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>
63 lines
2.8 KiB
Python
63 lines
2.8 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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from unittest.mock import MagicMock, patch
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import torch
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from tests.ut.base import TestBase
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from vllm_ascend._310p.attention.attention_mask import AttentionMaskBuilder310
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class TestAttentionMaskBuilder310(TestBase):
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def setUp(self):
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self.max_seqlen = 4096
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self.attention_mask_builder = AttentionMaskBuilder310(torch.device("cpu"), self.max_seqlen)
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def test_get_attention_mask_310_for_pooling_model(self):
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model_config = MagicMock()
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model_config.runner_type = "pooling"
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with self.assertRaises(NotImplementedError):
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self.attention_mask_builder.get_attention_mask(model_config)
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@patch("torch_npu.npu_format_cast")
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def test_get_attention_mask_310(self, mock_format_cast):
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mock_format_cast.side_effect = lambda x, y: x
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model_config = MagicMock()
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attn_mask = self.attention_mask_builder.get_attention_mask(model_config)
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self.assertEqual(attn_mask.shape, (1, self.max_seqlen // 16, self.max_seqlen, 16))
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self.assertEqual(attn_mask[0][-1][0][-1], torch.tensor(float("-inf"), dtype=torch.float16))
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@patch("torch_npu.npu_format_cast")
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def test_get_swa_mask_310(self, mock_format_cast):
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mock_format_cast.side_effect = lambda x, y: x
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swa_mask = self.attention_mask_builder.get_swa_mask(torch.float16, None)
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self.assertIsNone(swa_mask)
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sliding_window = 128
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swa_mask = self.attention_mask_builder.get_swa_mask(torch.float16, sliding_window)
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self.assertEqual(swa_mask.shape, (1, self.max_seqlen // 16, self.max_seqlen, 16))
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self.assertEqual(swa_mask[0][-1][0][-1], torch.tensor(float("-inf"), dtype=torch.float16))
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self.assertEqual(swa_mask[0][0][-1][0], torch.tensor(float("-inf"), dtype=torch.float16))
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@patch("torch_npu.npu_format_cast")
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def test_get_splitfuse_attn_mask_310(self, mock_format_cast):
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mock_format_cast.side_effect = lambda x, y: x
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attn_metadata = MagicMock()
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attn_metadata.query_start_loc = torch.tensor([0, 1, 5])
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attn_metadata.seq_lens = torch.tensor([7, 4])
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attn_mask = self.attention_mask_builder.get_splitfuse_mask(attn_metadata, torch.device("cpu"))
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self.assertEqual(attn_mask.shape, (1, self.max_seqlen // 16, 16, 16))
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