[V1][Minor] Move scheduler outputs to a separate file (vllm-project#13062)

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

Author: WoosukKwon

vllm/v1/core/scheduler.py

@@ -1,26 +1,20 @@
 # SPDX-License-Identifier: Apache-2.0
 
 from collections import deque
-from dataclasses import dataclass
-from typing import (TYPE_CHECKING, Deque, Dict, Iterable, List, Optional, Set,
-                    Tuple, Union)
+from typing import Deque, Dict, Iterable, List, Optional, Set, Tuple, Union
 
 from vllm.config import CacheConfig, LoRAConfig, ModelConfig, SchedulerConfig
 from vllm.logger import init_logger
-from vllm.lora.request import LoRARequest
-from vllm.sampling_params import SamplingParams
 from vllm.v1.core.encoder_cache_manager import (EncoderCacheManager,
                                                 compute_encoder_budget)
 from vllm.v1.core.kv_cache_manager import KVCacheManager
+from vllm.v1.core.scheduler_output import (CachedRequestData, NewRequestData,
+                                           SchedulerOutput)
 from vllm.v1.engine import EngineCoreOutput, EngineCoreOutputs
 from vllm.v1.metrics.stats import SchedulerStats
 from vllm.v1.outputs import ModelRunnerOutput
 from vllm.v1.request import Request, RequestStatus
 
-if TYPE_CHECKING:
-    from vllm.multimodal import MultiModalKwargs
-    from vllm.multimodal.base import PlaceholderRange
-
 logger = init_logger(__name__)
 
 
@@ -600,80 +594,3 @@ def make_stats(self) -> SchedulerStats:
             num_waiting_reqs=len(self.waiting),
             gpu_cache_usage=self.kv_cache_manager.usage,
         )
-
-
-@dataclass
-class NewRequestData:
-
-    req_id: str
-    prompt_token_ids: List[int]
-    prompt: Optional[str]
-    mm_inputs: List["MultiModalKwargs"]
-    mm_hashes: List[str]
-    mm_positions: List["PlaceholderRange"]
-    sampling_params: SamplingParams
-    block_ids: List[int]
-    num_computed_tokens: int
-    lora_request: Optional[LoRARequest]
-
-    @classmethod
-    def from_request(
-        cls,
-        request: Request,
-        block_ids: List[int],
-        num_computed_tokens: int,
-    ) -> "NewRequestData":
-        return cls(
-            req_id=request.request_id,
-            prompt_token_ids=request.prompt_token_ids,
-            prompt=request.prompt,
-            mm_inputs=request.mm_inputs,
-            mm_hashes=request.mm_hashes,
-            mm_positions=request.mm_positions,
-            sampling_params=request.sampling_params,
-            block_ids=block_ids,
-            num_computed_tokens=num_computed_tokens,
-            lora_request=request.lora_request,
-        )
-
-
-@dataclass
-class CachedRequestData:
-
-    req_id: str
-    # If resumed_from_preemption is False, new_block_ids will be appended to
-    # the request's block IDs. If True, new_block_ids will be used as the
-    # request's block IDs instead of appending to the existing block IDs.
-    resumed_from_preemption: bool
-    new_block_ids: List[int]
-    num_computed_tokens: int
-
-    @classmethod
-    def from_request(
-        cls,
-        request: Request,
-        resumed_from_preemption: bool,
-        new_block_ids: List[int],
-        num_computed_tokens: int,
-    ) -> "CachedRequestData":
-        return cls(
-            req_id=request.request_id,
-            resumed_from_preemption=resumed_from_preemption,
-            new_block_ids=new_block_ids,
-            num_computed_tokens=num_computed_tokens,
-        )
-
-
-@dataclass
-class SchedulerOutput:
-
-    scheduled_new_reqs: List[NewRequestData]
-    scheduled_cached_reqs: List[CachedRequestData]
-
-    num_scheduled_tokens: Dict[str, int]
-    total_num_scheduled_tokens: int
-    scheduled_encoder_inputs: Dict[str, List[int]]
-    num_common_prefix_blocks: int
-
-    finished_req_ids: Set[str]
-    free_encoder_input_ids: List[Tuple[str, int]]

