drop ascend scheduler (#4498)

Ascend scheduler was added for non chunk prefill case before, since that
the npu ops didn't work well with chunked prefill.

Now the ops with chunked prefill work better, it's time to remove the
ascend scheduler to use vLLM default scheduler.

- vLLM version: v0.11.2

---------

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

vllm_ascend/core/schedule_config.py

@@ -1,105 +0,0 @@
-#
-# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# This file is a part of the vllm-ascend project.
-#
-
-from dataclasses import dataclass, fields
-from typing import Type, Union
-
-from vllm.config import SchedulerConfig
-
-MAX_INT = 2147483647
-
-
-@dataclass
-class AscendSchedulerConfig(SchedulerConfig):
-    enable_chunked_prefill: bool = False
-    max_long_partial_prefills: int = 1
-    long_prefill_token_threshold: int = MAX_INT
-    policy: str = "fcfs"
-    scheduler_cls: Union[str, Type[object]] = (
-        "vllm_ascend.core.scheduler.AscendScheduler")
-    enable_pd_transfer: bool = False
-    decode_max_num_seqs: int = 0
-
-    @classmethod
-    def initialize_from_config(
-        cls,
-        vllm_scheduler_config: SchedulerConfig,
-        ascend_scheduler_config,
-    ):
-        scheduler_config = {
-            field.name: getattr(vllm_scheduler_config, field.name)
-            for field in fields(vllm_scheduler_config) if field.init
-        }
-        # Override default values into original SchedulerConfig
-        scheduler_config["enable_chunked_prefill"] = False
-        scheduler_config["max_long_partial_prefills"] = None
-        scheduler_config["long_prefill_token_threshold"] = None
-        scheduler_config["policy"] = "fcfs"
-        scheduler_config["scheduler_cls"] = (
-            "vllm_ascend.core.scheduler.AscendScheduler")
-        scheduler_config["enable_pd_transfer"] = False
-        scheduler_config["decode_max_num_seqs"] = 0
-        # Override params in original SchedulerConfig with params in ascend_scheduler_config
-        for k, _ in scheduler_config.items():
-            if hasattr(ascend_scheduler_config, k):
-                scheduler_config[k] = getattr(ascend_scheduler_config, k)
-        return cls(**scheduler_config)
-
-    def __post_init__(self, *args) -> None:
-        self.max_num_encoder_input_tokens = self.max_num_batched_tokens
-        self.encoder_cache_size = self.max_num_batched_tokens
-        self.chunked_prefill_enabled = self.enable_chunked_prefill
-        if (self.max_num_batched_tokens < self.max_model_len
-                and not self.chunked_prefill_enabled):
-            raise ValueError(
-                "Ascend scheduler is enabled without chunked prefill feature. "
-                f"Argument max_num_batched_tokens ({self.max_num_batched_tokens}) is "
-                f"smaller than max_model_len ({self.max_model_len}). "
-                "This effectively limits the maximum sequence length to "
-                "max_num_batched_tokens and makes vLLM reject longer "
-                "sequences. Please increase max_num_batched_tokens or "
-                "decrease max_model_len.")
-        # concurrent partial prefills. Default is 1 meaning not enabled.
-        if self.max_long_partial_prefills is None:
-            self.max_long_partial_prefills = 1
-            self.long_prefill_token_threshold = MAX_INT
-
-        if self.long_prefill_token_threshold is None or \
-            self.long_prefill_token_threshold <= 0:
-            if self.max_model_len is None:
-                self.long_prefill_token_threshold = MAX_INT
-            else:
-                self.long_prefill_token_threshold = \
-                    max(1, int(self.max_model_len * 0.04))
-
-        if self.max_long_partial_prefills < 0:
-            raise ValueError(
-                f"max_long_partial_prefills must be non-negative, but got "
-                f"{self.max_long_partial_prefills}")
-        if self.long_prefill_token_threshold < 0:
-            raise ValueError(
-                f"long_prefill_token_threshold must be non-negative, but got "
-                f"{self.long_prefill_token_threshold}")
-
-        if self.policy != "fcfs":
-            raise NotImplementedError(
-                f"currently AscendScheduler only supports fcfs policy, got {self.policy}"
-            )
-        if getattr(self, "scheduler_delay_factor", 0) > 0:
-            raise NotImplementedError(
-                "currently AscendScheduler doesn't support scheduler_delay_factor."
-            )