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[Lint]Style: Convert vllm-ascend/ to ruff format(Batch #3) (#5978)

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### What this PR does / why we need it?
**Scope of Changes**:
| File Path |
| :--- |
| `vllm_ascend/attention/mla_v1.py` |
| `vllm_ascend/attention/sfa_v1.py` |
| `vllm_ascend/core/recompute_scheduler.py` |
| `vllm_ascend/core/scheduler_dynamic_batch.py` |
| `vllm_ascend/distributed/device_communicators/npu_communicator.py` |
| `vllm_ascend/distributed/device_communicators/pyhccl.py` |
| `vllm_ascend/distributed/device_communicators/pyhccl_wrapper.py` |

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.13.0
- vLLM main:
2c24bc6996

---------

Signed-off-by: MrZ20 <2609716663@qq.com>
Co-authored-by: Soren <user@SorendeMac-mini.local>
This commit is contained in:
SILONG ZENG
2026-01-24 22:10:18 +08:00
committed by GitHub
parent 4e53c1d900
commit 7faa6878a6
9 changed files with 953 additions and 1148 deletions
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239
vllm_ascend/core/recompute_scheduler.py
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@@ -21,26 +21,21 @@ from __future__ import annotations
import time
from collections import defaultdict
from dataclasses import dataclass, fields
from typing import Type, Union
from vllm._bc_linter import bc_linter_include
from vllm.config import SchedulerConfig, VllmConfig
from vllm.distributed.ec_transfer.ec_connector.base import ECConnectorMetadata
from vllm.distributed.kv_events import KVEventBatch
from vllm.distributed.kv_transfer.kv_connector.v1.base import \
KVConnectorMetadata
from vllm.distributed.kv_transfer.kv_connector.v1.metrics import \
KVConnectorStats
from vllm.distributed.kv_transfer.kv_connector.v1.base import KVConnectorMetadata
from vllm.distributed.kv_transfer.kv_connector.v1.metrics import KVConnectorStats
from vllm.logger import init_logger
from vllm.v1.core.kv_cache_manager import KVCacheBlocks
from vllm.v1.core.sched.async_scheduler import AsyncScheduler
from vllm.v1.core.sched.output import NewRequestData, SchedulerOutput
from vllm.v1.core.sched.request_queue import (SchedulingPolicy,
create_request_queue)
from vllm.v1.core.sched.request_queue import SchedulingPolicy, create_request_queue
from vllm.v1.core.sched.scheduler import Scheduler
from vllm.v1.core.sched.utils import check_stop, remove_all
from vllm.v1.engine import (EngineCoreEventType, EngineCoreOutput,
EngineCoreOutputs, FinishReason)
from vllm.v1.engine import EngineCoreEventType, EngineCoreOutput, EngineCoreOutputs, FinishReason
from vllm.v1.outputs import ModelRunnerOutput
from vllm.v1.request import Request, RequestStatus
from vllm.v1.spec_decode.metrics import SpecDecodingStats
@@ -51,26 +46,22 @@ logger = init_logger(__name__)
@dataclass
class RecomputeSchedulerConfig(SchedulerConfig):
scheduler_cls: Union[str, Type[object]] = (
"vllm_ascend.core.recompute_scheduler.RecomputeScheduler")
scheduler_cls: str | type[object] = "vllm_ascend.core.recompute_scheduler.RecomputeScheduler"
@classmethod
def initialize_from_config(cls, vllm_config: VllmConfig):
vllm_scheduler_config = vllm_config.scheduler_config
scheduler_config = {
field.name: getattr(vllm_scheduler_config, field.name)
for field in fields(vllm_scheduler_config) if field.init
for field in fields(vllm_scheduler_config)
if field.init
}
if vllm_scheduler_config.async_scheduling:
scheduler_config["scheduler_cls"] = (
"vllm_ascend.core.recompute_scheduler.AsyncRecomputeScheduler")
scheduler_config["scheduler_cls"] = "vllm_ascend.core.recompute_scheduler.AsyncRecomputeScheduler"
else:
scheduler_config["scheduler_cls"] = (
"vllm_ascend.core.recompute_scheduler.RecomputeScheduler")
scheduler_config[
"max_model_len"] = vllm_config.model_config.max_model_len
scheduler_config[
"is_encoder_decoder"] = vllm_config.model_config.is_encoder_decoder
scheduler_config["scheduler_cls"] = "vllm_ascend.core.recompute_scheduler.RecomputeScheduler"
scheduler_config["max_model_len"] = vllm_config.model_config.max_model_len
scheduler_config["is_encoder_decoder"] = vllm_config.model_config.is_encoder_decoder
return cls(**scheduler_config)
@@ -125,33 +116,32 @@ class RecomputeScheduler(Scheduler):
while req_index < len(self.running) and token_budget > 0:
request = self.running[req_index]
