d13fb0766e
### What this PR does / why we need it?
Performance optimization for apply_top_k_top_p
### Does this PR introduce _any_ user-facing change?
Use VLLM_ASCEND_ENABLE_TOPK_TOPP_OPTIMIZATION to enable this feature
### How was this patch tested?
e2e & ut
- vLLM version: v0.9.2
- vLLM main:
6a9e6b2abf
Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
146 lines
6.8 KiB
Python
146 lines
6.8 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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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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# ----------------------------------------------------------------------------------
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# This module manage the patch for vllm. There are two folders in this module:
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# - platform: contains the patches applied before worker starts. It's called by
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# `vllm_ascend.utils.adapt_patch(is_global_patch=True)` in
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# `vllm_ascend.platform.NPUPlatform.pre_register_and_update()` function.
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# - worker: contains the patches applied when worker starts. It's called by
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# `vllm_ascend.utils.adapt_patch(is_global_patch=False)` in
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# each worker's `__init__` function.
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#
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# Then in each kind of patch, there are three folders:
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# - patch_0_9_2: contains the patches applied when vllm version is 0.9.2.
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# - patch_main: contains the patches applied when vllm version is main branch.
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# - patch_common: contains the patches applied in both 0.9.2 and main branch.
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#
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# Once a new patch is added in vllm-ascend, please add the patch description into this file as well.
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# ----------------------------------------------------------------------------------
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# What's Patched and how it works:
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# --------------------------------
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# * Platform Patch:
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# =================
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# ** File: platform/patch_common/patch_distributed.py**
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.distributed.parallel_state.destroy_model_parallel()`
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# Why:
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# vllm dose not support outside platform maintain its own `CoordinatorGroup`, vllm-ascend maintain EP and ETP
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# inside of the repo, and needs a common interface to destroy them, this patch add the interface of destroy
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# platform owned `CoordinatorGroup` to make sure all the CoordinateGroup can be properly destroyed
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# How：
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# Call `vllm_ascend.distributed.parallel_state method `destroy_platform_model_parallel` to destroy all the `CoordinateGroup`
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# Related PR (if no, explain why):
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# Future Plan:
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# Remove those patch when vllm merged them
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# 2. `vllm.config.ParallelConfig.get_next_dp_init_port`
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# Why:
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# vllm doesn't support get port from environment.
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# How：
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# Add the logic to get port from environment.
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# Related PR (if no, explain why):
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# Need a PR to vllm to support get port from environment.
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# Future Plan:
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# Remove those patch when vllm merged them
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#
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# * Worker Patch:
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# ===============
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# ** File: worker/patch_common/patch_minicpm.py **
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.model_executor.models.minicpm.MiniCPMAttention.forward`
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# Why:
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# The forward func of MiniCPMAttention in vllm do a datatype convert
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# (original datatype --> float32) to ensure the precision on cuda.
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# However float32 is not supported in cann rope op, thus we keep this patch
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# How：
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# Removed the dtype convert operations in forward
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# Related PR (if no, explain why):
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# NO, only for npu due to rope op.
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# Future Plan:
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# Keep this patch in vllm-ascend.
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#
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# ** File: worker/patch_common/patch_multi_step_worker.py **
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.spec_decode.multi_step_worker.MultiStepWorker.sampler_output`
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# Why:
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# There are cuda hard code (current_platform.is_cuda_alike()) in
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# `MultiStepWorker.sampler_output`, and we need to use the patched `TP1DraftModelRunner` in it.
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# How：
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# Make speculative decoding extensible to different backends.
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# - support attention metadata register to the set supported spec decode
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# - offer a api in platform to determine whether spec decode is supported,
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# and deprecate is_cuda_alike in it.
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# Related PR (if no, explain why):
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# - https://github.com/vllm-project/vllm/pull/15195
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# - https://github.com/vllm-project/vllm-ascend/pull/395
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# Future Plan:
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# Revert it when the related pr is merged in vllm and vllm-ascend.
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#
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# ** File: worker/patch_common/patch_spec_decode_worker.py **
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.spec_decode.spec_decode_worker.SpecDecodeWorker.create_worker`
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# Why:
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# We need to use the patched `TP1DraftModelRunner` in `SpecDecodeWorker.create_worker`.
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# The mainly reason to overwrite `TP1DraftModelRunner`is the hard code of
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# `FlashAttentionMetadata`
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# How：
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# ditto
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# Related PR (if no, explain why):
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# - https://github.com/vllm-project/vllm/pull/15195
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# - https://github.com/vllm-project/vllm-ascend/pull/395
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# Future Plan:
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# Revert it when the related pr is merged in vllm and vllm-ascend.
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#
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# ** File: worker/patch_common/patch_distributed.py **
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.distributed.parallel_state.GroupCoordinator`
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# Why:
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# vllm doesn't support all_to_all for GroupCoordinator.
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# How：
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# Add all_to_all implementation for GroupCoordinator.
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# Related PR (if no, explain why):
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# Need a PR to vllm to support all_to_all for GroupCoordinator.
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# Future Plan:
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# Remove this patch when vllm merged them.
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#
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# ** File: worker/patch_common/patch_utils.py **
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.utils.direct_register_custom_op`
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# Why:
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# pytorch 2.7.o is not compatible with pytorch 2.5.1. While vllm is based on pytorch 2.7.0, but vllm ascend
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# is based on pytorch 2.5.1, so we need to use this patch to make vllm compatible with pytorch 2.5.1.
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# How：
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# patch __annotations__ check to make it compatible with pytorch 2.5.1.
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# Related PR (if no, explain why):
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# This is the problem in vllm-ascend
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# Future Plan:
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# Remove this patch once pytorch 2.7.0 is supported for vllm ascend.
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#
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# ** File: worker/patch_common/patch_sampler.py **
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.v1.sample.sampler.Sampler.apply_top_k_top_p`
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# Why:
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# We need to use the patched `apply_top_k_top_p` in `sample`.
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# The mainly reason to overwrite `apply_top_k_top_p` is
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# to improve performance.
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# How：
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# Re-implementation the `apply_top_k_top_p` function by pytorch
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# Related PR (if no, explain why):
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# - https://github.com/vllm-project/vllm-ascend/pull/1732
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# Future Plan:
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# Revert it when the ascend scatter performance improves.
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