cadfa5ddc1
### What this PR does / why we need it?
This PR add `qkv_rmsnorm_rope` operator and introduces a graph fusion
pass for `qknorm_rope` operations. The implementation includes a new
configuration flag, a pattern matching pass using
`torch._inductor.pattern_matcher`, and a custom Triton kernel for the
fused operation.
Co-authored-by: Angazenn
[supperccell@163.com](mailto:supperccell@163.com)
### Does this PR introduce _any_ user-facing change?
Yes, add new additional_config
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: wxsIcey <1790571317@qq.com>
57 lines
2.1 KiB
Python
57 lines
2.1 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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#
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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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from torch import fx as fx
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from vllm.compilation.vllm_inductor_pass import VllmInductorPass
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from vllm.config import VllmConfig
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class GraphFusionPassManager:
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"""
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A pass manager for graph fusion passes.
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It handles the configuration and execution of passes.
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The counterpart in vllm is PostGradPassManager. Since torch_npu
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does not support triton for now, we define our own pass manager.
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"""
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def __init__(self):
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self.passes: list[VllmInductorPass] = []
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def __call__(self, graph: fx.Graph, runtime_shape) -> fx.Graph:
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for pass_ in self.passes:
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if pass_.is_applicable(runtime_shape):
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pass_(graph)
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return graph
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def add(self, pass_: VllmInductorPass):
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assert isinstance(pass_, VllmInductorPass)
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self.passes.append(pass_)
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def configure(self, config: VllmConfig):
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# By default, we enable the graph fusion and quantization fusion pass.
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self.ascend_compilation_config: dict = config.additional_config.get(
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"ascend_compilation_config", {})
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if self.ascend_compilation_config.get("fuse_norm_quant", True):
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from .passes.norm_quant_fusion_pass import \
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AddRMSNormQuantFusionPass
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self.passes.append(AddRMSNormQuantFusionPass(config))
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if self.ascend_compilation_config.get("fuse_qknorm_rope", True):
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from .passes.qknorm_rope_fusion_pass import QKNormRopeFusionPass
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self.passes.append(QKNormRopeFusionPass(config))
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