if (request.num_output_placeholders > 0
# This is (num_computed_tokens + 1) - (num_output_placeholders - 1).
# Since output placeholders are also included in the computed tokens
# count, we subtract (num_output_placeholders - 1) to remove any draft
# tokens, so that we can be sure no further steps are needed even if
# they are all rejected.
and request.num_computed_tokens + 2 -
request.num_output_placeholders
>= request.num_prompt_tokens + request.max_tokens):
if (
request.num_output_placeholders > 0
# This is (num_computed_tokens + 1) - (num_output_placeholders - 1).
# Since output placeholders are also included in the computed tokens
# count, we subtract (num_output_placeholders - 1) to remove any draft
# tokens, so that we can be sure no further steps are needed even if
# they are all rejected.
and request.num_computed_tokens + 2 - request.num_output_placeholders
>= request.num_prompt_tokens + request.max_tokens
):
# Async scheduling: Avoid scheduling an extra step when we are sure that
# the previous step has reached request.max_tokens. We don't schedule
# partial draft tokens since this prevents uniform decode optimizations.
req_index += 1
continue
num_new_tokens = (request.num_tokens_with_spec +
request.num_output_placeholders -
request.num_computed_tokens)
num_new_tokens = (
request.num_tokens_with_spec + request.num_output_placeholders - request.num_computed_tokens
)
if 0 < self.scheduler_config.long_prefill_token_threshold < num_new_tokens:
num_new_tokens = self.scheduler_config.long_prefill_token_threshold
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 - 1 - request.num_computed_tokens)
num_new_tokens = min(num_new_tokens, self.max_model_len - 1 - request.num_computed_tokens)
# Schedule encoder inputs.
encoder_inputs_to_schedule = None
@@ -209,9 +199,10 @@ class RecomputeScheduler(Scheduler):
recomputed_req = self.running.pop()
self.kv_cache_manager.free(recomputed_req)
recomputed_reqs.append(
RecomputeReqInfo(recomputed_req.request_id,
recomputed_req.output_token_ids,
recomputed_req.client_index))
RecomputeReqInfo(
recomputed_req.request_id, recomputed_req.output_token_ids, recomputed_req.client_index
)
)
if recomputed_req == request:
break
else:
@@ -223,28 +214,23 @@ class RecomputeScheduler(Scheduler):
self.running.remove(preempted_req)
if preempted_req in scheduled_running_reqs:
scheduled_running_reqs.remove(preempted_req)
token_budget += num_scheduled_tokens[
preempted_req.request_id]
token_budget += num_scheduled_tokens[preempted_req.request_id]
req_to_new_blocks.pop(preempted_req.request_id)
num_scheduled_tokens.pop(
preempted_req.request_id)
scheduled_spec_decode_tokens.pop(
preempted_req.request_id, None)
preempted_encoder_inputs = scheduled_encoder_inputs.pop(
preempted_req.request_id, None)
num_scheduled_tokens.pop(preempted_req.request_id)
scheduled_spec_decode_tokens.pop(preempted_req.request_id, None)
preempted_encoder_inputs = scheduled_encoder_inputs.pop(preempted_req.request_id, None)
if preempted_encoder_inputs:
# Restore encoder compute budget if the preempted
# request had encoder inputs scheduled in this step.
num_embeds_to_restore = sum(
preempted_req.get_num_encoder_embeds(i)
for i in preempted_encoder_inputs)
preempted_req.get_num_encoder_embeds(i) for i in preempted_encoder_inputs
)
encoder_compute_budget += num_embeds_to_restore
req_index -= 1
else:
preempted_req = self.running.pop()
self._preempt_request(preempted_req,
scheduled_timestamp)
self._preempt_request(preempted_req, scheduled_timestamp)
preempted_reqs.append(preempted_req)
if preempted_req == request:
# No more request to preempt. Cannot schedule this request.
@@ -263,23 +249,20 @@ class RecomputeScheduler(Scheduler):
# Speculative decode related.
if request.spec_token_ids:
num_scheduled_spec_tokens = (num_new_tokens +
request.num_computed_tokens -
request.num_tokens -
request.num_output_placeholders)
num_scheduled_spec_tokens = (
num_new_tokens + request.num_computed_tokens - request.num_tokens - request.num_output_placeholders
)
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)
scheduled_spec_decode_tokens[request.request_id] = request.spec_token_ids
# New spec tokens will be set in `update_draft_token_ids` before the
# next step when applicable.
request.spec_token_ids = []
# Encoder-related.
if encoder_inputs_to_schedule:
scheduled_encoder_inputs[request.request_id] = (
encoder_inputs_to_schedule)
scheduled_encoder_inputs[request.request_id] = encoder_inputs_to_schedule
# Allocate the encoder cache.
for i in encoder_inputs_to_schedule:
self.encoder_cache_manager.allocate(request, i)
@@ -294,8 +277,10 @@ class RecomputeScheduler(Scheduler):
scheduled_loras: set[int] = set()
if self.lora_config:
scheduled_loras = set(
req.lora_request.lora_int_id for req in scheduled_running_reqs
if req.lora_request and req.lora_request.lora_int_id > 0)
req.lora_request.lora_int_id
for req in scheduled_running_reqs
if req.lora_request and req.lora_request.lora_int_id > 0
)
assert len(scheduled_loras) <= self.lora_config.max_loras
# Use a temporary RequestQueue to collect requests that need to be
@@ -337,9 +322,14 @@ class RecomputeScheduler(Scheduler):
# 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)):
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.
self.waiting.pop_request()
skipped_waiting_requests.prepend_request(request)
@@ -351,14 +341,15 @@ class RecomputeScheduler(Scheduler):
# Get already-cached tokens.
if request.num_computed_tokens == 0:
# Get locally-cached tokens.
new_computed_blocks, num_new_local_computed_tokens = (
self.kv_cache_manager.get_computed_blocks(request))
new_computed_blocks, num_new_local_computed_tokens = self.kv_cache_manager.get_computed_blocks(
request
)
# Get externally-cached tokens if using a KVConnector.
if self.connector is not None:
ext_tokens, load_kv_async = (
self.connector.get_num_new_matched_tokens(
request, num_new_local_computed_tokens))
ext_tokens, load_kv_async = self.connector.get_num_new_matched_tokens(
request, num_new_local_computed_tokens
)
if ext_tokens is None:
# The request cannot be scheduled because
@@ -372,8 +363,7 @@ class RecomputeScheduler(Scheduler):
num_external_computed_tokens = ext_tokens
# Total computed tokens (local + external).
num_computed_tokens = (num_new_local_computed_tokens +
num_external_computed_tokens)
num_computed_tokens = num_new_local_computed_tokens + num_external_computed_tokens
else:
