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

vllm/model_executor/sampling_metadata.py

@@ -13,6 +13,8 @@
 
 _SAMPLING_EPS = 1e-5
 _SEED_0_REPLACEMENT = 3403598558
+# Some triton sampler related code is guarded before it is ready.
+_USE_TRITON_SAMPLER = False
 
 
 @dataclass
@@ -347,14 +349,16 @@ def from_sampling_metadata(
         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
 
-        # We need one base seed per Triton slice.
-        seeds_to_generate = (extra_seeds_to_generate +
-                             get_num_triton_sampler_splits(vocab_size))
+        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))
 
         assert sampling_metadata.seq_groups is not None
         for seq_group in sampling_metadata.seq_groups:
@@ -366,9 +370,6 @@ def from_sampling_metadata(
             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 @@ def from_sampling_metadata(
                 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
@@ -415,23 +415,27 @@ def from_sampling_metadata(
                 frequency_penalties += [f] * len(seq_ids)
                 repetition_penalties += [r] * len(seq_ids)
 
-            if is_prompt:
-                prompt_best_of.append(sampling_params.best_of)
-                query_len = seq_group.query_len
-                assert query_len is not None
-
-            for seq_id in seq_ids:
-                seq_data = seq_group.seq_data[seq_id]
-                extra_entropy = extra_entropy or ()
-                seq_seeds = cls._get_sequence_seeds(
-                    seed,
-                    seq_data.get_len(),
-                    *extra_entropy,
-                    seq_id,
-                    seeds_to_generate=seeds_to_generate,
-                    is_greedy=is_greedy)
-                sampling_seeds.append(seq_seeds)
-            sample_indices.extend(seq_group.sample_indices)
+            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 ()
+                    seq_seeds = cls._get_sequence_seeds(
+                        seed,
+                        seq_data.get_len(),
+                        *extra_entropy,
+                        seq_id,
+                        seeds_to_generate=seeds_to_generate,
+                        is_greedy=is_greedy)
+                    sampling_seeds.append(seq_seeds)
+                sample_indices.extend(seq_group.sample_indices)
 
         if do_penalties:
             for seq_group in sampling_metadata.seq_groups:
@@ -549,7 +553,7 @@ def from_lists(cls, temperatures: List[float], top_ps: List[float],
             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.