643e6f5486
### What this PR does / why we need it?
If a small batch of short requests is sent first, forming a chunk with a
length <128, it will corrupt the `attn_mask_cache`, causing subsequent
requests that do not form a chunk to have accuracy issues.
The root cause of this problem is the use of in-place multiplication.
Modifying it to use out-of-place multiplication will resolve the
accuracy problem.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Yes.
- vLLM version: v0.9.2
- vLLM main:
ad6c2e1a0b
---------
Signed-off-by: ApsarasX <apsarax@outlook.com>
157 lines
7.5 KiB
Python
157 lines
7.5 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.splitfuse_mask_value, -10000)
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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 int8
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attention_mask_builder = AttentionMaskBuilder(max_seq_len=512,
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dtype=torch.int8)
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self.assertEqual(attention_mask_builder._seq_len_cached, 512)
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self.assertEqual(attention_mask_builder.attn_mask_cache.dtype,
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torch.int8)
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self.assertEqual(attention_mask_builder.splitfuse_mask_value, -10000)
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self.assertEqual(attention_mask_builder.attn_mask_cache.shape,
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(512, 512))
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self.assertEqual(attention_mask_builder.attn_mask_cache[0][-1],
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torch.tensor(1, dtype=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=[512],
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query_lens=[512],
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position=torch.tensor([0]),
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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, (1, 512))
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self.assertEqual(attention_mask_builder._seq_len_cached, 1024)
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attn_mask = attention_mask_builder.get_splitfuse_attn_mask(
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seq_lens=[2048],
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query_lens=[1024],
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position=torch.tensor([0]),
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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, (1024, 2048))
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attention_mask_builder = AttentionMaskBuilder(max_seq_len=1024,
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dtype=torch.int8)
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attn_mask = attention_mask_builder.get_splitfuse_attn_mask(
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seq_lens=[512],
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query_lens=[512],
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position=torch.tensor([0]),
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dtype=torch.int8,
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device=torch.device("cpu"),
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)
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self.assertEqual(attn_mask.shape, (1, 512))
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def test_use_multiple_masks(self):
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max_seq_lens = [128, 512, 1024]
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dtypes = [torch.float16, torch.bfloat16, torch.int8]
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for max_seq_len, dtype in zip(max_seq_lens, dtypes):
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with self.subTest(max_seq_len=max_seq_len, dtype=dtype):
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self._test_use_multiple_masks(max_seq_len, dtype)
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def _test_use_multiple_masks(self, max_seq_len, dtype):
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expected_mask_value = torch.finfo(
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torch.float32).min if dtype == torch.float16 else 1
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if dtype == torch.float16:
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expected_splitfuse_mask_value = expected_mask_value
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elif dtype == torch.bfloat16:
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expected_splitfuse_mask_value = -10000
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else:
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assert dtype == torch.int8, "Unsupported dtype for attention mask"
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expected_splitfuse_mask_value = -16
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attention_mask_builder = AttentionMaskBuilder(max_seq_len=max_seq_len,
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dtype=dtype)
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splitfuse_attn_mask = attention_mask_builder.get_splitfuse_attn_mask(
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seq_lens=[max_seq_len],
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query_lens=[max_seq_len],
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position=torch.tensor([0]),
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dtype=dtype,
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device=torch.device("cpu"),
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)
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self.assertEqual(splitfuse_attn_mask.shape, (1, max_seq_len))
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self.assertEqual(
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splitfuse_attn_mask[0][-1],
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torch.tensor(expected_splitfuse_mask_value, dtype=dtype))
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self.assertEqual(attention_mask_builder._seq_len_cached, max_seq_len)
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self.assertEqual(attention_mask_builder.attn_mask_cache.shape,
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(max_seq_len, max_seq_len))
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self.assertEqual(attention_mask_builder.attn_mask_cache[0][-1],
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torch.tensor(expected_mask_value, dtype=dtype))
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attn_mask = attention_mask_builder.get_attn_mask(
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max_seq_len=max_seq_len, dtype=dtype, device=torch.device("cpu"))
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self.assertEqual(attn_mask.shape, (max_seq_len, max_seq_len))
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self.assertEqual(attn_mask[0][-1],
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torch.tensor(expected_mask_value, dtype=dtype))
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self.assertEqual(attention_mask_builder._seq_len_cached, max_seq_len)
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self.assertEqual(attention_mask_builder.attn_mask_cache.shape,
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(max_seq_len, max_seq_len))
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self.assertEqual(attention_mask_builder.attn_mask_cache[0][-1],
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torch.tensor(expected_mask_value, dtype=dtype))
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