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xc-llm-ascend/tests/ut/attention/test_attention_v1.py
Mengqing Cao 1327f9be1c Fix some ci issue and refactor modelrunner (#2445) 
### What this PR does / why we need it?
Fix some ci issue and refactor modelrunner

### Does this PR introduce _any_ user-facing change?
N/A

### How was this patch tested?
CI passed with existing test.

- vLLM version: v0.10.0
- vLLM main:
4d9c61993a

---------

Signed-off-by: wangli <wangli858794774@gmail.com>
Signed-off-by: MengqingCao <cmq0113@163.com>
Signed-off-by: weiguihua2 <weiguihua2@huawei.com>
Co-authored-by: wangli <wangli858794774@gmail.com>
Co-authored-by: weiguihua2 <weiguihua2@huawei.com>
2025-08-20 09:01:04 +08:00

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from unittest.mock import MagicMock, patch
import torch
from tests.ut.base import TestBase
from vllm_ascend.attention.attention_v1 import (AscendAttentionBackend,
AscendAttentionBackendImpl,
AscendAttentionMetadataBuilder,
AscendAttentionState,
AscendMetadata,
CommonAttentionState)
from vllm_ascend.attention.utils import AscendCommonAttentionMetadata
class TestAscendAttentionBackend(TestBase):
def test_get_name(self):
self.assertEqual(AscendAttentionBackend.get_name(), "ASCEND")
def test_get_impl_cls(self):
self.assertEqual(AscendAttentionBackend.get_impl_cls(),
AscendAttentionBackendImpl)
def test_get_metadata_cls(self):
self.assertEqual(AscendAttentionBackend.get_metadata_cls(),
AscendMetadata)
def test_get_state_cls(self):
self.assertEqual(AscendAttentionBackend.get_state_cls(),
CommonAttentionState)
def test_get_builder_cls(self):
self.assertEqual(AscendAttentionBackend.get_builder_cls(),
AscendAttentionMetadataBuilder)
@patch('vllm_ascend.attention.attention_v1.is_310p')
def test_get_kv_cache_shape_310p(self, mock_is_310p):
mock_is_310p.return_value = True
result = AscendAttentionBackend.get_kv_cache_shape(10, 20, 30, 40)
self.assertEqual(result, (2, 10, 30 * 40 // 16, 20, 16))
@patch('vllm_ascend.attention.attention_v1.is_310p', return_value=False)
def test_get_kv_cache_shape_not_310p(self, mock_is_310p):
result = AscendAttentionBackend.get_kv_cache_shape(10, 20, 30, 40)
self.assertEqual(result, (2, 10, 20, 30, 40))
def test_get_bsh_kv_cache_shape(self):
result = AscendAttentionBackend.get_bsh_kv_cache_shape(10, 20, 30, 40)
self.assertEqual(result, (2, 10, 20, 30 * 40))
def test_swap_blocks(self):
src_kv_cache = [torch.zeros((10, 20)), torch.zeros((10, 20))]
dst_kv_cache = [torch.zeros((10, 20)), torch.zeros((10, 20))]
src_to_dst = torch.tensor([[0, 1], [2, 3]])
AscendAttentionBackend.swap_blocks(src_kv_cache, dst_kv_cache,
src_to_dst)
self.assertTrue(torch.all(dst_kv_cache[0][1] == src_kv_cache[0][0]))
self.assertTrue(torch.all(dst_kv_cache[1][3] == src_kv_cache[1][2]))
def test_copy_blocks(self):
kv_caches = [torch.zeros((10, 20)), torch.zeros((10, 20))]
src_to_dists = torch.tensor([[0, 1], [2, 3]])
AscendAttentionBackend.copy_blocks(kv_caches, src_to_dists)
self.assertTrue(torch.all(kv_caches[0][1] == kv_caches[0][0]))
self.assertTrue(torch.all(kv_caches[1][3] == kv_caches[1][2]))
class TestAscendAttentionMetadataBuilder(TestBase):
def setUp(self):
self.mock_vllm_config = MagicMock()
self.mock_vllm_config.model_config.max_model_len = 640
self.mock_vllm_config.cache_config.block_size = 64
