0ae9ee0f8a
### What this PR does / why we need it? The pr will fix some bug about spec decode / MTP The pr add a mtp e2e UT `test_mtp_correctness.py` **vllm_ascend/attention/attention.py** 1. add support `self.attn_mask_cache` only has 1 element to cover scene in which both spec docode and chunked prefill are enabled. **vllm_ascend/distributed/parallel_state.py** 1. remove 2 assert because spec decode worker would use init_worker twice **vllm_ascend/models/deepseek_mtp.py** 1. remove unused params; 2. add support w8a8 in `CustomDeepSeekMTP` **vllm_ascend/quantization/quant_config.py** 1. use `AscendUnquantizedFusedMoEMethod` instead of `UnquantizedFusedMoEMethod` **other** 1. replace `from vllm.logger import init_logger` to `from vllm.logger import logger` all of the vllm-ascend project ### Does this PR introduce _any_ user-facing change? ### How was this patch tested? Signed-off-by: mengwei805 <mengwei25@huawei.com>
320 lines
14 KiB
Python
320 lines
14 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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#
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from collections import deque
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from vllm.logger import logger
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from vllm.utils import cdiv
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from vllm.v1.core.sched.output import NewRequestData, SchedulerOutput
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from vllm.v1.core.sched.scheduler import Scheduler
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from vllm.v1.request import Request, RequestStatus
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class AscendScheduler(Scheduler):
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"""This Scheduler extends vllm's original v1 scheduler
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with prefill-first scheduling strategy."""
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def schedule(self) -> SchedulerOutput:
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if self.scheduler_config.chunked_prefill_enabled:
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return super().schedule()
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scheduled_new_reqs: list[Request] = []
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scheduled_resumed_reqs: list[Request] = []
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scheduled_running_reqs: list[Request] = []
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preempted_reqs: list[Request] = []
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req_to_new_block_ids: dict[str, list[int]] = {}
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num_scheduled_tokens: dict[str, int] = {}
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token_budget = self.max_num_scheduled_tokens
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# Spec decode-related.
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scheduled_spec_decode_tokens: dict[str, list[int]] = {}
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# Record scheduled LoRA requests.
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scheduled_loras: set[int] = set()
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# Use a temporary deque to collect requests that need to be skipped
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# and put back at the head of the waiting queue later
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skipped_waiting_requests: deque[Request] = deque()
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# Schedule prefill requests first.
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while self.waiting and token_budget > 0:
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if len(scheduled_new_reqs) == self.max_num_running_reqs:
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break
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request = self.waiting[0]
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def skip_cur_request():
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self.waiting.popleft()
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skipped_waiting_requests.appendleft(request)
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# Check that adding the request still respects the max_loras
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# constraint.
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if (self.lora_config and request.lora_request and
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(len(scheduled_loras) == self.lora_config.max_loras
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and request.lora_request.lora_int_id not in scheduled_loras)):
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# Scheduling would exceed max_loras, skip.
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skip_cur_request()
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continue
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prompt_limit = self._get_prompt_limit(request)
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# Get already-cached tokens.
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computed_blocks, num_computed_tokens = (
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self.kv_cache_manager.get_computed_blocks(request))
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num_new_tokens = request.num_prompt_tokens - num_computed_tokens
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if (0 < self.scheduler_config.long_prefill_token_threshold <
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num_new_tokens):
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num_new_tokens = (
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self.scheduler_config.long_prefill_token_threshold)
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max_tokens_in_kvcache = (self.kv_cache_config.num_blocks *
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self.block_size)
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prompt_limit = min(prompt_limit, max_tokens_in_kvcache)
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# Finish request that exceeds prompt_limit or kv cache size.
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if num_new_tokens > prompt_limit:
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logger.warning(
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"Input prompt (%d tokens) is too long"
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" and exceeds limit of %d",
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num_new_tokens,
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prompt_limit,
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)
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request.status = RequestStatus.FINISHED_IGNORED
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self.finished_req_ids.add(request.request_id) # type: ignore
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self.waiting.popleft()
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continue
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if num_new_tokens > token_budget:
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# Scheduling would exceed token_budget, skip.
