807f0895b2
### What this PR does / why we need it?
Bump torch version to 2.7.1, and cleanup infer schema patch
https://github.com/vllm-project/vllm-ascend/commit/857f489
(https://github.com/vllm-project/vllm-ascend/pull/837), this patch
depends on also: https://github.com/vllm-project/vllm-ascend/pull/1974
### Does this PR introduce any user-facing change?
No
#### How was this patch tested?
CI passed
torch-npu 2.7.1rc1 install guide:
https://gitee.com/ascend/pytorch/tree/v2.7.1/
install depending:
```
pip3 install pyyaml
pip3 install setuptools
```
install torch-npu:
Closes: https://github.com/vllm-project/vllm-ascend/issues/1866
Closes: https://github.com/vllm-project/vllm-ascend/issues/1390
- vLLM version: v0.10.0
- vLLM main:
9af654cc38
---------
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
Signed-off-by: leo-pony <nengjunma@outlook.com>
Co-authored-by: Yikun Jiang <yikunkero@gmail.com>
105 lines
4.7 KiB
Python
105 lines
4.7 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_10_0: contains the patches applied when vllm version is 0.10.0.
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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 0.10.0 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.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_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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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.v1.sample.sampler.Sampler.gather_logprobs`
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# Why:
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# We need to patch gather_logprobs to make sure call batched_count_greater_than
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# with backend=current_platform.simple_compile_backend
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# How：
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# Patch gather_logprobs call new batched_count_greater_than
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# Related PR (if no, explain why):
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# - https://github.com/vllm-project/vllm/pull/21591
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# Future Plan:
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# Revert it when vLLM merge #21591 and release new version
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# ** File: worker/patch_common/patch_linear.py **
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.model_executor.layers.linear.RowParallelLinear`
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# Why:
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# We need to fuse matmul and allreuce in `RowParallelLinear`
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# to improve performance.
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# How：
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# Create a new class `AscendRowParallelLinear` that inherits from `RowParallelLinear`.
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# In this class, we override the `forward` method to use
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# torch_npu.npu_mm_all_reduce_base to replace matmul and allreduce.
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# Related PR (if no, explain why):
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# - https://github.com/vllm-project/vllm-ascend/pull/1926
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# Future Plan:
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# Validate more models in all kinds of scenario,
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# if performance is always improved, we can enable this patch by default and remove the env
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# variable `VLLM_ASCEND_ENABLE_FUSE_MATMUL_ALLREDUCE` in the future.
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