self.mock_device = 'cpu:0'
self.builder = AscendAttentionMetadataBuilder(self.mock_vllm_config,
self.mock_device)
def test_reorder_batch(self):
mock_input_batch = MagicMock()
mock_scheduler_output = MagicMock()
result = self.builder.reorder_batch(mock_input_batch,
mock_scheduler_output)
self.assertFalse(result)
@patch('vllm_ascend.attention.attention_v1.AscendMetadata')
@patch('torch_npu.npu_format_cast')
@patch('vllm_ascend.utils.nd_to_nz_2d')
@patch('vllm_ascend.attention.attention_v1.is_310p', return_value=True)
def test_build_prefill_no_cache(self, mock_is_310p, mock_nd_to_nz_2d,
mock_npu_format_cast,
mock_ascend_metadata):
common_attn_metadata = AscendCommonAttentionMetadata(
query_start_loc=torch.tensor([0, 3, 7]),
query_start_loc_cpu=torch.tensor([0, 3, 7]),
seq_lens_cpu=torch.tensor([5, 6]),
num_reqs=2,
num_actual_tokens=10,
max_query_len=5,
decode_token_per_req=torch.tensor([1, 1]),
block_table_tensor=torch.zeros((10, 10)),
slot_mapping_cpu=torch.tensor(range(20)),
actual_seq_lengths_q=torch.tensor([0, 1]),
positions=torch.tensor([10, 10]),
attn_mask=torch.ones((10, 10)),
spec_attn_mask=None,
attn_state=AscendAttentionState.PrefillNoCache)
mock_nz_tensor = MagicMock()
mock_model = MagicMock()
mock_nd_to_nz_2d.return_value = mock_nz_tensor
mock_npu_format_cast.return_value = mock_nz_tensor
self.builder.build(common_attn_metadata, mock_model)
@patch('vllm_ascend.attention.attention_v1.AscendMetadata')
@patch('torch_npu.npu_format_cast')
@patch('vllm_ascend.utils.nd_to_nz_spec')
@patch('vllm_ascend.attention.attention_v1.is_310p', return_value=True)
@patch('vllm_ascend.attention.attention_v1.AscendAttentionState')
def test_build_chunked_prefill(self, mock_ascend_attention_state,
mock_is_310p, mock_nd_to_nz_spec,
mock_npu_format_cast, mock_ascend_metadata):
common_attn_metadata = AscendCommonAttentionMetadata(
query_start_loc=torch.tensor([0, 2, 5, 9]),
query_start_loc_cpu=torch.tensor([0, 2, 5, 9]),
seq_lens_cpu=torch.tensor([4, 5, 6]),
num_reqs=3,
num_actual_tokens=15,
max_query_len=6,
decode_token_per_req=torch.tensor([1, 1, 1]),
block_table_tensor=torch.zeros((10, 10)),
slot_mapping_cpu=torch.tensor(range(20)),
actual_seq_lengths_q=torch.tensor([0, 1, 2]),
positions=torch.tensor([10, 10]),
attn_mask=torch.ones((15, 15)),
spec_attn_mask=None,
attn_state=AscendAttentionState.ChunkedPrefill)
mock_ascend_attention_state = MagicMock()
mock_ascend_attention_state.PrefillNoCache = 0
mock_nz_tensor = MagicMock()
mock_model = MagicMock()
mock_nd_to_nz_spec.return_value = mock_nz_tensor
mock_npu_format_cast.return_value = mock_nz_tensor
self.builder.build(common_attn_metadata, mock_model)
@patch('vllm_ascend.attention.attention_v1.AscendMetadata')
@patch('vllm_ascend.attention.attention_v1.is_310p', return_value=False)
def test_build_non_310p(self, mock_is_310p, mock_ascend_metadata):
common_attn_metadata = AscendCommonAttentionMetadata(
query_start_loc=torch.tensor([0, 2, 5, 9]),
query_start_loc_cpu=torch.tensor([0, 2, 5, 9]),
seq_lens_cpu=torch.tensor([4, 5, 6]),
num_reqs=3,
num_actual_tokens=15,
max_query_len=6,
decode_token_per_req=torch.tensor([1, 1, 1]),
block_table_tensor=torch.zeros((10, 10)),
slot_mapping_cpu=torch.tensor(range(20)),
actual_seq_lengths_q=torch.tensor([0, 1, 2]),
positions=torch.tensor([10, 10]),