# KVTransfer: WAITING reqs have num_computed_tokens > 0
# after async KV recvs are completed.
@@ -401,8 +391,7 @@ class RecomputeScheduler(Scheduler):
# chunked prefill has to be enabled explicitly to allow
# pooling requests to be chunked
if (not self.scheduler_config.enable_chunked_prefill
and num_new_tokens > token_budget):
if not self.scheduler_config.enable_chunked_prefill and num_new_tokens > token_budget:
# If chunked_prefill is disabled,
# we can stop the scheduling here.
break
@@ -433,9 +422,7 @@ class RecomputeScheduler(Scheduler):
# extra block gets allocated which
# creates a mismatch between the number
# of local and remote blocks.
effective_lookahead_tokens = (0 if request.num_computed_tokens
== 0 else
self.num_lookahead_tokens)
effective_lookahead_tokens = 0 if request.num_computed_tokens == 0 else self.num_lookahead_tokens
# Determine if we need to allocate cross-attention blocks.
if self.is_encoder_decoder and request.has_encoder_inputs:
@@ -443,8 +430,7 @@ class RecomputeScheduler(Scheduler):
# always padded to the maximum length. If we support other
# encoder-decoder models, this will need to be updated if we
# want to only allocate what is needed.
num_encoder_tokens = (
self.scheduler_config.max_num_encoder_input_tokens)
num_encoder_tokens = self.scheduler_config.max_num_encoder_input_tokens
else:
num_encoder_tokens = 0
@@ -488,20 +474,17 @@ class RecomputeScheduler(Scheduler):
req_index += 1
self.running.append(request)
if self.log_stats:
request.record_event(EngineCoreEventType.SCHEDULED,
scheduled_timestamp)
request.record_event(EngineCoreEventType.SCHEDULED, scheduled_timestamp)
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}")
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_blocks[request.request_id] = (
self.kv_cache_manager.get_blocks(request.request_id))
req_to_new_blocks[request.request_id] = self.kv_cache_manager.get_blocks(request.request_id)
num_scheduled_tokens[request.request_id] = num_new_tokens
token_budget -= num_new_tokens
request.status = RequestStatus.RUNNING
@@ -511,8 +494,7 @@ class RecomputeScheduler(Scheduler):
request.num_cached_tokens = num_computed_tokens
# Encoder-related.
if encoder_inputs_to_schedule:
scheduled_encoder_inputs[request.request_id] = (
encoder_inputs_to_schedule)
scheduled_encoder_inputs[request.request_id] = encoder_inputs_to_schedule
# Allocate the encoder cache.
for i in encoder_inputs_to_schedule:
self.encoder_cache_manager.allocate(request, i)
@@ -522,8 +504,7 @@ class RecomputeScheduler(Scheduler):
for i in external_load_encoder_input:
self.encoder_cache_manager.allocate(request, i)
if self.ec_connector is not None:
self.ec_connector.update_state_after_alloc(
request, i)
self.ec_connector.update_state_after_alloc(request, i)
# Put back any skipped requests at the head of the waiting queue
if skipped_waiting_requests:
self.waiting.prepend_requests(skipped_waiting_requests)
@@ -537,20 +518,15 @@ class RecomputeScheduler(Scheduler):
# Since some requests in the RUNNING queue may not be scheduled in
# this step, the total number of scheduled requests can be smaller than
# len(self.running).
assert len(scheduled_new_reqs) + len(scheduled_resumed_reqs) + len(
scheduled_running_reqs) <= len(self.running)
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] * len(
self.kv_cache_config.kv_cache_groups)
with record_function_or_nullcontext(
"schedule: get_num_common_prefix_blocks"):
num_common_prefix_blocks = [0] * len(self.kv_cache_config.kv_cache_groups)
with record_function_or_nullcontext("schedule: get_num_common_prefix_blocks"):
if self.running:
any_request = self.running[0]
num_common_prefix_blocks = (
self.kv_cache_manager.get_num_common_prefix_blocks(
any_request.request_id))
num_common_prefix_blocks = self.kv_cache_manager.get_num_common_prefix_blocks(any_request.request_id)
