Model: Split cache creation into a common function

Unifies the switch statement across both draft and model caches.

Signed-off-by: kingbri <[email protected]>

backends/exllamav2/model.py

@@ -548,30 +548,11 @@ def progress(loaded_modules: int, total_modules: int)
             if not self.quiet:
                 logger.info("Loading draft model: " + self.draft_config.model_dir)
 
-            if self.draft_cache_mode == "Q4":
-                self.draft_cache = ExLlamaV2Cache_Q4(
-                    self.draft_model,
-                    max_seq_len=self.cache_size,
-                    lazy=True,
-                )
-            elif self.draft_cache_mode == "Q6":
-                self.draft_cache = ExLlamaV2Cache_Q6(
-                    self.draft_model,
-                    max_seq_len=self.cache_size,
-                    lazy=True,
-                )
-            elif self.draft_cache_mode == "Q8":
-                self.draft_cache = ExLlamaV2Cache_Q8(
-                    self.draft_model,
-                    max_seq_len=self.cache_size,
-                    lazy=True,
-                )
-            else:
-                self.draft_cache = ExLlamaV2Cache(
-                    self.draft_model,
-                    max_seq_len=self.cache_size,
-                    lazy=True,
-                )
+            self.draft_cache = self.create_cache(
+                cache_mode=self.draft_cache_mode,
+                autosplit=True,
+            )
+
             for value in self.draft_model.load_autosplit_gen(
                 self.draft_cache,
                 reserve_vram=autosplit_reserve,
@@ -601,34 +582,10 @@ def progress(loaded_modules: int, total_modules: int)
                 if value:
                     yield value
 
-        if self.cache_mode == "Q4":
-            self.cache = ExLlamaV2Cache_Q4(
-                self.model,
-                max_seq_len=self.cache_size,
-                lazy=self.gpu_split_auto,
-                batch_size=1,
-            )
-        elif self.cache_mode == "Q6":
-            self.cache = ExLlamaV2Cache_Q6(
-                self.model,
-                max_seq_len=self.cache_size,
-                lazy=self.gpu_split_auto,
-                batch_size=1,
-            )
-        elif self.cache_mode == "Q8":
-            self.cache = ExLlamaV2Cache_Q8(
-                self.model,
-                max_seq_len=self.cache_size,
-                lazy=self.gpu_split_auto,
-                batch_size=1,
-            )
-        else:
-            self.cache = ExLlamaV2Cache(
-                self.model,
-                max_seq_len=self.cache_size,
-                lazy=self.gpu_split_auto,
-                batch_size=1,
-            )
+        self.cache = self.create_cache(
+            cache_mode=self.cache_mode,
+            autosplit=self.gpu_split_auto,
+        )
 
         # Load model with autosplit
         if self.gpu_split_auto:
@@ -647,6 +604,37 @@ def progress(loaded_modules: int, total_modules: int)
         input_ids = torch.zeros((1, self.config.max_input_len), dtype=torch.long)
         self.model.forward(input_ids, cache=self.cache, preprocess_only=True)
 
+    def create_cache(self, cache_mode: str, autosplit: bool):
+        match cache_mode:
+            case "Q4":
+                return ExLlamaV2Cache_Q4(
+                    self.model,
+                    max_seq_len=self.cache_size,
+                    lazy=autosplit,
+                    batch_size=1,
+                )
+            case "Q6":
+                return ExLlamaV2Cache_Q6(
+                    self.model,
+                    max_seq_len=self.cache_size,
+                    lazy=self.gpu_split_auto,
+                    batch_size=1,
+                )
+            case "Q8":
+                return ExLlamaV2Cache_Q8(
+                    self.model,
+                    max_seq_len=self.cache_size,
+                    lazy=autosplit,
+                    batch_size=1,
+                )
+            case _:
+                return ExLlamaV2Cache(
+                    self.model,
+                    max_seq_len=self.cache_size,
+                    lazy=self.gpu_split_auto,
+                    batch_size=1,
+                )
+
     async def create_generator(self):
         try:
             # Don't acquire locks unless a model is loaded