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skip_cur_request()
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continue
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assert num_new_tokens > 0
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watermark = getattr(self.scheduler_config, "watermark", 0.01)
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if not self._check_watermark_for_prefill(
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request, num_new_tokens, computed_blocks, watermark):
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# Scheduling would exceed watermark, skip.
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skip_cur_request()
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continue
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new_blocks = self.kv_cache_manager.allocate_slots(
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request, num_new_tokens, computed_blocks)
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if new_blocks is None:
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# The request cannot be scheduled.
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break
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self.waiting.popleft()
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self.running.append(request)
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self.scheduled_req_ids.add(request.request_id)
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# Check request status.
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if request.status == RequestStatus.WAITING:
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scheduled_new_reqs.append(request)
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elif request.status == RequestStatus.PREEMPTED:
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scheduled_resumed_reqs.append(request)
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else:
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raise RuntimeError(f"Invalid request status: {request.status}")
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if self.lora_config and request.lora_request:
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scheduled_loras.add(request.lora_request.lora_int_id)
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req_to_new_block_ids[request.request_id] = [
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b.block_id for b in computed_blocks + new_blocks
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]
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# Update request info.
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num_scheduled_tokens[request.request_id] = num_new_tokens
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token_budget -= num_new_tokens
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request.status = RequestStatus.RUNNING
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request.num_computed_tokens = num_computed_tokens
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# Put back any skipped requests at the head of the waiting queue
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if skipped_waiting_requests:
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self.waiting.extendleft(skipped_waiting_requests)
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# If no prefill requests are scheduled,
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# Schedule decode requests next.
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if len(self.scheduled_req_ids) == 0:
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req_index = 0
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while req_index < len(self.running) and token_budget > 0:
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request = self.running[req_index]
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if request.request_id in self.scheduled_req_ids:
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# This request has already been scheduled.
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req_index += 1
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continue
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num_new_tokens = (request.num_tokens_with_spec -
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request.num_computed_tokens)
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if (0 < self.scheduler_config.long_prefill_token_threshold <
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num_new_tokens):
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num_new_tokens = (
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self.scheduler_config.long_prefill_token_threshold)
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num_new_tokens = min(num_new_tokens, token_budget)
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assert num_new_tokens == 1
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while True:
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new_blocks = self.kv_cache_manager.allocate_slots(
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request, num_new_tokens)
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if new_blocks is None:
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# The request cannot be scheduled.
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# Preempt the lowest-priority request.
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preempted_req = self.running.pop()
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self.kv_cache_manager.free(preempted_req)
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preempted_req.status = RequestStatus.PREEMPTED
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preempted_req.num_computed_tokens = 0
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self.waiting.appendleft(preempted_req)
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preempted_reqs.append(preempted_req)
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if preempted_req == request:
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# No more request to preempt.
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can_schedule = False
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break
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else:
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# The request can be scheduled.
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can_schedule = True
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break
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if not can_schedule:
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break
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assert new_blocks is not None
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# Schedule the request.
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scheduled_running_reqs.append(request)
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self.scheduled_req_ids.add(request.request_id)
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req_to_new_block_ids[request.request_id] = [
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b.block_id for b in new_blocks
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]
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num_scheduled_tokens[request.request_id] = num_new_tokens
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token_budget -= num_new_tokens
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req_index += 1
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# Speculative decode related.
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if request.spec_token_ids:
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num_scheduled_spec_tokens = (num_new_tokens +
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request.num_computed_tokens -
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request.num_tokens)
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if num_scheduled_spec_tokens > 0:
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# Trim spec_token_ids list to num_scheduled_spec_tokens.
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del request.spec_token_ids[num_scheduled_spec_tokens:]
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scheduled_spec_decode_tokens[request.request_id] = (
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request.spec_token_ids)
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# Check if the scheduling constraints are satisfied.
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total_num_scheduled_tokens = sum(num_scheduled_tokens.values())
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assert total_num_scheduled_tokens <= self.max_num_scheduled_tokens
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assert token_budget >= 0
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assert len(self.running) <= self.max_num_running_reqs
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assert len(scheduled_new_reqs) + len(scheduled_resumed_reqs) + len(
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scheduled_running_reqs) <= len(self.running)
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# Get the longest common prefix among all requests in the running queue.
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# This can be potentially used for cascade attention.
