Implement Async Scheduling (#19970)

Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>

tests/v1/core/test_scheduler.py

@@ -19,133 +19,7 @@ from vllm.v1.request import Request, RequestStatus
 from vllm.v1.structured_output import StructuredOutputManager
 from vllm.v1.structured_output.request import StructuredOutputRequest
 
-EOS_TOKEN_ID = 50256
-
-
-def create_scheduler(
-    model: str = "facebook/opt-125m",
-    max_num_seqs: int = 16,
-    max_num_batched_tokens: int = 8192,
-    enable_prefix_caching: Optional[bool] = None,
-    long_prefill_token_threshold: int = 0,
-    disable_chunked_mm_input: bool = False,
-    use_kv_connector: bool = False,
-    num_blocks: int = 10000,
-    block_size: int = 16,
-    max_model_len: Optional[int] = None,
-    num_speculative_tokens: Optional[int] = None,
-    skip_tokenizer_init: bool = False,
-) -> Scheduler:
-    '''Create scheduler under test.
-
-    Args:
-      model: model under test
-      max_num_seqs: max sequences to schedule
-      max_num_batch_tokens: max num tokens to batch
-      enable_prefix_caching: optionally force APC config
-                             (True/False) or use default
-                             (None)
-
-    Returns:
-      {class}`Scheduler` instance
-    '''
-    if max_model_len is None:
-        max_model_len = max_num_batched_tokens
-    scheduler_config = SchedulerConfig(
-        max_num_seqs=max_num_seqs,
-        max_num_batched_tokens=max_num_batched_tokens,
-        max_model_len=max_model_len,
-        long_prefill_token_threshold=long_prefill_token_threshold,
-        disable_chunked_mm_input=disable_chunked_mm_input,
-        enable_chunked_prefill=True,
-    )
-    model_config = ModelConfig(
-        model=model,
-        task="auto",
-        tokenizer=model,
-        tokenizer_mode="auto",
-        trust_remote_code=True,
-        dtype="float16",
-        seed=42,
-        skip_tokenizer_init=skip_tokenizer_init,
-    )
-    # Cache config, optionally force APC
-    kwargs_cache = ({} if enable_prefix_caching is None else {
-        'enable_prefix_caching': enable_prefix_caching
-    })
-    cache_config = CacheConfig(
-        block_size=block_size,
-        gpu_memory_utilization=0.9,
-        swap_space=0,
-        cache_dtype="auto",
-        **kwargs_cache,
-    )
-    kv_transfer_config = KVTransferConfig(
-        kv_connector="SharedStorageConnector",
-        kv_role="kv_both",
-        kv_connector_extra_config={"shared_storage_path": "local_storage"},
-    ) if use_kv_connector else None
-
-    speculative_config: Optional[SpeculativeConfig] = None
-    if num_speculative_tokens is not None:
-        speculative_config = SpeculativeConfig(
-            model="ngram", num_speculative_tokens=num_speculative_tokens)
-
-    vllm_config = VllmConfig(
-        scheduler_config=scheduler_config,
-        model_config=model_config,
-        cache_config=cache_config,
-        kv_transfer_config=kv_transfer_config,
-        speculative_config=speculative_config,
-    )
-    kv_cache_config = KVCacheConfig(
-        num_blocks=num_blocks,  # A large number of blocks to hold all requests
-        kv_cache_tensors=[],
-        kv_cache_groups=[
-            KVCacheGroupSpec(['layer'],
-                             FullAttentionSpec(block_size, 1, 1, torch.float32,
-                                               False))
-        ],
-    )
-    cache_config.num_gpu_blocks = num_blocks
-    return Scheduler(
-        vllm_config=vllm_config,
-        kv_cache_config=kv_cache_config,
-        log_stats=True,
-        structured_output_manager=StructuredOutputManager(vllm_config),
-    )
-
-
-def create_requests(num_requests: int,
-                    num_tokens: int = 10,
-                    mm_positions: Optional[list[PlaceholderRange]] = None,
-                    max_tokens: int = 16,
-                    stop_token_ids: Optional[list[int]] = None,
-                    prompt_logprobs: Optional[int] = None):
-    sampling_params = SamplingParams(ignore_eos=False,
-                                     max_tokens=max_tokens,
-                                     stop_token_ids=stop_token_ids,
-                                     prompt_logprobs=prompt_logprobs)
-    requests = []
-    for i in range(num_requests):
-        if mm_positions is not None:
-            mm_position = mm_positions[i]
-            mm_inputs = [MultiModalKwargs({})] * len(mm_position)
-        else:
-            mm_position = None
-            mm_inputs = None
-        request = Request(
-            request_id=f"{i}",
-            prompt_token_ids=[i] * num_tokens,
-            sampling_params=sampling_params,
-            pooling_params=None,
-            multi_modal_inputs=mm_inputs,
-            multi_modal_placeholders=mm_position,
-            multi_modal_hashes=None,
-            eos_token_id=EOS_TOKEN_ID,
-        )
-        requests.append(request)
-    return requests
+from .utils import EOS_TOKEN_ID, create_requests, create_scheduler
 
 
 def test_add_requests():