[V1][Sampler] Don't apply temp for greedy-only (#13311)

Signed-off-by: Nick Hill <nhill@redhat.com>

vllm/v1/sample/sampler.py

@@ -41,8 +41,6 @@ class Sampler(nn.Module):
         logits = self.apply_logits_bias(logits, sampling_metadata)
         # Apply penalties (e.g., min_tokens, freq_penalties).
         logits = self.apply_penalties(logits, sampling_metadata)
-        # Apply temperature.
-        logits = self.apply_temperature(logits, sampling_metadata.temperature)
         # Sample the next token.
         sampled = self.sample(logits, sampling_metadata)
 
@@ -82,9 +80,21 @@ class Sampler(nn.Module):
     ) -> torch.Tensor:
         assert not (sampling_metadata.all_greedy
                     and sampling_metadata.all_random)
-        if sampling_metadata.all_greedy:
-            return self.greedy_sample(logits)
+        if sampling_metadata.all_random:
+            greedy_sampled = None
+        else:
+            greedy_sampled = self.greedy_sample(logits)
+            if sampling_metadata.all_greedy:
+                return greedy_sampled
 
+        # Apply temperature.
+        logits = self.apply_temperature(logits, sampling_metadata.temperature)
+
+        # Apply min_p.
+        if not sampling_metadata.no_min_p:
+            logits = self.apply_min_p(logits, sampling_metadata.min_p)
+
+        # Apply top_k and/or top_p.
         random_sampled = self.topk_topp_sampler(
             logits,
             sampling_metadata.generators,
@@ -94,13 +104,9 @@ class Sampler(nn.Module):
             sampling_metadata.top_p,
         )
 
-        if not sampling_metadata.no_min_p:
-            logits = self.apply_min_p(logits, sampling_metadata.min_p)
-
-        if sampling_metadata.all_random:
+        if greedy_sampled is None:
             return random_sampled
 
-        greedy_sampled = self.greedy_sample(logits)
         sampled = torch.where(
             sampling_metadata.temperature < _SAMPLING_EPS,
             greedy_sampled,