attn_mask=torch.ones((15, 15)),
spec_attn_mask=None,
attn_state=AscendAttentionState.ChunkedPrefill)
mock_model = MagicMock()
self.builder.build(common_attn_metadata, mock_model)
class TestAscendAttentionBackendImpl(TestBase):
def setUp(self):
self.layer = MagicMock()
self.layer.layer_name = "test_layer"
self.layer._k_scale_float = 1.0
self.layer._v_scale_float = 1.0
self.attention_type = MagicMock()
self.attention_type.DECODER = "decoder"
self.attention_type.ENCODER = "encoder"
self.attn_metadata = MagicMock()
self.attn_metadata.return_value = "1"
self.layer_no_quant = MagicMock(
spec=['layer_name', '_k_scale_float', '_v_scale_float'])
self.layer_no_quant.layer_name = "test_layer"
self.layer_no_quant._k_scale_float = 1.0
self.layer_no_quant._v_scale_float = 1.0
self.impl = AscendAttentionBackendImpl(
num_heads=8,
head_size=64,
scale=1.0,
num_kv_heads=8,
alibi_slopes=None,
sliding_window=None,
kv_cache_dtype="float16",
logits_soft_cap=None,
attn_type=self.attention_type.DECODER,
kv_sharing_target_layer_name=None)
self.impl_192 = AscendAttentionBackendImpl(
num_heads=8,
head_size=192,
scale=1.0,
num_kv_heads=8,
alibi_slopes=None,
sliding_window=None,
kv_cache_dtype="float16",
logits_soft_cap=None,
attn_type=self.attention_type.DECODER,
kv_sharing_target_layer_name=None)
self.impl_error = AscendAttentionBackendImpl(
num_heads=8,
head_size=192,
scale=1.0,
num_kv_heads=8,
alibi_slopes=None,
sliding_window=None,
kv_cache_dtype="float16",
logits_soft_cap=None,
attn_type=None,
kv_sharing_target_layer_name=None)
@patch('torch.ops.vllm.unified_ascend_attention_with_output')
def test_forward_trace_flag_true(self, mock_unified_attention):
"""Test forward pass when trace_flag is True"""
query = torch.randn(10, 8 * 64)
key = torch.randn(10, 8 * 64)
value = torch.randn(10, 8 * 64)
kv_cache = torch.empty(2, 0, 0, 8, 64)
metadata = self.attn_metadata
layer = self.layer
output = self.impl.forward(layer,
query,
key,
value,
kv_cache,
metadata,
trace_flag=True)
mock_unified_attention.assert_called_once()
assert output.shape == (10, 8 * 64)
@patch('torch_npu._npu_paged_attention_splitfuse')
def test_forward_with_quant_method(self, mock_paged_attention):
"""Test forward pass when layer has quant_method"""
query = torch.randn(10, 8 * 64)
key = torch.randn(10, 8 * 64)
value = torch.randn(10, 8 * 64)
k_cache = torch.ones(1, 10, 8, 64, dtype=torch.int8)
v_cache = torch.ones(1, 10, 8, 64, dtype=torch.int8)
kv_cache = [k_cache, v_cache]
ret_value = torch.ones(1, 1, 10, 8, 64, dtype=torch.int8)
metadata = MagicMock()
metadata.num_actual_tokens = torch.randn(10, 8 * 64)
metadata.block_tables = torch.randn(10, 8 * 64)
metadata.seq_lens = torch.randn(10, 8 * 64)
metadata.attn_mask = torch.randn(10, 8 * 64)
metadata.query_lens = torch.randn(10, 8 * 64)
layer = self.layer
layer.quant_method = MagicMock()
layer.quant_method.apply.return_value = ret_value
output = self.impl.forward(layer,
query,
key,
value,
kv_cache,
metadata,
trace_flag=False)
layer.quant_method.apply.assert_called_once()
assert output.shape == (10, 8 * 64)
def test_forward_no_attn_metadata(self):
"""Test forward pass when attn_metadata is None"""
query = torch.randn(10, 8 * 64)
key = torch.randn(10, 8 * 64)
value = torch.randn(10, 8 * 64)
kv_cache = torch.empty(2, 0, 0, 8, 64)
