mirror of
https://github.com/handsomezhuzhu/vllm-npu-plugin.git
synced 2026-02-20 11:42:30 +00:00
175 lines
7.6 KiB
Python
175 lines
7.6 KiB
Python
#
|
||
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
|
||
# This file is a part of the vllm-ascend project.
|
||
#
|
||
# 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 module manage the patch for vllm. There are two folders in this module:
|
||
# - platform: contains the patches applied before worker starts. It's called by
|
||
# `vllm_npu.utils.adapt_patch(is_global_patch=True)` in
|
||
# `vllm_npu.platform.NPUPlatform.pre_register_and_update()` function.
|
||
# - worker: contains the patches applied when worker starts. It's called by
|
||
# `vllm_npu.utils.adapt_patch(is_global_patch=False)` in
|
||
# each worker's `__init__` function.
|
||
#
|
||
# Once a new patch is added in vllm-ascend, please add the patch description into this file as well.
|
||
# ----------------------------------------------------------------------------------
|
||
|
||
# What's Patched and how it works:
|
||
# --------------------------------
|
||
# * Platform Patch:
|
||
# =================
|
||
# ** File: platform/patch_distributed.py**
|
||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||
# 1. `vllm.config.ParallelConfig.get_next_dp_init_port`
|
||
# Why:
|
||
# vllm doesn't support get port from environment.
|
||
# How：
|
||
# Add the logic to get port from environment.
|
||
# Related PR (if no, explain why):
|
||
# Need a PR to vllm to support get port from environment.
|
||
# Future Plan:
|
||
# Remove those patch when vllm merged them
|
||
# 2. `torch.distributed.all_reduce`, `torch.distributed.broadcast`
|
||
# Why:
|
||
# tensor alignment for 310p
|
||
# How：
|
||
# rewrite all_reduce and broadcast in torch.distributed
|
||
# Related PR (if no, explain why):
|
||
# No, not ready yet.
|
||
# Future Plan:
|
||
# Find a better way to support tensor alignment for 310p without this patch.
|
||
#
|
||
# ** File: worker/patch_multimodal_merge.py**
|
||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||
# 1. `vllm.model_executor.models.utils._merge_multimodal_embeddings`
|
||
# Why:
|
||
# '_merge_multimodal_embeddings' func of vllm is incompatible with Ascend.
|
||
# How：
|
||
# Replace with CPU operation that can be executed asynchronously.
|
||
# Related PR (if no, explain why):
|
||
# This is a bug by Ascend only. It can' be fixed in vLLM.
|
||
# Future Plan:
|
||
# Identify this pattern in torch-npu and remove this patch.
|
||
#
|
||
# * Worker Patch:
|
||
# ===============
|
||
# ** File: worker/patch_minicpm.py **
|
||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||
# 1. `vllm.model_executor.models.minicpm.MiniCPMAttention.forward`
|
||
# Why:
|
||
# The forward func of MiniCPMAttention in vllm do a datatype convert
|
||
# (original datatype --> float32) to ensure the precision on cuda.
|
||
# However float32 is not supported in cann rope op, thus we keep this patch
|
||
# How：
|
||
# Removed the dtype convert operations in forward
|
||
# Related PR (if no, explain why):
|
||
# NO, only for npu due to rope op.
|
||
# Future Plan:
|
||
# Keep this patch in vllm-ascend.
|
||
#
|
||
# ** File: worker/patch_distributed.py **
|
||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||
# 1. `vllm.distributed.parallel_state.GroupCoordinator`
|
||
# (1) __init__()
|
||
# Why:
|
||
# The original GroupCoordinator initialization lacks pg_options to generate new
|
||
# process group with customized options.
|
||
# How:
|
||
# Inject HCCL options during process group initialization.
|
||
# Related PR (if no, explain why):
|
||
# Need a PR to vllm to support a dictionary as input while initializing distributed
|
||
# environment (e.g., Dict[str, torch.distributed.ProcessGroupHCCL.Options])
|
