mirror of
https://github.com/vllm-project/vllm-ascend.git
synced 2025-10-20 13:43:53 +08:00
c7446438a9
### What this PR does / why we need it?
Follow vllm-project/vllm lint way:
https://github.com/vllm-project/vllm/blob/main/.pre-commit-config.yaml
Enable pre-commit to avoid some low level error AMAP.
This pr is one step of #1241, The purpose is make linting system more
clear and convenient, on this step, Mainly did the following things:
yapf, actionlint, ruff, typos, isort, mypy, png-lint, signoff-commit,
enforce-import-regex-instead-of-re.
TODO:
- clang-format(check for csrc with google style)
need clean code, disable for now
- pymarkdown
need clean code, disable for now
- shellcheck
need clean code, disable for now
### Does this PR introduce _any_ user-facing change?
Only developer UX change:
https://vllm-ascend--1256.org.readthedocs.build/en/1256/developer_guide/contributing.html#run-lint-locally
```
pip install -r requirements-lint.txt && pre-commit install
bash format.sh
```
### How was this patch tested?
CI passed with new added/existing test.
Co-authored-by: Yikun [yikunkero@gmail.com](mailto:yikunkero@gmail.com)
Co-authored-by: wangli
[wangli858794774@gmail.com](mailto:wangli858794774@gmail.com)
- vLLM version: v0.9.1
- vLLM main:
5358cce5ff
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
501 lines
23 KiB
Python
501 lines
23 KiB
Python
#
|
|
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# This file is a part of the vllm-ascend project.
|
|
#
|
|
import time
|
|
from collections import deque
|
|
from typing import Iterable, Union
|
|
|
|
from vllm.config import VllmConfig
|
|
from vllm.distributed.kv_events import KVEventBatch
|
|
from vllm.logger import logger
|
|
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
|
|
from vllm.utils import cdiv
|
|
from vllm.v1.core.kv_cache_manager import KVCacheBlocks
|
|
from vllm.v1.core.sched.output import NewRequestData, SchedulerOutput
|
|
from vllm.v1.core.sched.scheduler import Scheduler
|
|
from vllm.v1.engine import EngineCoreEventType, EngineCoreOutputs
|
|
from vllm.v1.kv_cache_interface import KVCacheConfig
|
|
from vllm.v1.outputs import ModelRunnerOutput
|
|
from vllm.v1.request import Request, RequestStatus
|
|
from vllm.v1.structured_output import StructuredOutputManager
|
|
|
|
|
|
class AscendScheduler(Scheduler):
|
|
"""This Scheduler extends vllm's original v1 scheduler
|
|
with prefill-first scheduling strategy."""
|
|
|
|
def __init__(
|
|
self,
|
|
vllm_config: VllmConfig,
|
|
kv_cache_config: KVCacheConfig,
|
|
structured_output_manager: StructuredOutputManager,
|
|
mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
|
|
include_finished_set: bool = False,
|
|
log_stats: bool = False,
|
|
) -> None:
|
|
super().__init__(vllm_config, kv_cache_config,
|
|
structured_output_manager, mm_registry,
|
|
include_finished_set, log_stats)
|
|
self.scheduled_req_ids: set[str] = set()
|
|
self.running: list[Request] = []
|
|
|
|
def schedule(self) -> SchedulerOutput:
|
|
if self.scheduler_config.chunked_prefill_enabled:
|
|
return super().schedule()
|
|
scheduled_new_reqs: list[Request] = []
|
|
scheduled_resumed_reqs: list[Request] = []
|
|
scheduled_running_reqs: list[Request] = []
|
|
preempted_reqs: list[Request] = []
