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>
50 lines
1.5 KiB
Python
50 lines
1.5 KiB
Python
#
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# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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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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from tests.e2e.conftest import VllmRunner
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def test_qwen3_dense_tp1_fp16():
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example_prompts = [
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"Hello, my name is",
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]
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max_tokens = 5
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with VllmRunner(
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"Qwen/Qwen3-8B",
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tensor_parallel_size=1,
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enforce_eager=True,
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dtype="float16",
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max_model_len=16384,
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) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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def test_qwen3_dense_tp1_w8a8():
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example_prompts = [
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"Hello, my name is",
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]
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max_tokens = 5
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with VllmRunner(
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"vllm-ascend/Qwen3-8B-W8A8",
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tensor_parallel_size=1,
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enforce_eager=True,
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dtype="float16",
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quantization="ascend",
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max_model_len=16384,
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) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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