Self-contained sampler patch (Don't merge)

Completely untested 3:00 AM code; beware! I will test and add more
documentation tomorrow.

modeling/inference_models/basic_hf/class.py

@@ -6,16 +6,24 @@ import time
 from typing import List, Optional, Union
 
 import torch
+import transformers
+from transformers import LogitsProcessorList
 from transformers.models.auto.modeling_auto import AutoModelForCausalLM
 
 import utils
 from logger import logger
-from modeling.inference_model import GenerationResult, GenerationSettings
+from modeling import warpers
+from modeling.inference_model import (
+    GenerationResult,
+    GenerationSettings,
+    use_core_manipulations,
+)
 from modeling.inference_models.hf import HFInferenceModel
 
 model_backend_name = "Basic Huggingface"
 model_backend_type = "Huggingface"
 
+
 class model_backend(HFInferenceModel):
     def __init__(self) -> None:
         super().__init__()
@@ -25,7 +33,7 @@ class model_backend(HFInferenceModel):
         # them in subclasses?
         self.hf_torch = True
         self.nobreakmodel = True
-    
+
     def _load(self, save_model: bool, initial_load: bool) -> None:
         utils.koboldai_vars.allowsp = False
 
@@ -43,16 +51,77 @@ class model_backend(HFInferenceModel):
 
         self.init_model_config()
 
-        self.model = AutoModelForCausalLM.from_pretrained(self.get_local_model_path(), low_cpu_mem_usage=True)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            self.get_local_model_path(), low_cpu_mem_usage=True
+        )
 
         if self.usegpu:
             self.model = self.model.to("cuda")
 
         self.tokenizer = self._get_tokenizer(self.get_local_model_path())
 
+        # Patch sampler to use KAI samplers
+        def _patched_get_logits_processor(*args, **kwargs) -> LogitsProcessorList:
+            processors = _patched_get_logits_processor.original(*args, **kwargs)
+            return processors
+
+        use_core_manipulations.get_logits_processor = _patched_get_logits_processor
+        _patched_get_logits_processor.original = (
+            transformers.GenerationMixin._get_logits_processor
+        )
+
+        class KoboldLogitsWarperList(LogitsProcessorList):
+            def __call__(
+                _self,  # Unused
+                input_ids: torch.LongTensor,
+                scores: torch.FloatTensor,
+                *args,
+                **kwargs,
+            ):
+                scores = self._apply_warpers(scores=scores, input_ids=input_ids)
+
+                for processor in self.logits_processors:
+                    scores = processor(self, scores=scores, input_ids=input_ids)
+                    assert (
+                        scores is not None
+                    ), f"Scores are None; processor '{processor}' is to blame"
+                return scores
+
+        def new_sample(self, *args, **kwargs):
+            assert kwargs.pop("logits_warper", None) is not None
+            kwargs["logits_warper"] = lambda: KoboldLogitsWarperList()
+
+            if utils.koboldai_vars.newlinemode in ["s", "ns"]:
+                kwargs["eos_token_id"] = -1
+                kwargs.setdefault("pad_token_id", 2)
+
+            return new_sample.old_sample(self, *args, **kwargs)
+
+        new_sample.old_sample = transformers.GenerationMixin.sample
+        use_core_manipulations.sample = new_sample
+
         self.model.kai_model = self
         utils.koboldai_vars.modeldim = self.model.get_input_embeddings().embedding_dim
 
+    def _apply_warpers(
+        self, scores: torch.Tensor, input_ids: torch.Tensor
+    ) -> torch.Tensor:
+        warpers.update_settings()
+
+        for sid in utils.koboldai_vars.sampler_order:
+            warper = warpers.Warper.from_id(sid)
+
+            if not warper.value_is_valid():
+                continue
+
+            if warper == warpers.RepetitionPenalty:
+                # Rep pen needs more data than other samplers
+                scores = warper.torch(scores, input_ids=input_ids)
+            else:
+                scores = warper.torch(scores)
+
+            assert scores is not None, f"Scores are None; warper '{warper}' is to blame"
+        return scores
 
     def _raw_generate(
         self,
@@ -102,4 +171,4 @@ class model_backend(HFInferenceModel):
             prompt=prompt_tokens,
             is_whole_generation=False,
             output_includes_prompt=True,
-        )
+        )