Exllamav3: small sampler fixes

modules/exllamav3.py

@@ -6,7 +6,9 @@ import torch
 from exllamav3 import Cache, Config, Generator, Model, Tokenizer
 from exllamav3.cache import CacheLayer_fp16, CacheLayer_quant
 from exllamav3.generator import Job
-from exllamav3.generator.sampler import (
+
+from modules import shared
+from modules.exllamav3_custom_sampler import (
     CustomSampler,
     SS_Argmax,
     SS_MinP,
@@ -17,8 +19,6 @@ from exllamav3.generator.sampler import (
     SS_TopK,
     SS_TopP
 )
-
-from modules import shared
 from modules.image_utils import (
     convert_image_attachments_to_pil,
     convert_openai_messages_to_images
@@ -194,7 +194,6 @@ class Exllamav3Model:
         # Process images and modify prompt (ExLlamaV3-specific)
         prompt, image_embeddings = self._process_images_for_generation(prompt, state)
 
-        # -- Manually build and sort the sampler stack --
         # Greedy decoding is a special case
         if state['temperature'] == 0:
             sampler = CustomSampler([SS_Argmax()])
@@ -205,7 +204,7 @@ class Exllamav3Model:
             # Penalties
             penalty_range = state['repetition_penalty_range']
             if penalty_range <= 0:
-                penalty_range = -1 # ExllamaV3 uses -1 for whole context
+                penalty_range = int(10e7)  # Use large number for "full context"
             rep_decay = 0 # Not a configurable parameter
 
             # Add penalty samplers if they are active
@@ -222,7 +221,7 @@ class Exllamav3Model:
             if state['min_p'] > 0.0:
                 unordered_samplers.append(SS_MinP(state['min_p']))
 
-            # Temperature
+            # Temperature (SS_NoOp is returned if temp is 1.0)
             unordered_samplers.append(SS_Temperature(state['temperature']))
 
             # 2. Define the mapping from class names to the priority list keys
@@ -246,7 +245,7 @@ class Exllamav3Model:
             def custom_sort_key(sampler_obj):
                 class_name = sampler_obj.__class__.__name__
                 nickname = class_name_to_nickname.get(class_name)
-                if nickname in sampler_priority:
+                if nickname and nickname in sampler_priority:
                     return sampler_priority.index(nickname)
                 return -1
 
@@ -255,7 +254,6 @@ class Exllamav3Model:
             # 5. Add the final sampling stage and build the sampler
             ordered_samplers.append(SS_Sample())
             sampler = CustomSampler(ordered_samplers)
-        # -- End of sampler building --
 
         # Encode prompt with embeddings (ExLlamaV3-specific)
         input_ids = self.tokenizer.encode(