168ad600b5
### What this PR does / why we need it?
For offline scenarios, adjust the scheduling process to prioritize the
prefill phase of all requests, then process the decode phase of all
requests.
### How was this patch tested?
```
max_num_seqs=24,
additional_config={
"ascend_scheduler_config":{
"enabled": True,
"enable_pd_transfer": True,
"decode_max_num_seqs": 24,
"enable_chunked_prefill": False
}
},
```
| input | output | num prompts | max_num_seqs | dp | tp | scheduler |
tps |
| ------ | ------ | ---------- | ---------------- | ---- | ---- |
---------------- | --------------- |
| dapo-math-17K | 2K | 384 | 24 | 2 | 1 | v1 | 234.06 |
| dapo-math-17K | 2K | 384 | 24 | 2 | 1 | pd transfer | 239.59(+2.4%) |
| dapo-math-17K| 2K | 384 | 24 | 4 | 1 | v1 | 222.85 |
| dapo-math-17K| 2K | 384 | 24 | 4 | 1 | pd transfer | 225.81(+1.3%) |
- vLLM version: v0.10.1.1
- vLLM main:
6fb2788163
---------
Signed-off-by: CaranLic <740821011@qq.com>
89 lines
3.8 KiB
Python
89 lines
3.8 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 dataclasses import dataclass, fields
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from typing import Type, Union
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from vllm.config import SchedulerConfig
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@dataclass
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class AscendSchedulerConfig(SchedulerConfig):
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enable_chunked_prefill: bool = False
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policy: str = "fcfs"
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num_scheduler_steps: int = 1
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scheduler_cls: Union[str, Type[object]] = (
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"vllm_ascend.core.scheduler.AscendScheduler")
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enable_pd_transfer: bool = False
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decode_max_num_seqs: int = 0
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@classmethod
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def initialize_from_config(
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cls,
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vllm_scheduler_config: SchedulerConfig,
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ascend_scheduler_config,
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):
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scheduler_config = {
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field.name: getattr(vllm_scheduler_config, field.name)
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for field in fields(vllm_scheduler_config) if field.init
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}
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# Override default values into original SchedulerConfig
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scheduler_config["enable_chunked_prefill"] = False
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scheduler_config["policy"] = "fcfs"
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scheduler_config["num_scheduler_steps"] = 1
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scheduler_config["scheduler_cls"] = (
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"vllm_ascend.core.scheduler.AscendScheduler")
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scheduler_config["enable_pd_transfer"] = False
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scheduler_config["decode_max_num_seqs"] = 0
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# Override params in original SchedulerConfig with params in ascend_scheduler_config
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for k, _ in scheduler_config.items():
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if hasattr(ascend_scheduler_config, k):
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scheduler_config[k] = getattr(ascend_scheduler_config, k)
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return cls(**scheduler_config)
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def __post_init__(self) -> None:
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self.max_num_encoder_input_tokens = self.max_num_batched_tokens
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self.encoder_cache_size = self.max_num_batched_tokens
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self.chunked_prefill_enabled = self.enable_chunked_prefill
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if (self.max_num_batched_tokens < self.max_model_len
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and not self.chunked_prefill_enabled):
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raise ValueError(
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"Ascend scheduler is enabled without chunked prefill feature. "
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f"Argument max_num_batched_tokens ({self.max_num_batched_tokens}) is "
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f"smaller than max_model_len ({self.max_model_len}). "
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"This effectively limits the maximum sequence length to "
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"max_num_batched_tokens and makes vLLM reject longer "
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"sequences. Please increase max_num_batched_tokens or "
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"decrease max_model_len.")
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if self.policy != "fcfs":
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raise NotImplementedError(
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f"currently AscendScheduler only supports fcfs policy, got {self.policy}"
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)
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if self.is_multimodal_model:
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raise NotImplementedError(
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"currently AscendScheduler only supports LLM models.")
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if self.num_scheduler_steps > 1:
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raise NotImplementedError(
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"currently AscendScheduler doesn't support multi-step.")
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if self.send_delta_data:
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raise NotImplementedError(
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"currently AscendScheduler doesn't support send_delta_data.")
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if self.delay_factor > 0:
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raise NotImplementedError(
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"currently AscendScheduler doesn't support scheduler_delay_factor."
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)
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