Files
vllm-ascend/tests/ut/attention/test_mla_v1.py
realliujiaxu f69a83b7ba [Feat] Flash comm allgher ep (#3334)
Support flash comm v1(Sequence Parallelism) for Allgather EP.

- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

---------

Signed-off-by: realliujiaxu <realliujiaxu@163.com>
Co-authored-by: zhaozx-cn <zhaozx2116@163.com>
2025-10-15 19:36:32 +08:00

678 lines
29 KiB
Python

from unittest.mock import MagicMock, patch
import torch
from vllm.distributed.parallel_state import GroupCoordinator
from vllm.model_executor.layers.linear import LinearBase
from tests.ut.base import TestBase
from vllm_ascend.attention.attention_v1 import AscendAttentionState
from vllm_ascend.attention.mla_v1 import (AscendMLABackend,
AscendMLADecodeMetadata,
AscendMLAImpl, AscendMLAMetadata,
AscendMLAMetadataBuilder,
AscendMLAPrefillMetadata)
class TestAscendMLABackend(TestBase):
def test_get_name(self):
self.assertEqual(AscendMLABackend.get_name(), "ASCEND_MLA")
def test_get_metadata_cls(self):
self.assertEqual(AscendMLABackend.get_metadata_cls(),
AscendMLAMetadata)
def test_get_builder_cls(self):
self.assertEqual(AscendMLABackend.get_builder_cls(),
AscendMLAMetadataBuilder)
def test_get_kv_cache_shape(self):
result = AscendMLABackend.get_kv_cache_shape(2, 4, 8, 128)
self.assertEqual(result, (2, 4, 8, 128))
def test_get_impl_cls(self):
result = AscendMLABackend.get_impl_cls()
self.assertEqual(result, AscendMLAImpl)
class TestAscendMLAPrefillMetadata(TestBase):
def test_ascend_mla_prefill_metadata_default(self):
attn_mask = torch.tensor([[1, 0], [1, 1]], dtype=torch.bool)
query_lens = [1, 2]
seq_lens = [2, 2]
context_lens = torch.tensor([1, 2])
input_positions = torch.tensor([0, 1, 0, 1])
query_start_loc = torch.tensor([0, 1, 3])
block_table = torch.tensor([[0, 1], [2, 3]])
max_query_len = 2
max_seq_lens = 2
metadata = AscendMLAPrefillMetadata(attn_mask=attn_mask,
query_lens=query_lens,
seq_lens=seq_lens,
context_lens=context_lens,
input_positions=input_positions,
query_start_loc=query_start_loc,
block_table=block_table,
max_query_len=max_query_len,
max_seq_lens=max_seq_lens)
self.assertIs(metadata.attn_mask, attn_mask)
self.assertEqual(metadata.query_lens, query_lens)
self.assertEqual(metadata.seq_lens, seq_lens)
self.assertIs(metadata.context_lens, context_lens)
self.assertIs(metadata.input_positions, input_positions)
self.assertIs(metadata.query_start_loc, query_start_loc)
self.assertIs(metadata.block_table, block_table)
self.assertEqual(metadata.max_query_len, max_query_len)
self.assertEqual(metadata.max_seq_lens, max_seq_lens)
self.assertIsNone(metadata.chunked_context)
def test_ascend_mla_prefill_metadata_with_chunked_context(self):
cu_seq_lens = torch.tensor([0, 2, 4])
starts = torch.tensor([0, 2])
seq_tot = [2, 2]
max_seq_lens = [2, 2]
workspace = torch.randn(2, 4)
chunk_seq_lens = torch.tensor([2, 2])
chunked_context = AscendMLAPrefillMetadata.ChunkedContextMetadata(
cu_seq_lens=cu_seq_lens,
starts=starts,
seq_tot=seq_tot,
max_seq_lens=max_seq_lens,
workspace=workspace,
chunk_seq_lens=chunk_seq_lens)
metadata = AscendMLAPrefillMetadata(
attn_mask=torch.tensor([[1, 0], [1, 1]], dtype=torch.bool),
query_lens=[1, 2],
seq_lens=[2, 2],
context_lens=torch.tensor([1, 2]),
input_positions=torch.tensor([0, 1, 0, 1]),
query_start_loc=torch.tensor([0, 1, 3]),
block_table=torch.tensor([[0, 1], [2, 3]]),
max_query_len=2,
max_seq_lens=2,
chunked_context=chunked_context)
self.assertIsNotNone(metadata.chunked_context)
self.assertIs(metadata.chunked_context.cu_seq_lens, cu_seq_lens)
self.assertIs(metadata.chunked_context.starts, starts)
self.assertEqual(metadata.chunked_context.seq_tot, seq_tot)
self.assertEqual(metadata.chunked_context.max_seq_lens, max_seq_lens)
self.assertIs(metadata.chunked_context.workspace, workspace)
self.assertIs(metadata.chunked_context.chunk_seq_lens, chunk_seq_lens)
class TestAscendMLADecodeMetadata(TestBase):
def test_ascend_mla_decode_metadata_default(self):
input_positions = torch.tensor([[1, 2, 3, 4], [1, 2, 3, 4]])
block_table = torch.tensor([[0, 3, 2, 1], [0, 2, 1, 3]])
seq_lens = torch.tensor([[2], [3]])
max_seq_lens = 4
seq_lens_list = [2, 3]
attn_mask = None
metadata = AscendMLADecodeMetadata(input_positions, block_table,
seq_lens, max_seq_lens,
seq_lens_list, attn_mask)
self.assertIs(metadata.input_positions, input_positions)
self.assertIs(metadata.block_table, block_table)
self.assertIs(metadata.seq_lens, seq_lens)
self.assertEqual(metadata.max_seq_lens, max_seq_lens)
self.assertEqual(metadata.seq_lens_list, seq_lens_list)
self.assertIsNone(attn_mask)
class TestAscendMLAMetadata(TestBase):
def test_ascend_mla_metadata_default(self):
num_actual_tokens = 100
slot_mapping = torch.randn(100, 4, 1024)
query_start_loc = torch.tensor([1, 2, 3, 4])
seq_lens = [30, 50]
block_tables = torch.randint(0, 100, (100, 4))
num_decodes = 4
num_decode_tokens = 8
num_prefills = 8
num_input_tokens = 2
query_lens = None
head_dim = None
attn_mask = None
attn_state = AscendAttentionState.ChunkedPrefill
decode = None
prefill = None
metadata = AscendMLAMetadata(num_actual_tokens, slot_mapping,
query_start_loc, seq_lens, block_tables,
num_decodes, num_decode_tokens,
num_prefills, num_input_tokens,
query_lens, head_dim, attn_mask,
attn_state, decode, prefill)
self.assertEqual(metadata.num_actual_tokens, num_actual_tokens)
self.assertIs(metadata.slot_mapping, slot_mapping)
self.assertIs(metadata.query_start_loc, query_start_loc)
self.assertEqual(metadata.seq_lens, seq_lens)
self.assertIs(metadata.block_tables, block_tables)
self.assertEqual(metadata.num_decodes, num_decodes)
self.assertEqual(metadata.num_decode_tokens, num_decode_tokens)
self.assertEqual(metadata.num_prefills, num_prefills)
self.assertEqual(metadata.num_input_tokens, num_input_tokens)
self.assertEqual(metadata.query_lens, query_lens)
self.assertEqual(metadata.head_dim, head_dim)
self.assertEqual(metadata.attn_mask, attn_mask)
self.assertEqual(metadata.attn_state, attn_state)
self.assertEqual(metadata.decode, decode)
self.assertEqual(metadata.prefill, prefill)
class TestAscendMLAMetadataBuilder(TestBase):
def test_ascend_mla_metadata_builder_default(self):
mock_vllm_config = MagicMock()
mock_vllm_config.model_config.max_model_len = 1024
mock_vllm_config.model_config.get_head_size.return_value = 64
mock_vllm_config.model_config.dtype = torch.float16
mock_vllm_config.cache_config.block_size = 16
mock_vllm_config.scheduler_config.max_num_seqs = 4
mock_vllm_config.scheduler_config.chunked_prefill_enabled = False
mock_device = 'cpu'
mock_vllm_config.speculative_config = None