vllm/v1/core/scheduler_output.py

@@ -0,0 +1,108 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Dict, List, Optional, Set, Tuple
+
+if TYPE_CHECKING:
+    from vllm.lora.request import LoRARequest
+    from vllm.multimodal import MultiModalKwargs
+    from vllm.multimodal.base import PlaceholderRange
+    from vllm.sampling_params import SamplingParams
+    from vllm.v1.request import Request
+
+
+@dataclass
+class NewRequestData:
+
+    req_id: str
+    prompt_token_ids: List[int]
+    prompt: Optional[str]
+    mm_inputs: List["MultiModalKwargs"]
+    mm_hashes: List[str]
+    mm_positions: List["PlaceholderRange"]
+    sampling_params: "SamplingParams"
+    block_ids: List[int]
+    num_computed_tokens: int
+    lora_request: Optional["LoRARequest"]
+
+    @classmethod
+    def from_request(
+        cls,
+        request: "Request",
+        block_ids: List[int],
+        num_computed_tokens: int,
+    ) -> "NewRequestData":
+        return cls(
+            req_id=request.request_id,
+            prompt_token_ids=request.prompt_token_ids,
+            prompt=request.prompt,
+            mm_inputs=request.mm_inputs,
+            mm_hashes=request.mm_hashes,
+            mm_positions=request.mm_positions,
+            sampling_params=request.sampling_params,
+            block_ids=block_ids,
+            num_computed_tokens=num_computed_tokens,
+            lora_request=request.lora_request,
+        )
+
+
+@dataclass
+class CachedRequestData:
+
+    req_id: str
+    # If resumed_from_preemption is False, new_block_ids will be appended to
+    # the request's block IDs. If True, new_block_ids will be used as the
+    # request's block IDs instead of appending to the existing block IDs.
+    resumed_from_preemption: bool
+    new_block_ids: List[int]
+    num_computed_tokens: int
+
+    @classmethod
+    def from_request(
+        cls,
+        request: "Request",
+        resumed_from_preemption: bool,
+        new_block_ids: List[int],
+        num_computed_tokens: int,
+    ) -> "CachedRequestData":
+        return cls(
+            req_id=request.request_id,
+            resumed_from_preemption=resumed_from_preemption,
+            new_block_ids=new_block_ids,
+            num_computed_tokens=num_computed_tokens,
+        )
+
+
+@dataclass
+class SchedulerOutput:
+
+    # List of the requests that are scheduled for the first time.
+    # We cache the request's data in each worker process, so that we don't
+    # need to re-send it every scheduling step.
+    scheduled_new_reqs: List[NewRequestData]
+    # List of the requests that have been scheduled before.
+    # Since the request's data is already cached in the worker processes,
+    # we only send the diff to minimize the communication cost.
+    scheduled_cached_reqs: List[CachedRequestData]
+
+    # req_id -> num_scheduled_tokens
+    # Number of tokens scheduled for each request.
+    num_scheduled_tokens: Dict[str, int]
+    # Total number of tokens scheduled for all requests.
+    # Equal to sum(num_scheduled_tokens.values())
+    total_num_scheduled_tokens: int
+    # req_id -> encoder input indices that need processing.
+    # E.g., if a request has [0, 1], it could mean the vision encoder needs
+    # to process that the request's 0-th and 1-th images in the current step.
+    scheduled_encoder_inputs: Dict[str, List[int]]
+    # Number of common prefix blocks for all requests.
+    # This can be used for cascade attention.
+    num_common_prefix_blocks: int
+
+    # Request IDs that are finished in between the previous and the current
+    # steps. This is used to notify the workers about the finished requests
+    # so that they can free the cached states for those requests.
+    finished_req_ids: Set[str]
+    # List of (req_id, encoder_input_index) tuples.
+    # Used to free the encoder cache.
+    free_encoder_input_ids: List[Tuple[str, int]]

vllm/v1/worker/gpu_model_runner.py

@@ -37,7 +37,7 @@
 from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
 
 if TYPE_CHECKING:
-    from vllm.v1.core.scheduler import SchedulerOutput
+    from vllm.v1.core.scheduler_output import SchedulerOutput
 
 logger = init_logger(__name__)
 

vllm/v1/worker/gpu_worker.py

@@ -18,15 +18,14 @@
 from vllm.model_executor import set_random_seed
 from vllm.platforms import current_platform
 from vllm.utils import GiB_bytes
-from vllm.v1.core.scheduler import SchedulerOutput
 from vllm.v1.kv_cache_interface import KVCacheConfig, KVCacheSpec
 from vllm.v1.outputs import ModelRunnerOutput
 from vllm.v1.worker.gpu_model_runner import GPUModelRunner
 
 logger = init_logger(__name__)
 
 if TYPE_CHECKING:
-    from vllm.v1.core.scheduler import SchedulerOutput
+    from vllm.v1.core.scheduler_output import SchedulerOutput
 
 
 class Worker: