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109 lines
4.6 KiB
Python
109 lines
4.6 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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MAX_INT = 2147483647
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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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max_long_partial_prefills: int = 1
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long_prefill_token_threshold: int = MAX_INT
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policy: str = "fcfs"
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scheduler_cls: Union[str, Type[object]] = (
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"vllm_npu.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["max_long_partial_prefills"] = None
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scheduler_config["long_prefill_token_threshold"] = None
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scheduler_config["policy"] = "fcfs"
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scheduler_config["scheduler_cls"] = (
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"vllm_npu.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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# concurrent partial prefills. Default is 1 meaning not enabled.
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if self.max_long_partial_prefills is None:
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self.max_long_partial_prefills = 1
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self.long_prefill_token_threshold = MAX_INT
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if self.long_prefill_token_threshold is None or \
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self.long_prefill_token_threshold <= 0:
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if self.max_model_len is None:
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self.long_prefill_token_threshold = MAX_INT
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else:
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self.long_prefill_token_threshold = \
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max(1, int(self.max_model_len * 0.04))
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if self.max_long_partial_prefills < 0:
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raise ValueError(
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f"max_long_partial_prefills must be non-negative, but got "
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f"{self.max_long_partial_prefills}")
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if self.long_prefill_token_threshold < 0:
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raise ValueError(
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f"long_prefill_token_threshold must be non-negative, but got "
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f"{self.long_prefill_token_threshold}")
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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.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 getattr(self, "scheduler_delay_factor", 0) > 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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