ascend_config = MagicMock()
with patch("vllm_ascend.attention.mla_v1.get_ascend_config",
return_value=ascend_config):
builder = AscendMLAMetadataBuilder(None, None, mock_vllm_config,
mock_device)
self.assertEqual(builder.block_size,
mock_vllm_config.cache_config.block_size)
self.assertEqual(
builder.chunked_prefill_enabled,
mock_vllm_config.scheduler_config.chunked_prefill_enabled)
def test_ascend_mla_metadata_builder_spec_decode(self):
mock_vllm_config = MagicMock()
mock_vllm_config.model_config.max_model_len = 1024
mock_vllm_config.model_config.get_head_size.return_value = 64
mock_vllm_config.model_config.dtype = torch.float16
mock_vllm_config.cache_config.block_size = 16
mock_vllm_config.scheduler_config.max_num_seqs = 4
mock_vllm_config.scheduler_config.chunked_prefill_enabled = False
mock_device = 'cpu'
mock_spec_config = MagicMock()
mock_spec_config.num_speculative_tokens = 3
mock_vllm_config.speculative_config = mock_spec_config
ascend_config = MagicMock()
with patch("vllm_ascend.attention.mla_v1.get_ascend_config",
return_value=ascend_config):
builder = AscendMLAMetadataBuilder(None, None, mock_vllm_config,
mock_device)
self.assertEqual(builder.block_size,
mock_vllm_config.cache_config.block_size)
self.assertEqual(
builder.chunked_prefill_enabled,
mock_vllm_config.scheduler_config.chunked_prefill_enabled)
def test_reorder_batch(self):
ascend_config = MagicMock()
mock_vllm_config = MagicMock()
mock_vllm_config.model_config.max_model_len = 1024
mock_vllm_config.cache_config.block_size = 16
mock_vllm_config.scheduler_config.max_num_seqs = 4
mock_vllm_config.scheduler_config.chunked_prefill_enabled = False
mock_device = 'cpu'
mock_vllm_config.speculative_config = None
with patch("vllm_ascend.attention.mla_v1.get_ascend_config",
return_value=ascend_config):
builder = AscendMLAMetadataBuilder(None, None, mock_vllm_config,
mock_device)
builder.decode_threshold = 1
input_batch = MagicMock()
input_batch.req_ids = [0, 1, 2, 3]
scheduler_output = MagicMock()
scheduler_output.num_scheduled_tokens = {0: 1, 1: 3, 2: 1, 3: 2}
scheduler_output.scheduled_spec_decode_tokens = {
0: [],
1: [1],
2: [],
3: []
}
input_batch.swap_states = MagicMock()
modified = builder.reorder_batch(input_batch, scheduler_output)
self.assertTrue(modified)
input_batch.swap_states.assert_called_once_with(1, 2)
class TestAscendMLAImpl(TestBase):
@patch('vllm.distributed.parallel_state._TP',
new_callable=lambda: MagicMock(spec=GroupCoordinator))
@patch("vllm.distributed.get_tensor_model_parallel_world_size",
return_value=2)
@patch("vllm_ascend.attention.mla_v1.get_current_vllm_config")
@patch("vllm_ascend.attention.mla_v1.get_ascend_config")
def setUp(self, ascend_config, get_current_vllm_config, mock_get_tp_size,
mock_tp):
mock_tp.world_size = 2
vllm_config = MagicMock()
speculative_config = MagicMock()
model_config = MagicMock()
speculative_config.num_speculative_tokens = 4
vllm_config.speculative_config = speculative_config
model_config.dtype = torch.float16
vllm_config.model_config = model_config
get_current_vllm_config.return_value = vllm_config
num_heads = 256
head_size = 1024
scale = 0.1
num_kv_heads = 8
kv_cache_dtype = "auto"
kv_a_layernorm = MagicMock()
kv_a_layernorm.weight = torch.randn(96)
kv_a_layernorm.variance_epsilon = 1e-6
kwargs = {
