134 lines
6.4 KiB
Python
134 lines
6.4 KiB
Python
#
|
|
# 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))
|