77a029979e
### What this PR does / why we need it? Revert PR 5253 to fix the smoking problem ### Does this PR introduce _any_ user-facing change? Does not. ### How was this patch tested? It was tested in the failure case. Signed-off-by: Rifa <865071616@qq.com>
280 lines
12 KiB
Python
280 lines
12 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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# 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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# ** 1. File: platform/patch_distributed.py**
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `torch.distributed.all_reduce`, `torch.distributed.broadcast`
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# Why:
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# tensor alignment for 310p
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# How：
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# rewrite all_reduce and broadcast in torch.distributed
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# Related PR (if no, explain why):
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# No, not ready yet.
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# Future Plan:
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# Find a better way to support tensor alignment for 310p without this patch.
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#
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# ** 2. File: platform/patch_ec_connector.py**
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.distributed.ec_transfer.ec_connector.shared_storage_connector.ECSharedStorageConnector.start_load_caches`
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# Why:
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# it's hard code to cuda
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# How：
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# change the cuda to npu
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# Related PR (if no, explain why):
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# https://github.com/vllm-project/vllm/pull/30225
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# Future Plan:
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# Remove this patch when vllm merges the PR.
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#
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# ** 3. File: platform/patch_mamba_config.py**
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.model_executor.models.config.HybridAttentionMambaModelConfig.verify_and_update_config`
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# Why:
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# block size is set to 16 in vLLM which is not supported by Ascend.
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# How：
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# Set block size to 128 on npu.
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# Related PR (if no, explain why):
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# we'll fix this in vLLM soon.
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# Future Plan:
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# Remove this patch when vLLM merges the PR.
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#
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# ** 4. File: platform/patch_multiproc_executor.py**
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.v1.executor.multiproc_executor.MultiprocExecutor`
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# Why:
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# vLLM create child process with daemon=True, which doesn't work with EPLB case, since EPLB will create
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# a new process which is not allowed by daemon=True.
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# How：
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# Set daemon=False in MultiprocExecutor.
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# Related PR (if no, explain why):
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# Find a way to support daemon=False in vLLM
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# Future Plan:
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# Remove this patch when vLLM fix the issue.
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#
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# ** 5. File: platform/patch_sched_yield.py**
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.distributed.utils.USE_SCHED_YIELD`
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# Why:
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# os.sched_yield() doesn't work on Arm systems.
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# How：
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# avoid using os.sched_yield() on Arm systems.
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# Related PR (if no, explain why):
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# https://github.com/vllm-project/vllm/pull/30228
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# Future Plan:
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# Remove this patch when vLLM merge the PR.
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#
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# ** 6. File: platform/patch_balance_schedule.py**
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.v1.engine.core.EngineCoreProc.run_engine_core`
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# `vllm.v1.core.sched.scheduler.Scheduler`
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# Why:
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# vLLM v1 scheduling currently enables chunkedprefill by default, which processes prefill and decode
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# requests simultaneously in a single scheduling session. This can impact the overall system throughput
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# and performance in some scenarios.
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# How：
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# Set environmental variables VLLM_ASCEND_BALANCE_SCHEDULING=1 in startup script.
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# Related PR (if no, explain why):
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# https://github.com/vllm-project/vllm/pull/29721
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# Future Plan:
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# Remove this patch when vLLM merge the PR.
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#
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# ** 7. File: platform/patch_compile_backend.py**
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.compilation.backends.PiecewiseCompileInterpreter`
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# `vllm.compilation.piecewise_backend.PiecewiseBackend`
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# Why:
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# vllm removed the compile graph for general shape, which caused operator fusion to fail.
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# This issue affects the performance of model inference on Ascend.
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# How：
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# recover the compiled graph for dynamic_shape in PiecewiseBackend.
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# Related PR (if no, explain why):
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# https://github.com/vllm-project/vllm/pull/24252
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# Future Plan:
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# Remove this patch when fix the problem.
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#
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# * Worker Patch:
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# ===============
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#
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# ** 1. File: worker/patch_deepseek.py **
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `DeepseekV2Model.forward`
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# Why:
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# getattr(self.config, "llama_4_scaling", None) will raise AttributeError
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# on npu with graph mode.
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# How：
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# catch the AttributeError and set llama_4_scaling to None.
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# Related PR (if no, explain why):
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# No, this is a bug in vLLM Ascend
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# Future Plan:
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# Find the root cause of this bug and fix it in vLLM Ascend.
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#
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# ** 2. File: worker/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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# No, we should use vlLM all2all manager to support all_to_all for npu.
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# Future Plan:
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# Remove this patch when the refactor of all2all manager is done.
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#
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# ** 3. File: worker/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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# ** 4. File: worker/patch_multimodal_merge.py**
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.model_executor.models.utils._merge_multimodal_embeddings`
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# Why:
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# '_merge_multimodal_embeddings' func of vllm is incompatible with Ascend.