"q_lora_rank": 64,
"kv_lora_rank": 32,
"qk_nope_head_dim": 64,
"qk_rope_head_dim": 32,
"qk_head_dim": 96,
"v_head_dim": 128,
"rotary_emb": MagicMock(),
"q_proj": MagicMock(),
"kv_b_proj": MagicMock(),
"o_proj": MagicMock(),
"kv_a_proj_with_mqa": MagicMock(),
"kv_a_layernorm": kv_a_layernorm,
}
self.impl = AscendMLAImpl(num_heads=num_heads,
head_size=head_size,
scale=scale,
num_kv_heads=num_kv_heads,
alibi_slopes=None,
sliding_window=None,
kv_cache_dtype=kv_cache_dtype,
blocksparse_params=None,
logits_soft_cap=None,
attn_type=None,
kv_sharing_target_layer_name=None,
**kwargs)
def test_init(self):
self.assertEqual(self.impl.num_heads, 256)
self.assertEqual(self.impl.head_size, 1024)
self.assertEqual(self.impl.scale, 0.1)
self.assertEqual(self.impl.num_kv_heads, 8)
self.assertEqual(self.impl.kv_cache_dtype, "auto")
self.assertEqual(self.impl.q_lora_rank, 64)
self.assertEqual(self.impl.kv_lora_rank, 32)
self.assertEqual(self.impl.qk_nope_head_dim, 64)
self.assertEqual(self.impl.qk_rope_head_dim, 32)
self.assertEqual(self.impl.qk_head_dim, 96)
self.assertEqual(self.impl.v_head_dim, 128)
self.assertIsNotNone(self.impl.rotary_emb)
self.assertIsNotNone(self.impl.q_proj)
self.assertIsNotNone(self.impl.kv_b_proj)
self.assertIsNotNone(self.impl.o_proj)
self.assertIsNotNone(self.impl.kv_a_proj_with_mqa)
self.assertIsNotNone(self.impl.kv_a_layernorm)
self.assertEqual(self.impl.num_queries_per_kv, 32)
self.assertEqual(self.impl.tp_size, 2)
def test_v_up_proj(self):
batch_size = 4
x = torch.randn(batch_size, self.impl.num_heads,
self.impl.kv_lora_rank)
if not hasattr(self.impl, 'W_UV') or self.impl.W_UV is None:
self.impl.W_UV = torch.randn(self.impl.num_heads,
self.impl.kv_lora_rank,
self.impl.v_head_dim)
result = self.impl._v_up_proj(x)
self.assertEqual(result.shape[0], batch_size)
self.assertEqual(result.shape[1],
self.impl.num_heads * self.impl.v_head_dim)
def test_q_proj_and_k_up_proj(self):
batch_size = 4
x = torch.randn(batch_size, self.impl.num_heads, self.impl.qk_head_dim)
q_proj_output = torch.randn(batch_size, self.impl.num_heads,
self.impl.qk_head_dim)
self.impl.q_proj.return_value = (q_proj_output, )
if not hasattr(self.impl, 'W_UK_T') or self.impl.W_UK_T is None:
self.impl.W_UK_T = torch.randn(self.impl.num_heads,
self.impl.qk_nope_head_dim,
self.impl.kv_lora_rank)
result = self.impl._q_proj_and_k_up_proj(x)
ql_nope, q_pe = result
self.assertEqual(ql_nope.shape[0], batch_size)
self.assertEqual(ql_nope.shape[1], self.impl.num_heads)
self.assertEqual(ql_nope.shape[2], self.impl.kv_lora_rank)
self.assertEqual(q_pe.shape[0], batch_size)
self.assertEqual(q_pe.shape[1], self.impl.num_heads)
self.assertEqual(q_pe.shape[2], self.impl.qk_rope_head_dim)
@patch('torch_npu.npu_format_cast')
def test_process_weights_after_loading(self, mock_format_cast):
layer = MagicMock(spec=LinearBase)
layer.input_size_per_partition = 10
quant_method = MagicMock()
apply = MagicMock()
quant_method.apply = apply
layer.quant_method = quant_method
shape_0 = self.impl.num_heads * (self.impl.qk_nope_head_dim +
self.impl.v_head_dim)
shape_1 = self.impl.kv_lora_rank
layer.weight = torch.randn(shape_0, shape_1)
self.impl.kv_b_proj = layer
apply.return_value = layer.weight.T
mock_format_cast.return_value = layer.weight
self.impl.process_weights_after_loading(torch.bfloat16)