# Construct the scheduler output.
if self.use_v2_model_runner:
@@ -561,17 +537,16 @@ class RecomputeScheduler(Scheduler):
req,
req_to_new_blocks[req.request_id].get_block_ids(),
req._all_token_ids,
) for req in scheduled_new_reqs
)
for req in scheduled_new_reqs
]
else:
new_reqs_data = [
NewRequestData.from_request(
req, req_to_new_blocks[req.request_id].get_block_ids())
NewRequestData.from_request(req, req_to_new_blocks[req.request_id].get_block_ids())
for req in scheduled_new_reqs
]
with record_function_or_nullcontext(
"schedule: make_cached_request_data"):
with record_function_or_nullcontext("schedule: make_cached_request_data"):
cached_reqs_data = self._make_cached_request_data(
scheduled_running_reqs,
scheduled_resumed_reqs,
@@ -592,15 +567,13 @@ class RecomputeScheduler(Scheduler):
scheduled_spec_decode_tokens=scheduled_spec_decode_tokens,
scheduled_encoder_inputs=scheduled_encoder_inputs,
num_common_prefix_blocks=num_common_prefix_blocks,
preempted_req_ids={req.request_id
for req in preempted_reqs},
preempted_req_ids={req.request_id for req in preempted_reqs},
# 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,
free_encoder_mm_hashes=self.encoder_cache_manager.
get_freed_mm_hashes(),
free_encoder_mm_hashes=self.encoder_cache_manager.get_freed_mm_hashes(),
recomputed_reqs=recomputed_reqs,
)
@@ -609,14 +582,12 @@ class RecomputeScheduler(Scheduler):
# 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: KVConnectorMetadata = self.connector.build_connector_meta(
scheduler_output)
meta: KVConnectorMetadata = self.connector.build_connector_meta(scheduler_output)
scheduler_output.kv_connector_metadata = meta
# Build the connector meta for ECConnector
if self.ec_connector is not None:
ec_meta: ECConnectorMetadata = self.ec_connector.build_connector_meta(
scheduler_output)
ec_meta: ECConnectorMetadata = self.ec_connector.build_connector_meta(scheduler_output)
scheduler_output.ec_connector_metadata = ec_meta
with record_function_or_nullcontext("schedule: update_after_schedule"):
@@ -639,8 +610,8 @@ class RecomputeScheduler(Scheduler):
outputs: dict[int, list[EngineCoreOutput]] = defaultdict(list)
spec_decoding_stats: SpecDecodingStats | None = None
kv_connector_stats: KVConnectorStats | None = (
kv_connector_output.kv_connector_stats
if kv_connector_output else None)
kv_connector_output.kv_connector_stats if kv_connector_output else None
)
if kv_connector_stats and self.connector:
kv_stats = self.connector.get_kv_connector_stats()
if kv_stats:
@@ -651,8 +622,7 @@ class RecomputeScheduler(Scheduler):
# These blocks contain externally computed tokens that failed to
# load. Identify affected requests and adjust their computed token
# count to trigger recomputation of the invalid blocks.
failed_kv_load_req_ids = self._handle_invalid_blocks(
kv_connector_output.invalid_block_ids)
failed_kv_load_req_ids = self._handle_invalid_blocks(kv_connector_output.invalid_block_ids)
# return recomputed requests as EngineCoreOutput
if scheduler_output.recomputed_reqs is not None:
@@ -663,7 +633,8 @@ class RecomputeScheduler(Scheduler):
finish_reason=FinishReason.STOP,
new_token_ids=[req_info.output_token_ids[-1]],
stop_reason="recomputed",
))
)
)
# NOTE(woosuk): As len(num_scheduled_tokens) can be up to 1K or more,
# the below loop can be a performance bottleneck. We should do our best
@@ -683,11 +654,9 @@ class RecomputeScheduler(Scheduler):
continue
req_index = model_runner_output.req_id_to_index[req_id]
generated_token_ids = (sampled_token_ids[req_index]
if sampled_token_ids else [])
generated_token_ids = sampled_token_ids[req_index] if sampled_token_ids else []
scheduled_spec_token_ids = (
scheduler_output.scheduled_spec_decode_tokens.get(req_id))
scheduled_spec_token_ids = scheduler_output.scheduled_spec_decode_tokens.get(req_id)
if scheduled_spec_token_ids:
num_draft_tokens = len(scheduled_spec_token_ids)
num_accepted = len(generated_token_ids) - 1
@@ -717,15 +686,13 @@ class RecomputeScheduler(Scheduler):