||
# https://github.com/vllm-project/vllm/pull/25417
|
||
# Future Plan:
|
||
# Remove this patch when vllm merges this PR.
|
||
# (2) all_to_all()
|
||
# Why:
|
||
# vllm doesn't support all_to_all for GroupCoordinator.
|
||
# How：
|
||
# Add all_to_all implementation for GroupCoordinator.
|
||
# Related PR (if no, explain why):
|
||
# Need a PR to vllm to support all_to_all for GroupCoordinator.
|
||
# Future Plan:
|
||
# Remove this patch when vllm merged them.
|
||
#
|
||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||
# 1. `vllm.v1.sample.sampler.Sampler.gather_logprobs`
|
||
# Why:
|
||
# We need to patch gather_logprobs to make sure call batched_count_greater_than
|
||
# with backend=current_platform.simple_compile_backend
|
||
# How：
|
||
# Patch gather_logprobs call new batched_count_greater_than
|
||
# Related PR (if no, explain why):
|
||
# - https://github.com/vllm-project/vllm/pull/21591
|
||
# Future Plan:
|
||
# Revert it when vLLM merge #21591 and release new version
|
||
# ** File: worker/patch_logits.py **
|
||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||
# 1. `vllm._custom_ops.apply_repetition_penalties`
|
||
# Why:
|
||
# apply_repetition_penalties in vLLM use tensor.is_cuda to check if tensor is on cuda. But the value is always True
|
||
# on ascend, thus we need to patch apply_repetition_penalties.
|
||
# How：
|
||
# Remove the related cuda check in apply_repetition_penalties.
|
||
# Related PR (if no, explain why):
|
||
# - this is a bug by Ascend only. It can' be fixed in vLLM.
|
||
# Future Plan:
|
||
# Fix this bug in torch-npu, bump torch-npu version and remove this patch.
|
||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||
# 1. `vllm.model_executor.models.roberta.RobertaEmbedding.forward`
|
||
# Why:
|
||
# shift operation in `_encode_token_type_ids` and `_decode_token_type_ids` cannot run in ascend aclgraph mode
|
||
# How：
|
||
# Replace shift operation with multiplication and division.
|
||
# Related PR (if no, explain why):
|
||
# No, this need CANN add an aclnn shift operation
|
||
# Future Plan:
|
||
# Revert this when CANN support shift aclnn operation
|
||
# 2. `vllm.model_executor.models.roberta.RobertaForSequenceClassification.forward `
|
||
# Why:
|
||
# shift operation in `_encode_token_type_ids` and `_decode_token_type_ids` cannot run in ascend aclgraph mode
|
||
# How：
|
||
# Replace shift operation with multiplication and division.
|
||
# Related PR (if no, explain why):
|
||
# No, this need CANN add an aclnn shift operation
|
||
# Future Plan:
|
||
# Revert this when CANN support shift aclnn operation
|
||
#
|
||
# ** File: worker/patch_deepseek_mtp.py**
|
||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||
# 1. `vllm.model_executor.models.deepseek_mtp.DeepSeekMultiTokenPredictorLayer.__init__`
|
||
# Why:
|
||
# '__init__' func of DeepSeekMultiTokenPredictorLayer didn't pass prefix to SharedHead.
|
||
# How：
|
||
# Replace with a new __init__.
|
||
# Use a new SharedHead which passes prefix to ParallelLMHead.
|
||
# Related PR (if no, explain why):
|
||
# https://github.com/vllm-project/vllm/pull/25805
|
||
# Future Plan:
|
||
# Remove this patch when adapted vllm version contains the above PR.
|
||
#
|
||
# ** File: worker/patch_attention_layer.py **
|
||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||
# 1. `vllm.attention.layer.Attention.forward`
|
||
# Why:
|
||
# There is a zerolike operator before the attention operation in each decoding stage.
|
||
# How
|
||
# Replace this zerolike operator with torch.empty
|
||
# Related PR (if no, explain why):
|
||
# - https://github.com/vllm-project/vllm/pull/26680
|
||
# Future Plan:
|
||
# Remove this to match the optimization supported in the VLLM version.
|
||
#
|