v0.10.1rc1

tests/ut/attention/test_attention_mask.py

@@ -0,0 +1,133 @@
+#
+# 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.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 bfloat16
+        attention_mask_builder = AttentionMaskBuilder(max_seq_len=2048,
+                                                      dtype=torch.bfloat16)
+        self.assertEqual(attention_mask_builder._seq_len_cached, 2048)
+        self.assertEqual(attention_mask_builder.attn_mask_cache.dtype,
+                         torch.bfloat16)
+        self.assertEqual(attention_mask_builder.attn_mask_cache.shape,
+                         (2048, 2048))
+        self.assertEqual(attention_mask_builder.attn_mask_cache[0][-1],
+                         torch.tensor(1, dtype=torch.bfloat16))
+
+    def test_get_mask_scale_factor(self):
+        # supported data types
+        self.assertEqual(
+            AttentionMaskBuilder.get_mask_scale_factor(torch.float16), 1)
+        self.assertEqual(
+            AttentionMaskBuilder.get_mask_scale_factor(torch.bfloat16), -10000)
+        # mask_scale_factor now only supports data types: torch.float16 and torch.bfloat16
+        # Otherwise raise ValueError
+        with self.assertRaises(ValueError):
+            AttentionMaskBuilder.get_mask_scale_factor(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=torch.tensor([10, 20, 100]),
+            position=torch.tensor([7, 8, 9, 18, 19, 99]),
+            dtype=torch.float16,
+            device=torch.device("cpu"),
+        )
+        self.assertEqual(attn_mask.shape, (6, 100))
+        self.assertEqual(attention_mask_builder._seq_len_cached, 1024)
+
+        attn_mask = attention_mask_builder.get_splitfuse_attn_mask(
+            seq_lens=torch.tensor([10, 3000, 2000]),
+            position=torch.tensor([7, 8, 9, 2999, 1999]),
+            dtype=torch.float16,
+            device=torch.device("cpu"),
+        )
+        self.assertEqual(attn_mask.shape, (5, 3000))
+        self.assertEqual(attention_mask_builder._seq_len_cached, 3000)
+
+        # splitfuse_attn_mask now only supports data types: torch.float16 and torch.bfloat16
+        # otherwise raise ValueError
+        with self.assertRaises(ValueError):
+            attn_mask = attention_mask_builder.get_splitfuse_attn_mask(
+                seq_lens=torch.tensor([10, 20, 100]),
+                position=torch.tensor([7, 8, 9, 18, 19, 99]),
+                dtype=torch.int8,
+                device=torch.device("cpu"),
+            )
+
+    def test_mask_value_cleanliness(self):
+        attention_mask_builder = AttentionMaskBuilder(max_seq_len=6,
+                                                      dtype=torch.bfloat16)
+        self.assertEqual(attention_mask_builder.attn_mask_cache[-2][-1],
+                         torch.tensor(1, dtype=torch.bfloat16))
+
+        attn_mask = attention_mask_builder.get_splitfuse_attn_mask(
+            seq_lens=torch.tensor([6]),
+            position=torch.tensor([3, 4, 5]),
+            dtype=torch.bfloat16,
+            device=torch.device("cpu"),
+        )
+        self.assertEqual(
+            attn_mask[-2][-1],
+            torch.tensor(-10000, dtype=torch.bfloat16,
+                         device=attn_mask.device))
+        self.assertEqual(attention_mask_builder.attn_mask_cache[-2][-1],
+                         torch.tensor(1, dtype=torch.bfloat16))