# Check for stop and update request status.
if new_token_ids:
new_token_ids, stopped = self._update_request_with_output(
request, new_token_ids)
new_token_ids, stopped = self._update_request_with_output(request, new_token_ids)
# Stop checking for pooler models.
pooler_output = None
if pooler_outputs:
pooler_output = pooler_outputs[req_index]
stopped = check_stop(request, self.max_model_len,
pooler_output)
stopped = check_stop(request, self.max_model_len, pooler_output)
if stopped:
kv_transfer_params = self._free_request(request)
@@ -735,19 +702,14 @@ class RecomputeScheduler(Scheduler):
stopped_preempted_reqs.add(request)
# Extract sample logprobs if needed.
if (request.sampling_params is not None
and request.sampling_params.logprobs is not None
and logprobs):
new_logprobs = logprobs.slice_request(req_index,
len(new_token_ids))
if request.sampling_params is not None and request.sampling_params.logprobs is not None and logprobs:
new_logprobs = logprobs.slice_request(req_index, len(new_token_ids))
if new_token_ids and self.structured_output_manager.should_advance(
request):
if new_token_ids and self.structured_output_manager.should_advance(request):
struct_output_request = request.structured_output_request
assert struct_output_request is not None
assert struct_output_request.grammar is not None
struct_output_request.grammar.accept_tokens(
req_id, new_token_ids)
struct_output_request.grammar.accept_tokens(req_id, new_token_ids)
if num_nans_in_logits is not None and req_id in num_nans_in_logits:
request.num_nans_in_logits = num_nans_in_logits[req_id]
@@ -770,7 +732,8 @@ class RecomputeScheduler(Scheduler):
trace_headers=request.trace_headers,
num_cached_tokens=request.num_cached_tokens,
num_nans_in_logits=request.num_nans_in_logits,
))
)
)
else:
# Invariant: EngineCore returns no partial prefill outputs.
assert not prompt_logprobs_tensors
@@ -805,10 +768,7 @@ class RecomputeScheduler(Scheduler):
# Create EngineCoreOutputs for all clients that have requests with
# outputs in this step.
engine_core_outputs = {
client_index: EngineCoreOutputs(outputs=outs)
for client_index, outs in outputs.items()
}
engine_core_outputs = {client_index: EngineCoreOutputs(outputs=outs) for client_index, outs in outputs.items()}
finished_req_ids = self.finished_req_ids_dict
if finished_req_ids:
@@ -819,12 +779,10 @@ class RecomputeScheduler(Scheduler):
if (eco := engine_core_outputs.get(client_index)) is not None:
eco.finished_requests = finished_set
else:
engine_core_outputs[client_index] = EngineCoreOutputs(
finished_requests=finished_set)
engine_core_outputs[client_index] = EngineCoreOutputs(finished_requests=finished_set)
finished_req_ids.clear()
if (stats := self.make_stats(spec_decoding_stats,
kv_connector_stats)) is not None:
if (stats := self.make_stats(spec_decoding_stats, kv_connector_stats)) is not None:
# Return stats to only one of the front-ends.
if (eco := next(iter(engine_core_outputs.values()), None)) is None:
# We must return the stats even if there are no request
@@ -836,6 +794,5 @@ class RecomputeScheduler(Scheduler):
class AsyncRecomputeScheduler(AsyncScheduler, RecomputeScheduler):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)