Comment out unused code in sampler

vllm/model_executor/sampling_metadata.py

@@ -7,7 +7,6 @@
 
 from vllm.sampling_params import SamplingParams, SamplingType
 from vllm.sequence import SequenceData, SequenceGroupMetadata
-from vllm.triton_utils.sample import get_num_triton_sampler_splits
 from vllm.utils import (async_tensor_h2d, is_pin_memory_available,
                         make_tensor_with_pad, maybe_expand_dim)
 
@@ -347,14 +346,13 @@ 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))
+        # # 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 +364,6 @@ 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 +384,7 @@ 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 +409,26 @@ 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)
+            # The following code is for a Triton-based sampler
+            # that is not enabled.
+
+            # 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 do_penalties:
             for seq_group in sampling_metadata.seq_groups:
@@ -549,7 +546,7 @@ 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.