[V1] Optimize handling of sampling metadata and req_ids list (#13244)

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

tests/v1/sample/test_rejection_sampler.py

@@ -26,17 +26,13 @@ def create_logits_tensor(token_ids: List[int],
 def create_sampling_metadata(spec_tokens: List[List[int]]) -> SamplingMetadata:
     batch_size = len(spec_tokens)
     return SamplingMetadata(
-        temperature=0.0,
+        temperature=torch.tensor([]),
         all_greedy=True,
         all_random=False,
-        rejection_sampling=True,
         spec_token_ids=spec_tokens,
         top_p=None,
         top_k=None,
-        no_top_p=False,
-        no_top_k=False,
         min_p=torch.empty(batch_size, ),
-        no_min_p=True,
         generators={},
         max_num_logprobs=0,
         no_penalties=False,
@@ -45,8 +41,7 @@ def create_sampling_metadata(spec_tokens: List[List[int]]) -> SamplingMetadata:
         presence_penalties=torch.tensor([]),
         repetition_penalties=torch.tensor([]),
         output_token_ids=[],
-        min_tokens=[],
-        stop_token_ids=[],
+        min_tokens={},
         logit_bias=[None] * batch_size,
     )