[Misc] Add attention mask (#1673)

Move attention mark from V0 to common place.
- vLLM version: v0.9.2
- vLLM main:
b942c094e3

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

tests/ut/attention/test_attention_mask.py

@@ -0,0 +1,107 @@
+#
+# Copyright (c) 2025 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.
+
+import torch
+
+from tests.ut.base import TestBase
+from vllm_ascend.attention.attention_mask import AttentionMaskBuilder
+
+
+class TestAttentionMaskBuilder(TestBase):
+
+    def test_init_attention_mask_builder(self):
+        # generate attention_mask_builder with float16
+        attention_mask_builder = AttentionMaskBuilder(max_seq_len=1024,
+                                                      dtype=torch.float16)
+        self.assertEqual(attention_mask_builder._seq_len_cached, 1024)
+        self.assertEqual(attention_mask_builder.attn_mask_cache.dtype,
+                         torch.float16)
+        self.assertEqual(attention_mask_builder.splitfuse_mask_value, -10000)
+        self.assertEqual(attention_mask_builder.attn_mask_cache.shape,
+                         (1024, 1024))
+        self.assertEqual(attention_mask_builder.attn_mask_cache[0][-1],
+                         torch.tensor(float("-inf"), dtype=torch.float16))
+
+        # generate attention_mask_builder with int8
+        attention_mask_builder = AttentionMaskBuilder(max_seq_len=512,
+                                                      dtype=torch.int8)
+        self.assertEqual(attention_mask_builder._seq_len_cached, 512)
+        self.assertEqual(attention_mask_builder.attn_mask_cache.dtype,
+                         torch.int8)
+        self.assertEqual(attention_mask_builder.splitfuse_mask_value, -10000)
+        self.assertEqual(attention_mask_builder.attn_mask_cache.shape,
+                         (512, 512))
+        self.assertEqual(attention_mask_builder.attn_mask_cache[0][-1],
+                         torch.tensor(1, dtype=torch.int8))
+
+    def test_get_attn_mask(self):
+        # if the len is less than max_seq_len, the attn_mask_cache will not be updated
+        attention_mask_builder = AttentionMaskBuilder(max_seq_len=1024,
+                                                      dtype=torch.float16)
+        attn_mask = attention_mask_builder.get_attn_mask(
+            max_seq_len=512, dtype=torch.float16, device=torch.device("cpu"))
+        self.assertEqual(attn_mask.shape, (512, 512))
+        self.assertEqual(attn_mask[0][-1],
+                         torch.tensor(float("-inf"), dtype=torch.float16))
+        self.assertEqual(attention_mask_builder._seq_len_cached, 1024)
+        self.assertEqual(attention_mask_builder.attn_mask_cache.shape,
+                         (1024, 1024))
+        self.assertEqual(attention_mask_builder.attn_mask_cache[0][-1],
+                         torch.tensor(float("-inf"), dtype=torch.float16))
+
+        # if the len is greater than max_seq_len, the attn_mask_cache will be updated
+        attn_mask = attention_mask_builder.get_attn_mask(
+            max_seq_len=2048, dtype=torch.float16, device=torch.device("cpu"))
+        self.assertEqual(attn_mask.shape, (2048, 2048))
+        self.assertEqual(attn_mask[0][-1],
+                         torch.tensor(float("-inf"), dtype=torch.float16))
+        self.assertEqual(attention_mask_builder._seq_len_cached, 2048)
+        self.assertEqual(attention_mask_builder.attn_mask_cache.shape,
+                         (2048, 2048))
+        self.assertEqual(attention_mask_builder.attn_mask_cache[0][-1],
+                         torch.tensor(float("-inf"), dtype=torch.float16))
+
+    def test_get_splitfuse_attn_mask(self):
+        attention_mask_builder = AttentionMaskBuilder(max_seq_len=1024,
+                                                      dtype=torch.float16)
+        attn_mask = attention_mask_builder.get_splitfuse_attn_mask(
+            seq_lens=[512],
+            query_lens=[512],
+            position=torch.tensor([0]),
+            dtype=torch.float16,
+            device=torch.device("cpu"),
+        )
+        self.assertEqual(attn_mask.shape, (1, 512))
+        self.assertEqual(attention_mask_builder._seq_len_cached, 1024)
+
+        attn_mask = attention_mask_builder.get_splitfuse_attn_mask(
+            seq_lens=[2048],
+            query_lens=[1024],
+            position=torch.tensor([0]),
+            dtype=torch.float16,
+            device=torch.device("cpu"),
+        )
+        self.assertEqual(attn_mask.shape, (1024, 2048))
+
+        attention_mask_builder = AttentionMaskBuilder(max_seq_len=1024,
+                                                      dtype=torch.int8)
+        attn_mask = attention_mask_builder.get_splitfuse_attn_mask(
+            seq_lens=[512],
+            query_lens=[512],
+            position=torch.tensor([0]),
+            dtype=torch.int8,
+            device=torch.device("cpu"),
+        )
+        self.assertEqual(attn_mask.shape, (1, 512))