7ee0b0b5d8
This PR upgrade CANN from 8.2rc1 to 8.3rc1 and remove the CANN version
check logic.
TODO: we notice that UT runs failed with CANN 8.3 image. So the base
image for UT is still 8.2. We'll fix it later.
- vLLM version: v0.11.0
- vLLM main:
83f478bb19
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
96 lines
4.7 KiB
Python
96 lines
4.7 KiB
Python
#
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# Copyright (c) 2025 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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import torch
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from tests.ut.base import TestBase
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from vllm_ascend.attention.attention_mask import AttentionMaskBuilder
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class TestAttentionMaskBuilder(TestBase):
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def test_init_attention_mask_builder(self):
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# generate attention_mask_builder with float16
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attention_mask_builder = AttentionMaskBuilder(max_seq_len=1024,
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dtype=torch.float16)
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self.assertEqual(attention_mask_builder._seq_len_cached, 1024)
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self.assertEqual(attention_mask_builder.attn_mask_cache.dtype,
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torch.float16)
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self.assertEqual(attention_mask_builder.attn_mask_cache.shape,
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(1024, 1024))
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self.assertEqual(attention_mask_builder.attn_mask_cache[0][-1],
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torch.tensor(float("-inf"), dtype=torch.float16))
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# generate attention_mask_builder with bfloat16
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attention_mask_builder = AttentionMaskBuilder(max_seq_len=2048,
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dtype=torch.bfloat16)
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self.assertEqual(attention_mask_builder._seq_len_cached, 2048)
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self.assertEqual(attention_mask_builder.attn_mask_cache.dtype,
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torch.bfloat16)
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self.assertEqual(attention_mask_builder.attn_mask_cache.shape,
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(2048, 2048))
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self.assertEqual(attention_mask_builder.attn_mask_cache[0][-1],
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torch.tensor(1, dtype=torch.bfloat16))
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def test_get_mask_scale_factor(self):
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# supported data types
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self.assertEqual(
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AttentionMaskBuilder.get_mask_scale_factor(torch.float16), 1)
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self.assertEqual(
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AttentionMaskBuilder.get_mask_scale_factor(torch.bfloat16), -10000)
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# mask_scale_factor now only supports data types: torch.float16 and torch.bfloat16
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# Otherwise raise ValueError
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with self.assertRaises(ValueError):
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AttentionMaskBuilder.get_mask_scale_factor(torch.int8)
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def test_get_attn_mask(self):
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# if the len is less than max_seq_len, the attn_mask_cache will not be updated
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attention_mask_builder = AttentionMaskBuilder(max_seq_len=1024,
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dtype=torch.float16)
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attn_mask = attention_mask_builder.get_attn_mask(
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max_seq_len=512, dtype=torch.float16, device=torch.device("cpu"))
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self.assertEqual(attn_mask.shape, (512, 512))
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self.assertEqual(attn_mask[0][-1],
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torch.tensor(float("-inf"), dtype=torch.float16))
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self.assertEqual(attention_mask_builder._seq_len_cached, 1024)
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self.assertEqual(attention_mask_builder.attn_mask_cache.shape,
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(1024, 1024))
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self.assertEqual(attention_mask_builder.attn_mask_cache[0][-1],
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torch.tensor(float("-inf"), dtype=torch.float16))
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# if the len is greater than max_seq_len, the attn_mask_cache will be updated
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attn_mask = attention_mask_builder.get_attn_mask(
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max_seq_len=2048, dtype=torch.float16, device=torch.device("cpu"))
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self.assertEqual(attn_mask.shape, (2048, 2048))
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self.assertEqual(attn_mask[0][-1],
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torch.tensor(float("-inf"), dtype=torch.float16))
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self.assertEqual(attention_mask_builder._seq_len_cached, 2048)
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self.assertEqual(attention_mask_builder.attn_mask_cache.shape,
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(2048, 2048))
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self.assertEqual(attention_mask_builder.attn_mask_cache[0][-1],
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torch.tensor(float("-inf"), dtype=torch.float16))
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def test_get_splitfuse_attn_mask(self):
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attention_mask_builder = AttentionMaskBuilder(max_seq_len=1024,
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dtype=torch.float16)
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attn_mask = attention_mask_builder.get_splitfuse_attn_mask(
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seq_lens=torch.tensor([10, 20, 100]),
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position=torch.tensor([7, 8, 9, 18, 19, 99]),
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dtype=torch.float16,
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device=torch.device("cpu"),
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)
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self.assertEqual(attn_mask.shape, (2048, 2048))
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self.assertEqual(attention_mask_builder._seq_len_cached, 1024)
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