self.assertEqual(self.impl.W_UK_T.shape[0], self.impl.num_heads)
self.assertEqual(self.impl.W_UK_T.shape[1], self.impl.qk_nope_head_dim)
self.assertEqual(self.impl.W_UK_T.shape[2], self.impl.kv_lora_rank)
self.assertEqual(self.impl.W_UV.shape[0], self.impl.num_heads)
self.assertEqual(self.impl.W_UV.shape[1], self.impl.kv_lora_rank)
self.assertEqual(self.impl.W_UV.shape[2], self.impl.v_head_dim)
def test_compute_prefill_context_none(self):
batch_size = 4
kv_cache = torch.randn(10, 1, 1, 192)
query = torch.randn(batch_size, self.impl.num_heads,
self.impl.qk_head_dim)
metadata = MagicMock()
metadata.prefill = None
prefix_out = torch.randn(2, 16, 128)
prefix_lse = torch.randn(2, 16, 8)
q_pe = query[..., self.impl.qk_nope_head_dim:]
q_nope = query[..., :self.impl.qk_nope_head_dim]
out, lse = self.impl._compute_prefill_context(q_nope, q_pe, kv_cache,
32, metadata, prefix_out,
prefix_lse)
self.assertTrue(torch.equal(prefix_out, out))
self.assertTrue(torch.equal(prefix_lse, lse))
@patch("torch_npu.atb.npu_paged_cache_load")
@patch("torch_npu.atb.npu_ring_mla")
def test_compute_prefill_context(self, mock_ring, mock_load):
S, N, D, VD = 2, self.impl.num_heads, self.impl.qk_head_dim, self.impl.v_head_dim
_, AND = self.impl.qk_rope_head_dim, self.impl.qk_nope_head_dim
latent_kv_dim = self.impl.kv_lora_rank
num_blocks, block_size = 100, 20
query = torch.randn(S, N, D)
q_nope = query[..., :self.impl.qk_nope_head_dim]
q_pe = query[..., self.impl.qk_nope_head_dim:]
kv_cache_0 = torch.randn(num_blocks, block_size, N, latent_kv_dim)
kv_cache_1 = torch.randn(num_blocks, block_size, N, D)
kv_cache = [kv_cache_0, kv_cache_1]
prefix_out = torch.randn(S, N, 128)
prefix_lse = torch.randn(S, N)
self.impl.kv_b_proj.return_value = (torch.randn(8, N, VD + AND), )
chunk_ctx = MagicMock()
chunk_ctx.seq_tot = [8]
chunk_ctx.chunk_seq_lens = [torch.tensor([8])]
chunk_ctx.starts = [torch.tensor([0])]
prefill_meta = MagicMock()
prefill_meta.chunked_context = chunk_ctx
prefill_meta.query_lens = [8]
prefill_meta.block_table = torch.randint(0, 100, (S, 4))
meta = MagicMock()
meta.prefill = prefill_meta
self.impl.prefill_mask = torch.triu(
torch.ones(512, 512, device=q_nope.device, dtype=q_nope.dtype), 1)
out, lse = self.impl._compute_prefill_context(q_nope, q_pe, kv_cache,
32, meta, prefix_out,
prefix_lse)
mock_load.assert_called_once()
mock_ring.assert_called_once()
self.assertEqual(out.shape, prefix_out.shape)
self.assertEqual(lse.shape, prefix_lse.shape)
@patch('vllm_ascend.attention.mla_v1.get_forward_context')
@patch("vllm_ascend.attention.mla_v1.AscendMLAImpl._v_up_proj")
@patch("torch_npu.npu_fused_infer_attention_score")
def test_forward_decode_without_graph(self,
mock_npu_fused_infer_attention_score,
mock_up_proj,
mock_get_forward_context):
num_tokens = 100
block_size = 4
q_nope = torch.randn(num_tokens, self.impl.num_heads,
self.impl.qk_nope_head_dim)
q_pe = torch.randn(num_tokens, self.impl.num_heads,
self.impl.qk_rope_head_dim)
k_nope = torch.randn(num_tokens, self.impl.num_heads,
self.impl.qk_nope_head_dim)
k_pe = torch.randn(num_tokens, self.impl.num_heads,
self.impl.qk_rope_head_dim)
metadata = MagicMock()
metadata.decode = MagicMock()
metadata.decode.block_table = MagicMock()
metadata.decode.seq_lens = 10
