fbae41697e
### What this PR does / why we need it?
* Refactor the LayerNorm and activation operator classes to decouple the
310P device implementation from the main branch.
* Refactor `mm_encoder_attention` on 310P to use the
`torch_npu._npu_flash_attention_unpad` operator.
* Refactor the QKV inputs in the prefill stage of `attention_v1` on 310P
so they are no longer padded to 16× alignment.
* Refactor `model_runner` on 310P to align the KV-cache initialization
logic with the mainline implementation.
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
use the e2e tests.
- vLLM version: v0.13.0
- vLLM main:
d68209402d
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Signed-off-by: Tflowers-0129 <2906339855@qq.com>
38 lines
1.3 KiB
Python
38 lines
1.3 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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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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# This file is a part of the vllm-ascend project.
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#
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import torch_npu
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from vllm.logger import logger
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from vllm_ascend._310p.model_runner_310p import NPUModelRunner310
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from vllm_ascend.worker.worker import NPUWorker, init_workspace_manager
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class NPUWorker310(NPUWorker):
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def init_device(self):
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self.device = self._init_device()
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torch_npu.npu.set_compile_mode(jit_compile=False)
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init_workspace_manager(self.device, num_ubatches=1)
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self.model_runner = NPUModelRunner310(self.vllm_config, self.device)
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def _warm_up_atb(self):
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# 310p device donot support torch_npu._npu_matmul_add_fp32 atb ops
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logger.info("Skip warm-up atb ops for 310P device")
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