[1/N][Refactor] Refactor code to adapt with vllm main (#3612)
### What this PR does / why we need it? This is the step 1 of refactoring code to adapt with vllm main, and this pr aligned with17c540a9931. refactor deepseek to the latest code arch as of17c540a9932. bunches of fixes due to vllm changes - Fix `AscendScheduler` `__post_init__`, caused by https://github.com/vllm-project/vllm/pull/25075 - Fix `AscendScheduler` init got an unexpected arg `block_size`, caused by https://github.com/vllm-project/vllm/pull/26296 - Fix `KVCacheManager` `get_num_common_prefix_blocks` arg, caused by https://github.com/vllm-project/vllm/pull/23485 - Fix `MLAAttention` import,caused by https://github.com/vllm-project/vllm/pull/25103 - Fix `SharedFusedMoE` import, caused by https://github.com/vllm-project/vllm/pull/26145 - Fix `LazyLoader` improt, caused by https://github.com/vllm-project/vllm/pull/27022 - Fix `vllm.utils.swap_dict_values` improt, caused by https://github.com/vllm-project/vllm/pull/26990 - Fix `Backend` enum import, caused by https://github.com/vllm-project/vllm/pull/25893 - Fix `CompilationLevel` renaming to `CompilationMode` issue introduced by https://github.com/vllm-project/vllm/pull/26355 - Fix fused_moe ops, caused by https://github.com/vllm-project/vllm/pull/24097 - Fix bert model because of `inputs_embeds`, caused by https://github.com/vllm-project/vllm/pull/25922 - Fix MRope because of `get_input_positions_tensor` to `get_mrope_input_positions`, caused by https://github.com/vllm-project/vllm/pull/24172 - Fix `splitting_ops` changes introduced by https://github.com/vllm-project/vllm/pull/25845 - Fix multi-modality changes introduced by https://github.com/vllm-project/vllm/issues/16229 - Fix lora bias dropping issue introduced by https://github.com/vllm-project/vllm/pull/25807 - Fix structured ouput break introduced by https://github.com/vllm-project/vllm/issues/26737 ### Does this PR introduce _any_ user-facing change? ### How was this patch tested? CI passed with existing test. - vLLM version: v0.11.0rc3 - vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0 --------- Signed-off-by: MengqingCao <cmq0113@163.com> Signed-off-by: Icey <1790571317@qq.com> Co-authored-by: Icey <1790571317@qq.com>
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@@ -16,6 +16,7 @@
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#
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import os
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import time
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from typing import Optional
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import pandas as pd
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from vllm.config import VllmConfig
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@@ -32,6 +33,8 @@ from vllm.v1.kv_cache_interface import KVCacheConfig
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from vllm.v1.request import Request, RequestStatus
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from vllm.v1.structured_output import StructuredOutputManager
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from vllm_ascend.utils import vllm_version_is
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class BudgetRefiner:
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"""This budget refiner can make dynamic adjustment to the token budget
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@@ -122,13 +125,19 @@ class SchedulerDynamicBatch(Scheduler):
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vllm_config: VllmConfig,
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kv_cache_config: KVCacheConfig,
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structured_output_manager: StructuredOutputManager,
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block_size: Optional[int] = None,
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mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
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include_finished_set: bool = False,
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log_stats: bool = False,
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) -> None:
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super().__init__(vllm_config, kv_cache_config,
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structured_output_manager, mm_registry,
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include_finished_set, log_stats)
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if vllm_version_is("0.11.0"):
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super().__init__(vllm_config, kv_cache_config,
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structured_output_manager, mm_registry,
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include_finished_set, log_stats)
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else:
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super().__init__(vllm_config, kv_cache_config,
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structured_output_manager, block_size,
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mm_registry, include_finished_set, log_stats)
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self.running: list[Request] = []
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self.budget_refiner = BudgetRefiner(
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default_budget=self.scheduler_config.max_num_batched_tokens,
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@@ -531,10 +540,14 @@ class SchedulerDynamicBatch(Scheduler):
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self.kv_cache_config.kv_cache_groups)
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if self.running:
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any_request = self.running[0]
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num_common_prefix_blocks = (
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self.kv_cache_manager.get_num_common_prefix_blocks(
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any_request, len(self.running)))
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if vllm_version_is("0.11.0"):
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num_common_prefix_blocks = (
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self.kv_cache_manager.get_num_common_prefix_blocks(
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any_request, len(self.running)))
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else:
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num_common_prefix_blocks = (
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self.kv_cache_manager.get_num_common_prefix_blocks(
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any_request.request_id))
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# Construct the scheduler output.
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new_reqs_data = [
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NewRequestData.from_request(
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