EngineX-Ascend/enginex-ascend-910-vllm
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init v0.23.0

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Signed-off-by: Sun Ruoxi <sunruoxi@4paradigm.com>
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Sun Ruoxi
2026-08-27 15:11:51 +08:00
parent b582a8e7d1
commit 7f8a1b1f7a
2849 changed files with 712887 additions and 22001 deletions
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121
tests/ut/attention/test_attention_mask.py
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@@ -1,5 +1,5 @@
#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# 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.
@@ -20,114 +20,29 @@ 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"))
attention_mask_builder = AttentionMaskBuilder(torch.device("cpu"))
attn_mask = attention_mask_builder.get_attn_mask(max_seq_len=512, dtype=torch.float16)
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))
self.assertEqual(attn_mask[0][-1], torch.tensor(float("-inf"), dtype=torch.float16))
self.assertEqual(attention_mask_builder._seq_len_cached, 512)
self.assertEqual(attention_mask_builder.attn_mask_cache.shape, (512, 512))
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"))
attn_mask = attention_mask_builder.get_attn_mask(max_seq_len=2048, dtype=torch.float16)
self.assertEqual(attn_mask.shape, (2048, 2048))
self.assertEqual(attn_mask[0][-1],
torch.tensor(float("-inf"), dtype=torch.float16))
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))
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))
attention_mask_builder = AttentionMaskBuilder(torch.device("cpu"))
attn_mask = attention_mask_builder.get_splitfuse_attn_mask()
self.assertEqual(attn_mask.shape, (2048, 2048))