[Core] Comment out unused code in sampler (#7023)

vllm/model_executor/sampling_metadata.py

@@ -13,6 +13,8 @@ from vllm.utils import (async_tensor_h2d, is_pin_memory_available,
 
 _SAMPLING_EPS = 1e-5
 _SEED_0_REPLACEMENT = 3403598558
+# Some triton sampler related code is guarded before it is ready.
+_USE_TRITON_SAMPLER = False
 
 
 @dataclass
@@ -347,11 +349,13 @@ class SamplingTensors:
         repetition_penalties: List[float] = []
         sampling_seeds: List[int] = []
         sample_indices: List[int] = []
-        prompt_best_of: List[int] = []
         do_penalties = False
         do_top_p_top_k = False
         do_min_p = False
 
+        if _USE_TRITON_SAMPLER:
+            prompt_best_of: List[int] = []
+
             # We need one base seed per Triton slice.
             seeds_to_generate = (extra_seeds_to_generate +
                                  get_num_triton_sampler_splits(vocab_size))
@@ -366,9 +370,6 @@ class SamplingTensors:
             r = sampling_params.repetition_penalty
             top_p = sampling_params.top_p
             min_p = sampling_params.min_p
-            seed = sampling_params.seed
-
-            is_greedy = sampling_params.sampling_type == SamplingType.GREEDY
 
             # k should not be greater than the vocab size.
             top_k = min(sampling_params.top_k, vocab_size)
@@ -389,8 +390,7 @@ class SamplingTensors:
                 do_penalties = True
 
             is_prompt = seq_group.is_prompt
-            if (seq_group.is_prompt
-                    and sampling_params.prompt_logprobs is not None):
+            if (is_prompt and sampling_params.prompt_logprobs is not None):
                 # For tokens in the prompt that we only need to get
                 # their logprobs
                 query_len = seq_group.query_len
@@ -415,11 +415,15 @@ class SamplingTensors:
                 frequency_penalties += [f] * len(seq_ids)
                 repetition_penalties += [r] * len(seq_ids)
 
+            if _USE_TRITON_SAMPLER:
                 if is_prompt:
                     prompt_best_of.append(sampling_params.best_of)
                     query_len = seq_group.query_len
                     assert query_len is not None
 
+                seed = sampling_params.seed
+                is_greedy = sampling_params.sampling_type == SamplingType.GREEDY
+
                 for seq_id in seq_ids:
                     seq_data = seq_group.seq_data[seq_id]
                     extra_entropy = extra_entropy or ()
@@ -549,7 +553,7 @@ class SamplingTensors:
             device="cpu",
             dtype=torch.long,
             pin_memory=pin_memory,
-        ).T.contiguous()
+        ).t().contiguous()
 
         # Because the memory is pinned, we can do non-blocking
         # transfer to device.