|
|
|
|
req_to_new_block_ids: dict[str, list[int]] = {}
|
|
num_scheduled_tokens: dict[str, int] = {}
|
|
token_budget = self.max_num_scheduled_tokens
|
|
# Spec decode-related.
|
|
scheduled_spec_decode_tokens: dict[str, list[int]] = {}
|
|
|
|
# For logging.
|
|
scheduled_timestamp = time.monotonic()
|
|
|
|
# Record scheduled LoRA requests.
|
|
scheduled_loras: set[int] = set()
|
|
|
|
# Use a temporary deque to collect requests that need to be skipped
|
|
# and put back at the head of the waiting queue later
|
|
skipped_waiting_requests: deque[Request] = deque()
|
|
|
|
# Schedule prefill requests first.
|
|
while self.waiting and token_budget > 0:
|
|
if len(self.running) == self.max_num_running_reqs:
|
|
break
|
|
|
|
request = self.waiting[0]
|
|
|
|
def skip_cur_request():
|
|
self.waiting.popleft()
|
|
skipped_waiting_requests.appendleft(request)
|
|
|
|
num_prealloc_computed_tokens = 0
|
|
# P/D: skip request if still waiting for remote kvs.
|
|
if request.status == RequestStatus.WAITING_FOR_REMOTE_KVS:
|
|
is_ready = self._update_waiting_for_remote_kv(request)
|
|
if is_ready:
|
|
request.status = RequestStatus.WAITING
|
|
num_prealloc_computed_tokens = (
|
|
request.num_computed_tokens)
|
|
else:
|
|
skip_cur_request()
|
|
continue
|
|
|
|
# Check that adding the request still respects the max_loras
|
|
# constraint.
|
|
if (self.lora_config and request.lora_request and
|
|
(len(scheduled_loras) == self.lora_config.max_loras
|
|
and request.lora_request.lora_int_id not in scheduled_loras)):
|
|
# Scheduling would exceed max_loras, skip.
|
|
skip_cur_request()
|
|
continue
|
|
|
|
num_external_computed_tokens = 0
|
|
load_kv_async = False
|
|
|
|
# Get already-cached tokens.
|
|
if num_prealloc_computed_tokens == 0:
|
|
new_computed_blocks, num_native_computed_tokens = \
|
|
self.kv_cache_manager.get_computed_blocks(
|
|
request)
|
|
|
|
# Get externally-cached tokens if using a KVConnector.
|
|
if self.connector is not None:
|
|
num_external_computed_tokens, load_kv_async = (
|
|
self.connector.get_num_new_matched_tokens(
|
|
request, num_native_computed_tokens))
|
|
|
|
# Total computed tokens (local + external).
|
|
num_computed_tokens = (num_native_computed_tokens +
|
|
num_external_computed_tokens)
|
|
else:
|
|
# P/D: skip checking prefix cache if loaded from remote kvs.
|
|
new_computed_blocks = KVCacheBlocks.create_empty()
|
|
num_native_computed_tokens = 0
|
|
|
|
# Total computed tokens (allocated in prior step).
|
|
num_computed_tokens = num_prealloc_computed_tokens
|
|
|
|
# P/D: loading remote KV, do not allocate for new work.
|
|
if load_kv_async:
|
|
assert num_external_computed_tokens > 0
|
|
num_new_tokens = 0
|
|
blocks = None
|
|
# Number of tokens to be scheduled.
|
|
else:
|
|
prompt_limit = self._get_prompt_limit(request)
|
|
# Get already-cached tokens.
|
|
computed_blocks, num_computed_tokens = (
|
|
self.kv_cache_manager.get_computed_blocks(request))
|
|
# We use `request.num_tokens` instead of
|
|
# `request.num_prompt_tokens` to consider the resumed
|
|
# requests, which have output tokens.
|
|
num_new_tokens = request.num_tokens - num_computed_tokens
|
|
max_tokens_in_kvcache = (self.kv_cache_config.num_blocks *
|
|
self.block_size)
|
|
prompt_limit = min(prompt_limit, max_tokens_in_kvcache)