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# How：
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# Replace with CPU operation that can be executed asynchronously.
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# Related PR (if no, explain why):
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# This is a bug by Ascend only. It can' be fixed in vLLM.
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# Future Plan:
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# Identify this pattern in torch-npu and remove this patch.
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#
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# ** 5. File: worker/patch_roberta.py **
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.model_executor.models.bert `
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# Why:
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# shift operation in `_encode_token_type_ids` and `_decode_token_type_ids` cannot run in ascend aclgraph mode
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# How：
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# Replace shift operation with multiplication and division.
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# Related PR (if no, explain why):
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# No, this need CANN add an aclnn shift operation
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# Future Plan:
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# Revert this when CANN support shift aclnn operation
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#
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# ** 6. File: worker/patch_triton.py**
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.model_executor.layers.mamba.ops`, `vllm.model_executor.layers.fla.ops`
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# Why:
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# triton ops in vLLM perform not good on NPU. And there is no dispatch mechanism for triton ops.
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# How：
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# override triton ops in vLLM with ascend implementation
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# Related PR (if no, explain why):
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# Let vLLM support triton ops dispatch.
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# Future Plan:
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# Remove this patch when vLLM support the dispatch function.
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#
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# ** 7. File: worker/patch_qwen3_next_mtp.py**
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.v1.worker.utils.bind_kv_cache`
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# Why:
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# 'bind_kv_cache' func will raise an exception when current_platform is npu.
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# How：
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# Replace with a new bind_kv_cache.
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# Skip the raise.
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# Related PR (if no, explain why):
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# It need discuss.
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# Future Plan:
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# Remove this patch after discussing with vllm community and adapting bind_kv_cache to npu.
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#
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# ** 8. File: worker/patch_module.py**
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.v1.attention.backends.gdn_attn.torch.argsort`
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# Why:
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# 1. 'torch.argsort' func of npu does not support bool.
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# 2. Without `stable=True`, the output will have a lot of redundant tokens.
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# How：
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# Replace with a new torch.argsort that will cast the input to torch.int32
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# and do stable sort.
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# Related PR (if no, explain why):
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# 1. It depends on torch_npu.
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# 2. https://github.com/vllm-project/vllm/pull/30632
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# Future Plan:
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# Remove this patch when bool is supported in 'torch.argsort' func of npu.
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# Make 'torch.argsort' in `vllm.v1.attention.backends.gdn_attn` be stable.
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#
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# ** 9. File: worker/patch_rejection_sampler.py**
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.v1.sample.rejection_sampler`
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# Why:
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# - some functions from `rejection_sampler` are not supported or slow on npu.
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# How：
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# - add npu_top_k_top_p to 'apply_sampling_constraints' func
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# - add custom triton kernel to `expand_batch_to_tokens` and `rejection_sample`
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# Related PR (if no, explain why):
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# https://github.com/vllm-project/vllm/pull/874
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# https://github.com/vllm-project/vllm/pull/4849
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# Future Plan:
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# 1. make these functions as class func of RejectionSampler, create AscendRejectionSampler
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# to override them, then delete the patch file `worker/patch_rejection_sampler.py`.
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# 2. make these functions as costom op, then remove AscendRejectionSampler
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#
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# ** 10.File: worker/patch_qwen3_next.py**
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.model_executor.models.qwen3_next.Qwen3NextGatedDeltaNet.forward`
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# Why:
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# The Qwen3Next GatedDeltaNet forward cannot directly add custom operators.
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# How：
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# Add a branch in Qwen3NextGatedDeltaNet.forward to adapt to fused_qkvzba_split_reshape_cat.
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# Related PR (if no, explain why):
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# https://github.com/vllm-project/vllm/pull/30863
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# Future Plan:
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# Remove this patch when vLLM support these operators.
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#
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# ** 11. File: worker/patch_qwen3_next.py**
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.model_executor.models.qwen3_next.Qwen3NextGatedDeltaNet._forward_core`
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# Why:
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# triton ops fused_recurrent_gated_delta_rule and fused_gdn_gating in vLLM perform not good on NPU.
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# How：
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# add a new fused triton ops in vLLM with ascend implementation.
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# Related PR (if no, explain why):
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# https://github.com/vllm-project/vllm/pull/30860
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# Future Plan:
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# Remove this patch when vLLM support these operators.
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#
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# 2. `vllm.model_executor.models.qwen3_next.Qwen3NextGatedDeltaNet._forward_core`
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# Why:
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# The Qwen3Next GatedDeltaNet _forward_core cannot directly add custom operators.
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# How：
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# Add a branch in Qwen3NextGatedDeltaNet._forward_core to adapt to fused_gdn_gating_patch.
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# Related PR (if no, explain why):
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# https://github.com/vllm-project/vllm/pull/31002
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# Future Plan:
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# Remove this patch when vLLM support these operators.
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#
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