layer = self.layer_no_quant
output = self.impl.forward(layer,
query,
key,
value,
kv_cache,
None,
trace_flag=False)
assert output.shape == (10, 8 * 64)
@patch('torch_npu._npu_reshape_and_cache')
@patch('torch_npu._npu_flash_attention')
def test_forward_prefill_no_cache(self, mock_flash_attention,
mock_reshape_cache):
"""Test forward pass in PrefillNoCache state"""
query = torch.randn(10, 8 * 64)
key = torch.randn(10, 8 * 64)
value = torch.randn(10, 8 * 64)
kv_cache = torch.empty(2, 5, 128, 8, 64)
metadata = self.attn_metadata
metadata.attn_state = AscendAttentionState.PrefillNoCache
metadata.attn_mask = torch.randn(1, 1, 10, 10)
metadata.seq_lens = torch.tensor([10])
metadata.num_actual_tokens = 10
metadata.slot_mapping = torch.zeros(10, dtype=torch.long)
layer = self.layer_no_quant
# layer.quant_method.apply.return_value = metadata
print(self.layer_no_quant._v_scale_float)
output = self.impl.forward(layer,
query,
key,
value,
kv_cache,
metadata,
trace_flag=False)
mock_reshape_cache.assert_called_once()
mock_flash_attention.assert_called_once()
assert output.shape == (10, 8 * 64)
@patch('torch_npu._npu_reshape_and_cache')
@patch('torch_npu._npu_flash_attention_qlens')
def test_forward_prefill_cache_hit(self, mock_flash_attention_qlens,
mock_npu_reshape_and_cache):
"""Test forward pass in PrefillCacheHit state"""
query = torch.randn(10, 8 * 64)
key = torch.randn(10, 8 * 64)
value = torch.randn(10, 8 * 64)
kv_cache = torch.empty(2, 5, 128, 8, 64)
metadata = self.attn_metadata
metadata.attn_state = AscendAttentionState.PrefillCacheHit
metadata.attn_mask = torch.randn(1, 1, 10, 10)
metadata.query_lens = torch.tensor([10])
metadata.seq_lens = torch.tensor([10])
metadata.block_tables = torch.zeros(1, 5, dtype=torch.long)
metadata.num_actual_tokens = 10
metadata.slot_mapping = torch.zeros(10, dtype=torch.long)
layer = self.layer_no_quant
output = self.impl.forward(layer,
query,
key,
value,
kv_cache,
metadata,
trace_flag=False)
mock_flash_attention_qlens.assert_called_once()
assert output.shape == (10, 8 * 64)
@patch('torch_npu._npu_reshape_and_cache')
@patch('torch_npu._npu_paged_attention')
def test_forward_decode_only(self, mock_paged_attention,
mock_npu_reshape_and_cache):
"""Test forward pass in DecodeOnly state"""
query = torch.randn(10, 8 * 64)
key = torch.randn(10, 8 * 64)
value = torch.randn(10, 8 * 64)
kv_cache = torch.empty(2, 5, 128, 8, 64)
metadata = self.attn_metadata
metadata.attn_state = AscendAttentionState.DecodeOnly
metadata.seq_lens = torch.tensor([10])
metadata.block_tables = torch.zeros(1, 5, dtype=torch.long)
metadata.num_actual_tokens = 10
metadata.slot_mapping = torch.zeros(10, dtype=torch.long)
layer = self.layer_no_quant
output = self.impl.forward(layer,
query,
key,
value,
kv_cache,
metadata,
trace_flag=False)
mock_paged_attention.assert_called_once()
assert output.shape == (10, 8 * 64)
@patch('vllm_ascend.attention.attention_v1.is_310p', return_value=False)
@patch('torch_npu._npu_reshape_and_cache')
@patch('vllm_ascend.attention.attention_v1.vanilla_chunked_prefill')
def test_forward_head_size_192(self, mock_vanilla_prefill,
mock_npu_reshape_and_cache, mock_is_310p):
"""Test forward pass when head_size is 192"""
self.impl.head_size = 192
query = torch.randn(10, 8 * 192)
key = torch.randn(10, 8 * 192)
value = torch.randn(10, 8 * 192)