|
|
|
|
# Finish request that exceeds prompt_limit or kv cache size.
|
|
if num_new_tokens > prompt_limit:
|
|
logger.warning(
|
|
"Input prompt (%d tokens) is too long"
|
|
" and exceeds limit of %d",
|
|
num_new_tokens,
|
|
prompt_limit,
|
|
)
|
|
request.status = RequestStatus.FINISHED_IGNORED
|
|
self.finished_req_ids.add( # type: ignore
|
|
request.request_id) # type: ignore
|
|
self.waiting.popleft()
|
|
continue
|
|
|
|
if num_new_tokens > token_budget:
|
|
# Scheduling would exceed token_budget, skip.
|
|
skip_cur_request()
|
|
continue
|
|
assert num_new_tokens > 0
|
|
blocks = computed_blocks.blocks[0]
|
|
|
|
watermark = getattr(self.scheduler_config, "watermark", 0.01)
|
|
if not self._check_watermark_for_prefill(request, num_new_tokens,
|
|
blocks, watermark):
|
|
# Scheduling would exceed watermark, skip.
|
|
skip_cur_request()
|
|
continue
|
|
|
|
new_blocks = self.kv_cache_manager.allocate_slots(
|
|
request,
|
|
num_new_tokens + num_external_computed_tokens,
|
|
num_native_computed_tokens,
|
|
new_computed_blocks=computed_blocks,
|
|
num_lookahead_tokens=self.num_lookahead_tokens,
|
|
delay_cache_blocks=load_kv_async)
|
|
if new_blocks is None:
|
|
# The request cannot be scheduled.
|
|
break
|
|
|
|
# KVConnector: update internal state after allocation.
|
|
# This information is used to determine if a load is
|
|
# needed for this request.
|
|
if num_external_computed_tokens:
|
|
assert self.connector is not None
|
|
self.connector.update_state_after_alloc(
|
|
request,
|
|
new_computed_blocks + new_blocks,
|
|
num_external_computed_tokens,
|
|
)
|
|
|
|
self.waiting.popleft()
|
|
if load_kv_async:
|
|
# If loading async, allocate memory and put request
|
|
# into the WAITING_FOR_REMOTE_KV state.
|
|
skipped_waiting_requests.appendleft(request)
|
|
request.status = RequestStatus.WAITING_FOR_REMOTE_KVS
|
|
continue
|
|
self.running.append(request)
|
|
if self.log_stats:
|
|
request.record_event(EngineCoreEventType.SCHEDULED,
|
|
scheduled_timestamp)
|
|
self.scheduled_req_ids.add(request.request_id)
|
|
# Check request status.
|
|
if request.status == RequestStatus.WAITING:
|
|
scheduled_new_reqs.append(request)
|
|
elif request.status == RequestStatus.PREEMPTED:
|
|
scheduled_resumed_reqs.append(request)
|
|
else:
|
|
raise RuntimeError(f"Invalid request status: {request.status}")
|
|
|
|
if self.lora_config and request.lora_request:
|
|
scheduled_loras.add(request.lora_request.lora_int_id)
|
|
req_to_new_block_ids[request.request_id] = (
|
|
self.kv_cache_manager.get_block_ids(request.request_id))
|
|
# Update request info.
|
|
num_scheduled_tokens[request.request_id] = num_new_tokens
|
|
token_budget -= num_new_tokens
|
|
request.status = RequestStatus.RUNNING
|
|
request.num_computed_tokens = num_computed_tokens
|
|
# Count the number of prefix cached tokens.
|
|
if request.num_cached_tokens < 0:
|
|
request.num_cached_tokens = num_computed_tokens
|
|
|
|
# Put back any skipped requests at the head of the waiting queue
|
|
if skipped_waiting_requests:
|
|
self.waiting.extendleft(skipped_waiting_requests)
|
|
|
|
# If no prefill requests are scheduled,
|
|
# Schedule decode requests next.
|
|
if len(self.scheduled_req_ids) == 0:
|
|
req_index = 0
|
|
while req_index < len(self.running) and token_budget > 0:
|
|
request = self.running[req_index]
|
|
if request.request_id in self.scheduled_req_ids:
