4f33e25046
### What this PR does / why we need it?
This pull request significantly refactors the attention mechanism for
the Ascend 310P hardware, enhancing its architecture by separating mask
generation concerns from the core attention implementation. It
introduces a dedicated mask builder class capable of handling various
mask types, including causal, splitfuse, and sliding window attention
masks, all optimized for the NPU's fractal data format. This change not
only cleans up the codebase but also lays the groundwork for more robust
and feature-rich attention operations on Ascend devices, backed by new,
extensive unit tests.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
E2E test with qwen3 and qwen3-moe
- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd
---------
Signed-off-by: pu-zhe <zpuaa@outlook.com>
62 lines
2.7 KiB
Python
62 lines
2.7 KiB
Python
#
|
|
# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from unittest.mock import MagicMock, patch
|
|
|
|
import torch
|
|
|
|
from tests.ut.base import TestBase
|
|
from vllm_ascend._310p.attention.attention_mask import AttentionMaskBuilder310
|
|
|
|
|
|
class TestAttentionMaskBuilder310(TestBase):
|
|
def setUp(self):
|
|
self.attention_mask_builder = AttentionMaskBuilder310(torch.device("cpu"))
|
|
|
|
def test_get_attention_mask_310_for_pooling_model(self):
|
|
model_config = MagicMock()
|
|
model_config.runner_type = "pooling"
|
|
with self.assertRaises(NotImplementedError):
|
|
self.attention_mask_builder.get_attention_mask(model_config)
|
|
|
|
@patch("torch_npu.npu_format_cast")
|
|
def test_get_attention_mask_310(self, mock_format_cast):
|
|
mock_format_cast.side_effect = lambda x, y: x
|
|
model_config = MagicMock()
|
|
attn_mask = self.attention_mask_builder.get_attention_mask(model_config)
|
|
self.assertEqual(attn_mask.shape, (1, 128, 2048, 16))
|
|
self.assertEqual(attn_mask[0][-1][0][-1], torch.tensor(float("-inf"), dtype=torch.float16))
|
|
|
|
@patch("torch_npu.npu_format_cast")
|
|
def test_get_swa_mask_310(self, mock_format_cast):
|
|
mock_format_cast.side_effect = lambda x, y: x
|
|
swa_mask = self.attention_mask_builder.get_swa_mask(torch.float16, None)
|
|
self.assertIsNone(swa_mask)
|
|
|
|
sliding_window = 128
|
|
swa_mask = self.attention_mask_builder.get_swa_mask(torch.float16, sliding_window)
|
|
self.assertEqual(swa_mask.shape, (1, 128, 2048, 16))
|
|
self.assertEqual(swa_mask[0][-1][0][-1], torch.tensor(float("-inf"), dtype=torch.float16))
|
|
self.assertEqual(swa_mask[0][0][-1][0], torch.tensor(float("-inf"), dtype=torch.float16))
|
|
|
|
@patch("torch_npu.npu_format_cast")
|
|
def test_get_splitfuse_attn_mask_310(self, mock_format_cast):
|
|
mock_format_cast.side_effect = lambda x, y: x
|
|
attn_metadata = MagicMock()
|
|
attn_metadata.query_start_loc = torch.tensor([0, 1, 5])
|
|
attn_metadata.seq_lens = torch.tensor([7, 4])
|
|
attn_mask = self.attention_mask_builder.get_splitfuse_mask(attn_metadata, torch.device("cpu"))
|
|
self.assertEqual(attn_mask.shape, (1, 128, 16, 16))
|