diff --git a/server/text_generation_server/utils/tokens.py b/server/text_generation_server/utils/tokens.py
index 789e2e85..ae14b337 100644
--- a/server/text_generation_server/utils/tokens.py
+++ b/server/text_generation_server/utils/tokens.py
@@ -29,7 +29,7 @@ class Sampling:
         q = torch.empty_like(probs).exponential_(1, generator=self.generator)
         torch.div(probs, q, out=q)
 
-        return torch.argmax(q, dim=-1, keepdim=True)
+        return q.argmax()
 
 
 class Greedy:
@@ -107,36 +107,36 @@ class NextTokenChooser:
         seed=0,
         device="cpu",
     ):
-        self.watermark_warper = (
+        self.watermark_processor = (
             WatermarkLogitsProcessor(device=device) if watermark else None
         )
-        self.repetition_warper = (
+        self.repetition_processor = (
             RepetitionPenaltyLogitsProcessor(penalty=repetition_penalty)
             if repetition_penalty
             else None
         )
 
-        sampling = (
-            do_sample
-            or (temperature is not None and temperature != 1.0)
+        has_warpers = (
+            (temperature is not None and temperature != 1.0)
             or (top_k is not None and top_k != 0)
             or (top_p is not None and top_p < 1.0)
             or (typical_p is not None and typical_p < 1.0)
         )
-        if sampling:
-            self.choice = Sampling(seed, device)
+        if has_warpers:
             self.static_warper = static_warper(
                 temperature=temperature, top_k=top_k, top_p=top_p, typical_p=typical_p
             )
         else:
-            self.choice = Greedy()
             self.static_warper = None
 
+        sampling = do_sample or has_warpers
+        self.choice = Sampling(seed, device) if sampling else Greedy()
+
     def __call__(self, input_ids, scores):
-        if self.watermark_warper:
-            scores = self.watermark_warper(input_ids, scores)
-        if self.repetition_warper:
-            scores = self.repetition_warper(input_ids, scores)
+        if self.watermark_processor:
+            scores = self.watermark_processor(input_ids, scores)
+        if self.repetition_processor:
+            scores = self.repetition_processor(input_ids, scores)
 
         if self.static_warper is None:
             next_logprob = torch.log_softmax(scores, -1)