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[Optimization] Optimize ttft for prefill pd (#6680)

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* optimize ttft

* fix

* fix

* fix ci

* fix ci

* fix

* fix bug

* fix

* add comments

* fix ci

* fix

* fix ci

* fix format

* update according to review

* add comment

* fix

* fix format
This commit is contained in:
chenjian
2026-03-30 20:36:23 +08:00
committed by GitHub
parent 05f2d95729
commit 6727df8286
11 changed files with 134 additions and 135 deletions
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fastdeploy/scheduler/dp_scheduler.py
+11 -44
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@@ -23,7 +23,7 @@ from typing import Dict, List, Optional
from fastdeploy.engine.request import Request, RequestOutput
from fastdeploy.scheduler.data import ScheduledResponse
from fastdeploy.scheduler.local_scheduler import LocalScheduler
from fastdeploy.utils import envs, get_logger
from fastdeploy.utils import get_logger
class DPLocalScheduler(LocalScheduler):
@@ -131,52 +131,19 @@ class DPLocalScheduler(LocalScheduler):
Returns:
List of Request objects ready for processing
"""
if available_blocks <= reserved_output_blocks or batch < 1:
self.scheduler_logger.debug(
f"Scheduler's resource are insufficient: available_blocks={available_blocks} "
f"reserved_output_blocks={reserved_output_blocks} batch={batch} "
f"max_num_batched_tokens={max_num_batched_tokens}"
)
return []
required_total_blocks = 0
current_prefill_tokens = 0
start_batch_time = time.time()
# DP scheduler is used in V1, there is no need to manage request fetching in the scheduler, resource_manager_v1 will do that.
requests: List[Request] = []
with self.requests_not_empty:
while True:
batch_ids = self.requests_not_empty.wait_for(
lambda: self.ids[self.ids_read_cursor : self.ids_read_cursor + batch],
0.005,
)
if batch_ids:
for request_id in batch_ids:
request = self.requests[request_id]
required_input_blocks = self.calc_required_blocks(request.prompt_tokens_ids_len, block_size)
current_prefill_tokens += request.prompt_tokens_ids_len
required_total_blocks += required_input_blocks + reserved_output_blocks
if required_total_blocks > available_blocks:
break
requests.append(request.raw)
self.ids_read_cursor += 1
start_batch_time = time.time()
if current_prefill_tokens > max_num_batched_tokens:
break
if len(requests) >= batch:
break
if (
(current_prefill_tokens > max_num_batched_tokens)
or (len(requests) >= batch)
or (time.time() - start_batch_time > envs.FD_EP_BATCHED_TOKEN_TIMEOUT)
):
break
if batch_ids:
if len(batch_ids) > 0 and len(requests) == 0:
self.scheduler_logger.debug(
f"Scheduler has put all just-pulled request into the queue: {len(batch_ids)}"
)
batch_ids = self.requests_not_empty.wait_for(
lambda: self.ids[self.ids_read_cursor : self.ids_read_cursor + 1],
0.005,
)
if batch_ids:
for request_id in batch_ids:
request = self.requests[request_id]
requests.append(request.raw)
self.ids_read_cursor += 1
if len(requests) > 0:
self.scheduler_logger.info(