e76b69b9ef
### What this PR does / why we need it?
This pull request resolves an attention accuracy issue by enhancing the
AttentionMaskBuilder310 to correctly handle the maximum model length.
The change ensures that the attention mask generation process is
properly parameterized by the model's configuration, rather than relying
on a fixed internal value. This leads to more accurate attention mask
creation, which is crucial for the correct functioning of the attention
mechanism.
Update fused_moe to main branch.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Qwen3 dense mode & moe model e2e test
- vLLM version: v0.15.0
- vLLM main:
83b47f67b1
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Signed-off-by: pu-zhe <zpuaa@outlook.com>
59 lines
2.2 KiB
Python
59 lines
2.2 KiB
Python
#
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# Copyright (c) 2026 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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from typing import Any
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import torch
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from vllm.config import VllmConfig
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from vllm.v1.kv_cache_interface import AttentionSpec
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from vllm_ascend._310p.attention.attention_mask import AttentionMaskBuilder310
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from vllm_ascend.attention.attention_v1 import AscendAttentionMetadataBuilder
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class AscendAttentionMetadataBuilder310(AscendAttentionMetadataBuilder):
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"""
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Metadata builder specialized for the Huawei Ascend 310P NPU.
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This class extends the base Ascend attention metadata builder to use
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the 310P-specific attention mask builder, ensuring that masks are
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generated in the correct format (FRACTAL_NZ) and logic required by
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the 310P hardware.
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"""
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def __init__(
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self,
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kv_cache_spec: AttentionSpec,
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layer_names: list[str],
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vllm_config: VllmConfig,
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device: torch.device,
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):
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"""
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Initializes the metadata builder and the 310P-specific mask builder.
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Args:
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kv_cache_spec (AttentionSpec): Specification for the KV cache (block size, etc.).
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layer_names (list[str]): List of layer names in the model.
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vllm_config (VllmConfig): Global vLLM configuration object.
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device (torch.device): The device (NPU) to run operations on.
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"""
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super().__init__(kv_cache_spec, layer_names, vllm_config, device)
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# Override the mask builder with the 310P-specific version
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max_model_len = vllm_config.model_config.max_model_len
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self.attn_mask_builder: Any = AttentionMaskBuilder310(self.device, max_model_len)
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