_input_embeddings()(input_ids)
+            shifted_input_ids = input_ids - model.config.vocab_size
+        input_ids.clamp_(max=model.config.vocab_size-1)
+        inputs_embeds = old_embedding_call(self, input_ids, *args, **kwargs)
         if(vars.sp is not None):
             vars.sp = vars.sp.to(inputs_embeds.dtype).to(inputs_embeds.device)
             inputs_embeds = torch.where(
@@ -1266,13 +1268,10 @@ def patch_causallm(cls):
                 vars.sp[shifted_input_ids.clamp(min=0)],
                 inputs_embeds,
             )
-        if(hasattr(self, "model") and hasattr(self.model, "embed_scale")):
-            inputs_embeds *= self.model.embed_scale
-        kwargs['inputs_embeds'] = inputs_embeds
-        return old_forward(self, *args, **kwargs)
-    cls.forward = new_causallm_forward
-    cls._koboldai_patch_causallm_patched = True
-    return cls
+        return inputs_embeds
+    Embedding.__call__ = new_embedding_call
+    Embedding._koboldai_patch_causallm_model = model
+    return model
 
 
 def patch_transformers():
@@ -1864,7 +1863,7 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
                 else:
                     model = model.to('cpu').float()
                     generator = model.generate
-                patch_causallm(model.__class__)
+                patch_causallm(model)
             # Use the Generic implementation
             else:
                 lowmem = maybe_low_cpu_mem_usage()
@@ -1998,7 +1997,7 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
                 if(vars.badwordsids is vars.badwordsids_default and vars.model_type not in ("gpt2", "gpt_neo", "gptj", "xglm")):
                     vars.badwordsids = [[v] for k, v in tokenizer.get_vocab().items() if any(c in k for c in "<>[]")]
 
-                patch_causallm(model.__class__)
+                patch_causallm(model)
 
                 if(vars.hascuda):
                     if(vars.usegpu):