|
|
# This request has already been scheduled.
|
|
req_index += 1
|
|
continue
|
|
|
|
num_new_tokens = (request.num_tokens_with_spec -
|
|
request.num_computed_tokens)
|
|
assert (request.num_tokens - request.num_computed_tokens) == 1
|
|
num_new_tokens = min(num_new_tokens, token_budget)
|
|
# Make sure the input position does not exceed the max model len.
|
|
# This is necessary when using spec decoding.
|
|
num_new_tokens = min(
|
|
num_new_tokens,
|
|
self.max_model_len - request.num_computed_tokens)
|
|
# Check that adding the request still respects the max_loras
|
|
# constraint.
|
|
if self.lora_config and request.lora_request and (
|
|
len(scheduled_loras) == self.lora_config.max_loras
|
|
and request.lora_request.lora_int_id
|
|
not in scheduled_loras):
|
|
# Scheduling would exceed max_loras, skip.
|
|
num_new_tokens = 0
|
|
|
|
if num_new_tokens == 0:
|
|
# The request cannot be scheduled because one of the following
|
|
# reason:
|
|
# 1. No new tokens to schedule. This may happen when PP>1 and
|
|
# we have already scheduled all prompt tokens but they are
|
|
# not finished yet.
|
|
# 2. Adding the request exceeds the max_loras constraint.
|
|
# NOTE(woosuk): Here, by doing `continue` instead of `break`,
|
|
# we do not strictly follow the FCFS scheduling policy and
|
|
# allow the lower-priority requests to be scheduled.
|
|
req_index += 1
|
|
continue
|
|
|
|
num_draft_tokens = max(
|
|
num_new_tokens + request.num_computed_tokens -
|
|
request.num_tokens, 0)
|
|
|
|
while True:
|
|
new_blocks = self.kv_cache_manager.allocate_slots(
|
|
request,
|
|
num_new_tokens,
|
|
num_draft_tokens=num_draft_tokens,
|
|
num_lookahead_tokens=self.num_lookahead_tokens)
|
|
if new_blocks is None:
|
|
# The request cannot be scheduled.
|
|
# Preempt the lowest-priority request.
|
|
preempted_req = self.running.pop()
|
|
self.kv_cache_manager.free(preempted_req)
|
|
preempted_req.status = RequestStatus.PREEMPTED
|
|
preempted_req.num_computed_tokens = 0
|
|
if self.log_stats:
|
|
preempted_req.record_event(
|
|
EngineCoreEventType.PREEMPTED,
|
|
scheduled_timestamp)
|
|
self.waiting.appendleft(preempted_req)
|
|
preempted_reqs.append(preempted_req)
|
|
if preempted_req == request:
|
|
# No more request to preempt.
|
|
can_schedule = False
|
|
break
|
|
else:
|
|
# The request can be scheduled.
|
|
can_schedule = True
|
|
break
|
|
if not can_schedule:
|
|
break
|
|
assert new_blocks is not None
|
|
|
|
# Schedule the request.
|
|
scheduled_running_reqs.append(request)
|
|
self.scheduled_req_ids.add(request.request_id)
|
|
req_to_new_block_ids[request.request_id] = (
|
|
new_blocks.get_block_ids())
|
|
num_scheduled_tokens[request.request_id] = num_new_tokens
|
|
token_budget -= num_new_tokens
|
|
req_index += 1
|
|
|
|
# Speculative decode related.
|
|
if request.spec_token_ids:
|
|
num_scheduled_spec_tokens = (num_new_tokens +
|
|
request.num_computed_tokens -
|
|
request.num_tokens)
|
|
if num_scheduled_spec_tokens > 0:
|
|
# Trim spec_token_ids list to num_scheduled_spec_tokens.
|
|
del request.spec_token_ids[num_scheduled_spec_tokens:]
|
|
scheduled_spec_decode_tokens[request.request_id] = (
|
|
request.spec_token_ids)
|
|
|
|
# Record scheduled LoRA requests.
|
|
if self.lora_config and request.lora_request:
|
|
scheduled_loras.add(request.lora_request.lora_int_id)
|
|
|
|
# Check if the scheduling constraints are satisfied.
|
|
total_num_scheduled_tokens = sum(num_scheduled_tokens.values())
|
|
assert total_num_scheduled_tokens <= self.max_num_scheduled_tokens
|
|
assert token_budget >= 0
|
|
assert len(self.running) <= self.max_num_running_reqs
|
|
assert len(scheduled_new_reqs) + len(scheduled_resumed_reqs) + len(
|
|
scheduled_running_reqs) <= len(self.running)
|
|
|
|
# Get the longest common prefix among all requests in the running queue.
|
|
# This can be potentially used for cascade attention.
|
|
num_common_prefix_blocks = 0
|
|
if self.running:
|
|
any_request = self.running[0]
|
|
num_common_prefix_blocks = (
|
|
self.kv_cache_manager.get_num_common_prefix_blocks(
|
|
any_request, len(self.running)))
|
|
|
|
# Construct the scheduler output.
|
|
new_reqs_data = [
|
|
NewRequestData.from_request(req,
|
|
req_to_new_block_ids[req.request_id])
|
|
for req in scheduled_new_reqs
|
|
]
|
|
|
|
cached_reqs_data = self._make_cached_request_data(
|
|
scheduled_running_reqs, scheduled_resumed_reqs,
|
|
num_scheduled_tokens, scheduled_spec_decode_tokens,
|
|
req_to_new_block_ids)
|
|
scheduled_cached_reqs = cached_reqs_data
|
|
|
|
scheduler_output = SchedulerOutput(
|
|
scheduled_new_reqs=new_reqs_data,
|
|
scheduled_cached_reqs=scheduled_cached_reqs,
|
|
num_scheduled_tokens=num_scheduled_tokens,
|
|
total_num_scheduled_tokens=total_num_scheduled_tokens,
|
|
scheduled_spec_decode_tokens=scheduled_spec_decode_tokens,
|
|
scheduled_encoder_inputs={},
|
|
num_common_prefix_blocks=num_common_prefix_blocks,
|
|
# finished_req_ids is an existing state in the scheduler,
|
|
# instead of being newly scheduled in this step.
|
|
# It contains the request IDs that are finished in between
|
|
# the previous and the current steps.
|
|
finished_req_ids=self.finished_req_ids, # type: ignore
|
|
free_encoder_input_ids=self.encoder_cache_manager.get_freed_ids(),
|
|
structured_output_request_ids={},
|
|
grammar_bitmask=None,
|
|
)
|
|
|
|
# NOTE(Kuntai): this function is designed for multiple purposes:
|
|
# 1. Plan the KV cache store
|
|
# 2. Wrap up all the KV cache load / save ops into an opaque object
|
|
# 3. Clear the internal states of the connector
|
|
if self.connector is not None:
|
|
meta = self.connector.build_connector_meta(scheduler_output)
|
|
scheduler_output.kv_connector_metadata = meta
|
|
|
|
events = self.kv_cache_manager.take_events()
|
|
if events:
|
|
batch = KVEventBatch(ts=time.time(), events=events)
|
|
self.kv_event_publisher.publish(batch)