mock_npu_fused_infer_attention_score.return_value = [
torch.randn(num_tokens, self.impl.num_heads,
self.impl.kv_lora_rank), None
]
mock_up_proj.return_value = torch.randn(num_tokens,
self.impl.num_heads,
self.impl.v_head_dim)
mock_get_forward_context.return_value = MagicMock(capturing=False)
result = self.impl._forward_decode(q_nope, q_pe, k_nope, k_pe,
block_size, metadata)
self.assertEqual(result.shape[0], num_tokens)
self.assertEqual(result.shape[1], self.impl.num_heads)
self.assertEqual(result.shape[2], self.impl.v_head_dim)
mock_up_proj.assert_called_once()
mock_npu_fused_infer_attention_score.assert_called_once()
@patch("torch.ops.vllm.maybe_all_gather_and_maybe_unpad")
@patch("vllm_ascend.attention.mla_v1.maybe_npu_prefetch")
def test_mla_preprocess(self, magic_npu_fetch,
mock_maybe_all_gather_and_maybe_unpad):
magic_npu_fetch.return_value = MagicMock()
mock_maybe_all_gather_and_maybe_unpad.side_effect = lambda x, label: x
batch_size = 4
seq_len = 8
hidden_size = 1024
hidden_states = torch.randn(batch_size * seq_len, hidden_size)
kv_cache = MagicMock()
attn_metadata = MagicMock()
attn_metadata.num_decodes = 2
attn_metadata.num_prefills = 2
attn_metadata.num_decode_tokens = 2
attn_metadata.num_actual_tokens = 4
num_prefill_tokens = 2
attn_metadata.slot_mapping = torch.arange(4)
attn_metadata.decode.cos = torch.randn(2, 64)
attn_metadata.decode.sin = torch.randn(2, 64)
attn_metadata.prefill.cos = torch.randn(2, 64)
attn_metadata.prefill.sin = torch.randn(2, 64)
self.impl.q_a_proj = MagicMock()
self.impl.q_a_layernorm = MagicMock()
self.impl.q_a_layernorm.return_value = torch.randn(
attn_metadata.num_actual_tokens, self.impl.num_heads,
self.impl.qk_rope_head_dim)
self.impl.kv_a_proj_with_mqa = MagicMock()
self.impl.kv_a_proj_with_mqa.return_value = [
torch.randn(num_prefill_tokens, self.impl.num_heads,
self.impl.qk_nope_head_dim + self.impl.kv_lora_rank)
]
self.impl.q_proj = MagicMock()
self.impl.q_proj.return_value = [
torch.randn(num_prefill_tokens, self.impl.num_heads,
self.impl.qk_head_dim)
]
self.impl.kv_b_proj = MagicMock()
self.impl.kv_b_proj.return_value = [
torch.randn(num_prefill_tokens, self.impl.num_heads,
self.impl.v_head_dim + self.impl.qk_nope_head_dim)
]
self.impl.rope_single = MagicMock(side_effect=lambda x, cos, sin: x)
self.impl.exec_kv_decode = MagicMock()
self.impl.exec_kv_decode.return_value = [MagicMock(), MagicMock()]
self.impl.exec_kv_prefill = MagicMock()
self.impl.exec_kv_prefill.return_value = [
torch.randn(num_prefill_tokens, self.impl.num_heads,
self.impl.qk_rope_head_dim),
torch.randn(num_prefill_tokens, self.impl.num_heads,
self.impl.kv_lora_rank)
]
self.impl._q_proj_and_k_up_proj = MagicMock()
self.impl._q_proj_and_k_up_proj.return_value = [
MagicMock(), MagicMock()
]
self.impl.num_kv_heads = self.impl.num_heads
decode_res, prefill_res = self.impl._mla_preprocess(
"mock_layer",
hidden_states,
kv_cache,
attn_metadata,
need_gather_q_kv=False)
self.assertIsNotNone(decode_res)
self.assertIsNotNone(prefill_res)
@patch("torch_npu.npu_kv_rmsnorm_rope_cache")
def test_exec_kv_prefill(self, mock_kv_rmsnorm_rope_cache):
B = 2
N = self.impl.num_kv_heads
D = self.impl.kv_lora_rank + self.impl.qk_rope_head_dim
kv_no_split = torch.randn(B, N, D)
self.impl.enable_kv_nz = None
self.impl.kv_a_layernorm.weight = MagicMock()