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num_common_prefix_blocks = 0
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if self.running:
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any_request = self.running[0]
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num_common_prefix_blocks = (
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self.kv_cache_manager.get_num_common_prefix_blocks(
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any_request, len(self.running)))
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# Construct the scheduler output.
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new_reqs_data = [
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NewRequestData.from_request(req,
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req_to_new_block_ids[req.request_id])
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for req in scheduled_new_reqs
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]
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resumed_reqs_data = [
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self._make_cached_request_data(
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req,
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num_scheduled_tokens[req.request_id],
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len(scheduled_spec_decode_tokens.get(req.request_id, ())),
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req_to_new_block_ids[req.request_id],
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resumed_from_preemption=True,
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) for req in scheduled_resumed_reqs
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]
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running_reqs_data = [
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self._make_cached_request_data(
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req,
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num_scheduled_tokens[req.request_id],
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len(scheduled_spec_decode_tokens.get(req.request_id, ())),
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req_to_new_block_ids[req.request_id],
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resumed_from_preemption=False,
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) for req in scheduled_running_reqs
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]
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scheduler_output = SchedulerOutput(
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scheduled_new_reqs=new_reqs_data,
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scheduled_cached_reqs=resumed_reqs_data + running_reqs_data,
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num_scheduled_tokens=num_scheduled_tokens,
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total_num_scheduled_tokens=total_num_scheduled_tokens,
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scheduled_spec_decode_tokens=scheduled_spec_decode_tokens,
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scheduled_encoder_inputs={},
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num_common_prefix_blocks=num_common_prefix_blocks,
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# finished_req_ids is an existing state in the scheduler,
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# instead of being newly scheduled in this step.
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# It contains the request IDs that are finished in between
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# the previous and the current steps.
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finished_req_ids=self.finished_req_ids, # type: ignore
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free_encoder_input_ids=self.encoder_cache_manager.get_freed_ids(),
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structured_output_request_ids={},
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grammar_bitmask=None,
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)
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# Advance the number of computed tokens for the request AFTER
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# the request is scheduled.
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# 1. The scheduler_output of the current step has to include the
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# original number of scheduled tokens to determine input IDs.
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# 2. Advance the number of computed tokens here allowing us to
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# schedule the prefill request again immediately in the next
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# scheduling step.
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# 3. If some tokens (e.g. spec tokens) are rejected later, the number of
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# computed tokens will be adjusted in update_from_output.
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for req_id, num_scheduled_token in num_scheduled_tokens.items():
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self.requests[req_id].num_computed_tokens += num_scheduled_token
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self.finished_req_ids = set() # type: ignore
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return scheduler_output
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def _check_watermark_for_prefill(self,
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request,
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num_new_tokens,
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computed_blocks,
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watermark=0.01):
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computed_blocks = computed_blocks or []
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watermark_blocks = self.kv_cache_config.num_blocks * watermark
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num_computed_tokens = (request.num_computed_tokens +
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len(computed_blocks) * self.block_size)
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num_required_blocks = cdiv(num_new_tokens + num_computed_tokens,
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self.block_size)
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req_blocks = self.kv_cache_manager.req_to_blocks[request.request_id]
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num_new_blocks = (num_required_blocks - len(req_blocks) -
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len(computed_blocks))
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num_evictable_computed_blocks = sum(1 for blk in computed_blocks
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if blk.ref_cnt == 0)
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# If number of free blocks is less than water mark after allocating, don't allocate.
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if (self.kv_cache_manager.block_pool.get_num_free_blocks() -
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num_evictable_computed_blocks -
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num_new_blocks) < watermark_blocks:
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return False
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return True
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def _get_prompt_limit(self, request: Request) -> int:
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if (self.scheduler_config.chunked_prefill_enabled
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and not self.scheduler_config.is_multi_step):
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prompt_limit = self.scheduler_config.max_model_len
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else:
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prompt_limit = min(
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self.scheduler_config.max_model_len,
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self.scheduler_config.max_num_batched_tokens,
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)
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# Model is fine tuned with long context. Return the fine tuned max_len.
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if request.lora_request and request.lora_request.long_lora_max_len:
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assert prompt_limit <= request.lora_request.long_lora_max_len
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return request.lora_request.long_lora_max_len
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else:
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return prompt_limit
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