|
|
|
|
# Advance the number of computed tokens for the request AFTER
|
|
# the request is scheduled.
|
|
# 1. The scheduler_output of the current step has to include the
|
|
# original number of scheduled tokens to determine input IDs.
|
|
# 2. Advance the number of computed tokens here allowing us to
|
|
# schedule the prefill request again immediately in the next
|
|
# scheduling step.
|
|
# 3. If some tokens (e.g. spec tokens) are rejected later, the number of
|
|
# computed tokens will be adjusted in update_from_output.
|
|
for req_id, num_scheduled_token in num_scheduled_tokens.items():
|
|
self.requests[req_id].num_computed_tokens += num_scheduled_token
|
|
|
|
self.finished_req_ids = set() # type: ignore
|
|
return scheduler_output
|
|
|
|
def _check_watermark_for_prefill(self,
|
|
request,
|
|
num_new_tokens,
|
|
computed_blocks,
|
|
watermark=0.01):
|
|
computed_blocks = computed_blocks or []
|
|
watermark_blocks = self.kv_cache_config.num_blocks * watermark
|
|
num_computed_tokens = (request.num_computed_tokens +
|
|
len(computed_blocks) * self.block_size)
|
|
num_required_blocks = cdiv(num_new_tokens + num_computed_tokens,
|
|
self.block_size)
|
|
req_blocks = self.kv_cache_manager.coordinator.get_blocks(
|
|
request.request_id)
|
|
num_new_blocks = (num_required_blocks - len(req_blocks) -
|
|
len(computed_blocks))
|
|
num_evictable_computed_blocks = sum(1 for blk in computed_blocks
|
|
if blk.ref_cnt == 0)
|
|
# If number of free blocks is less than water mark after allocating, don't allocate.
|
|
if (self.kv_cache_manager.block_pool.get_num_free_blocks() -
|
|
num_evictable_computed_blocks -
|
|
num_new_blocks) < watermark_blocks:
|
|
return False
|
|
return True
|
|
|
|
def _get_prompt_limit(self, request: Request) -> int:
|
|
if (self.scheduler_config.chunked_prefill_enabled
|
|
and not self.scheduler_config.is_multi_step):
|
|
prompt_limit = self.scheduler_config.max_model_len
|
|
else:
|
|
prompt_limit = min(
|
|
self.scheduler_config.max_model_len,
|
|
self.scheduler_config.max_num_batched_tokens,
|
|
)
|
|
|
|
# Model is fine tuned with long context. Return the fine tuned max_len.
|
|
if request.lora_request and request.lora_request.long_lora_max_len:
|
|
assert prompt_limit <= request.lora_request.long_lora_max_len
|
|
return request.lora_request.long_lora_max_len
|
|
else:
|
|
return prompt_limit
|
|
|
|
def finish_requests(
|
|
self,
|
|
request_ids: Union[str, Iterable[str]],
|
|
finished_status: RequestStatus,
|
|
) -> None:
|
|
"""Handles the finish signal from outside the scheduler.
|
|
|
|
For example, the API server can abort a request when the client
|
|
disconnects.
|
|
"""
|
|
for req_id in request_ids:
|
|
request = self.requests.get(req_id)
|
|
if request is None:
|
|
# Invalid request ID.
|
|
continue
|
|
if request.status == RequestStatus.RUNNING:
|
|
self.scheduled_req_ids.discard(request.request_id)
|
|
super().finish_requests(request_ids, finished_status)
|
|
|
|
def update_from_output(
|
|
self,
|
|
scheduler_output: SchedulerOutput,
|
|
model_runner_output: ModelRunnerOutput,
|
|
) -> EngineCoreOutputs:
|
|
num_scheduled_tokens = scheduler_output.num_scheduled_tokens
|
|
|
|
# NOTE(woosuk): As len(self.running) can be up to 1K or more, the below
|
|
# loop can be a performance bottleneck. We should do our best to avoid
|
|
# expensive operations inside the loop.
|
|
for request in self.running:
|
|
req_id = request.request_id
|
|
num_tokens_scheduled = num_scheduled_tokens.get(req_id, 0)
|
|
if num_tokens_scheduled == 0:
|
|
# The request was not scheduled in this step.
|
|
continue
|
|
if req_id in self.scheduled_req_ids:
|
|
self.scheduled_req_ids.remove(req_id)
|
|
|
|
return super().update_from_output(scheduler_output,
|
|
model_runner_output)
|