kv_cache = torch.empty(2, 5, 128, 8, 192)
metadata = self.attn_metadata
metadata.attn_mask = torch.randn(1, 1, 10, 10)
metadata.query_lens = torch.tensor([10])
metadata.seq_lens = torch.tensor([10])
metadata.block_tables = torch.zeros(1, 5, dtype=torch.long)
metadata.num_actual_tokens = 10
metadata.slot_mapping = torch.zeros(10, dtype=torch.long)
layer = self.layer_no_quant
mock_vanilla_prefill.return_value = MagicMock()
output = self.impl_192.forward(layer,
query,
key,
value,
kv_cache,
metadata,
trace_flag=False)
mock_vanilla_prefill.assert_called_once()
assert output.shape == (10, 8 * 192)
@patch('torch_npu._npu_reshape_and_cache')
@patch('torch_npu._npu_paged_attention_splitfuse')
def test_forward_normal_v1_situation(self, mock_paged_attention,
mock_npu_reshape_and_cache):
"""Test forward pass in normal V1 situation"""
query = torch.randn(10, 8 * 64)
key = torch.randn(10, 8 * 64)
value = torch.randn(10, 8 * 64)
kv_cache = torch.empty(2, 5, 128, 8, 64)
metadata = self.attn_metadata
metadata.attn_mask = torch.randn(1, 1, 10, 10)
metadata.query_lens = torch.tensor([10])
metadata.seq_lens = torch.tensor([10])
metadata.block_tables = torch.zeros(1, 5, dtype=torch.long)
metadata.num_actual_tokens = 10
metadata.slot_mapping = torch.zeros(10, dtype=torch.long)
layer = self.layer_no_quant
output = self.impl.forward(layer,
query,
key,
value,
kv_cache,
metadata,
trace_flag=False)
mock_paged_attention.assert_called_once()
assert output.shape == (10, 8 * 64)
@patch('torch_npu.npu_format_cast')
@patch('torch_npu._npu_reshape_and_cache')
@patch('torch_npu._npu_paged_attention_splitfuse')
@patch('vllm_ascend.attention.attention_v1.is_310p', return_value=True)
def test_forward_310p_device(self, mock_is_310p, mock_paged_attention,
mock_npu_reshape_and_cache,
mock_npu_format_cast):
"""Test forward pass on 310P device"""
query = torch.randn(10, 8 * 64)
key = torch.randn(10, 8 * 64)
value = torch.randn(10, 8 * 64)
kv_cache = torch.empty(2, 5, 128, 8, 64)
metadata = self.attn_metadata
metadata.attn_mask = torch.randn(1, 1, 10, 10)
metadata.query_lens = torch.tensor([10])
metadata.seq_lens = torch.tensor([10])
metadata.block_tables = torch.zeros(1, 5, dtype=torch.long)
metadata.num_actual_tokens = 10
metadata.slot_mapping = torch.zeros(10, dtype=torch.long)
layer = self.layer_no_quant
mock_npu_format_cast.return_value = metadata.attn_mask
output = self.impl.forward(layer,
query,
key,
value,
kv_cache,
metadata,
trace_flag=False)
mock_paged_attention.assert_called_once()
assert output.shape == (10, 8 * 64)
@patch('torch_npu._npu_reshape_and_cache')
def test_forward_raise_error(self, mock_paged_attention):
query = torch.randn(10, 8 * 64)
key = torch.randn(10, 8 * 64)
value = torch.randn(10, 8 * 64)
kv_cache = torch.empty(2, 5, 128, 8, 64)
metadata = self.attn_metadata
metadata.attn_mask = torch.randn(1, 1, 10, 10)
metadata.query_lens = torch.tensor([10])
metadata.seq_lens = torch.tensor([10])
metadata.block_tables = torch.zeros(1, 5, dtype=torch.long)
metadata.num_actual_tokens = 10
metadata.slot_mapping = torch.zeros(10, dtype=torch.long)
layer = self.layer_no_quant
with self.assertRaises(NotImplementedError):
self.impl_error.forward(layer,
query,
key,
value,
kv_cache,
metadata,
trace_flag=False)
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