self.impl.kv_a_layernorm.variance_epsilon = MagicMock()
cos = MagicMock()
sin = MagicMock()
slots = MagicMock()
kv_cache = [MagicMock(), MagicMock()]
mock_kv_rmsnorm_rope_cache.return_value = [
None, None,
torch.randn(B, N, 1, self.impl.qk_rope_head_dim),
torch.randn(B, N, 1, self.impl.kv_lora_rank)
]
k_pe, k_nope = self.impl.exec_kv_prefill(kv_no_split, cos, sin,
kv_cache, slots)
self.assertEqual(k_pe.shape[-1], self.impl.qk_rope_head_dim)
self.assertEqual(k_nope.shape[-1], self.impl.kv_lora_rank)
@patch("torch_npu.npu_kv_rmsnorm_rope_cache")
def test_exec_kv_decode(self, mock_kv_rmsnorm_rope_cache):
B = 2
N = self.impl.num_kv_heads
D = self.impl.kv_lora_rank + self.impl.qk_rope_head_dim
kv_no_split = torch.randn(B, N, D)
self.impl.enable_kv_nz = None
self.impl.kv_a_layernorm.weight = MagicMock()
self.impl.kv_a_layernorm.variance_epsilon = MagicMock()
cos = MagicMock()
sin = MagicMock()
slots = MagicMock()
kv_cache = [MagicMock(), MagicMock()]
mock_kv_rmsnorm_rope_cache.return_value = [
torch.randn(B, N, 1, self.impl.qk_rope_head_dim),
torch.randn(B, N, 1, self.impl.kv_lora_rank), None, None
]
k_pe, k_nope = self.impl.exec_kv_decode(kv_no_split, cos, sin,
kv_cache, slots)
self.assertEqual(k_pe.shape[-1], self.impl.qk_rope_head_dim)
self.assertEqual(k_nope.shape[-1], self.impl.kv_lora_rank)
@patch('vllm_ascend.attention.mla_v1.get_forward_context')
@patch("torch.npu.stream")
@patch("vllm_ascend.attention.mla_v1.get_multistream_comm_context")
@patch("torch_npu.npu_fused_infer_attention_score")
def test_forward_decode(self, mock_npu_fused_infer_attention_score,
mock_get_multistream_comm_context, mock_npu_stream,
mock_get_forward_context):
B = 2
N = self.impl.num_kv_heads
BS = 100
HD = self.impl.v_head_dim
self.impl.kv_lora_rank = 256
self.impl.spec_token_num = 1
self.impl._v_up_proj = MagicMock()
self.impl._v_up_proj.return_value = torch.randn(B, N, HD)
q_nope = torch.randn(B, N, self.impl.qk_nope_head_dim)
q_pe = torch.randn(B, N, self.impl.qk_rope_head_dim)
k_nope = torch.randn(BS, N, self.impl.kv_lora_rank)
k_pe = torch.randn(BS, N, self.impl.qk_rope_head_dim)
attn_metadata = MagicMock()
attn_metadata.attn_state = AscendAttentionState.SpecDecoding
attn_metadata.decode = MagicMock()
attn_metadata.decode.actual_seq_lengths_q = MagicMock()
attn_metadata.decode.seq_lens_list = MagicMock()
self.impl.enable_kv_nz = True
mock_npu_fused_infer_attention_score.return_value = [
torch.randn(B, N, self.impl.kv_lora_rank), None
]
mock_get_multistream_comm_context.return_value = None
mock_get_forward_context.return_value = MagicMock(capturing=False)
result = self.impl._forward_decode(q_nope, q_pe, k_nope, k_pe, BS,
attn_metadata)
self.assertEqual(result.shape[0], B)
self.assertEqual(result.shape[1], N)
self.assertEqual(result.shape[2], HD)
self.impl.enable_kv_nz = False
attn_metadata.attn_state = None
mock_return_value = MagicMock()
mock_get_multistream_comm_context.return_value = mock_return_value
mock_return_value.before_comm_event = MagicMock()
mock_return_value.comm_stream = MagicMock()
mock_npu_stream.return_value = MagicMock()
result = self.impl._forward_decode(q_nope, q_pe, k_nope, k_pe, BS,
attn_metadata)
self.assertEqual(result.shape[0], B)
self.assertEqual(result.shape[1], N)
self.assertEqual(result.shape[2], HD)