diff --git a/vllm/model_executor/layers/sampler.py b/vllm/model_executor/layers/sampler.py
index 5179b8d94ae1..a17dfcfbaf16 100644
--- a/vllm/model_executor/layers/sampler.py
+++ b/vllm/model_executor/layers/sampler.py
@@ -133,37 +133,22 @@ def _get_penalties(
     # Collect the presence and frequency penalties.
     presence_penalties: List[float] = []
     frequency_penalties: List[float] = []
-    for i, seq_group in enumerate(input_metadata.seq_groups):
+    for seq_group in input_metadata.seq_groups:
         seq_ids, sampling_params = seq_group
         p = sampling_params.presence_penalty
         f = sampling_params.frequency_penalty
-        if i < input_metadata.num_prompts:
-            # A prompt input.
-            presence_penalties.append(p)
-            frequency_penalties.append(f)
-        else:
-            # A generation token.
-            presence_penalties += [p] * len(seq_ids)
-            frequency_penalties += [f] * len(seq_ids)
+        presence_penalties += [p] * len(seq_ids)
+        frequency_penalties += [f] * len(seq_ids)
     return presence_penalties, frequency_penalties
 
 
 def _get_output_tokens(input_metadata: InputMetadata) -> List[List[int]]:
     output_tokens: List[List[int]] = []
-    for i, seq_group in enumerate(input_metadata.seq_groups):
+    for seq_group in input_metadata.seq_groups:
         seq_ids, _ = seq_group
-        if i < input_metadata.num_prompts:
-            # A prompt input.
-            # NOTE: While the prompt input usually has no output tokens,
-            # it may have output tokens in the case of recomputation.
-            seq_id = seq_ids[0]
+        for seq_id in seq_ids:
             seq_data = input_metadata.seq_data[seq_id]
             output_tokens.append(seq_data.output_token_ids)
-        else:
-            # A generation token.
-            for seq_id in seq_ids:
-                seq_data = input_metadata.seq_data[seq_id]
-                output_tokens.append(seq_data.output_token_ids)
     return output_tokens
 
 
@@ -221,7 +206,7 @@ def _apply_penalties(
 def _get_temperatures(input_metadata: InputMetadata) -> List[float]:
     # Collect the temperatures for the logits.
     temperatures: List[float] = []
-    for i, seq_group in enumerate(input_metadata.seq_groups):
+    for seq_group in input_metadata.seq_groups:
         seq_ids, sampling_params = seq_group
         temperature = sampling_params.temperature
         if temperature < _SAMPLING_EPS:
@@ -229,13 +214,7 @@ def _get_temperatures(input_metadata: InputMetadata) -> List[float]:
             # (i.e., greedy sampling or beam search).
             # Set the temperature to 1 to avoid division by zero.
             temperature = 1.0
-
-        if i < input_metadata.num_prompts:
-            # A prompt input.
-            temperatures.append(temperature)
-        else:
-            # A generation token.
-            temperatures += [temperature] * len(seq_ids)
+        temperatures += [temperature] * len(seq_ids)
     return temperatures
 
 
@@ -245,21 +224,15 @@ def _get_top_p_top_k(
 ) -> Tuple[List[float], List[int]]:
     top_ps: List[float] = []
     top_ks: List[int] = []
-    for i, seq_group in enumerate(input_metadata.seq_groups):
+    for seq_group in input_metadata.seq_groups:
         seq_ids, sampling_params = seq_group
         top_p = sampling_params.top_p
         # k should not be greater than the vocab size.
         top_k = min(sampling_params.top_k, vocab_size)
         # k=-1 means no truncation.
         top_k = vocab_size if top_k == -1 else top_k
-        if i < input_metadata.num_prompts:
-            # A prompt input.
-            top_ps.append(top_p)
-            top_ks.append(top_k)
-        else:
-            # A generation token.
-            top_ps += [top_p] * len(seq_ids)
-            top_ks += [top_k] * len(seq_ids)
+        top_ps += [top_p] * len(seq_ids)
+        top_ks += [top_k] * len(seq_ids)
     return top_ps, top_ks