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Drop 0.11.0 support (#4377)

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There is a lot hack code for v0.11.0, which makes the code hard to
upgrade to newer vLLM version. Since v0.11.0 will release soon. Let's
drop v0.11.0 support first. Then we'll upgrade to v0.11.2 soon.


- vLLM version: v0.11.0
- vLLM main:
2918c1b49c

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
This commit is contained in:
wangxiyuan
2025-11-24 17:08:20 +08:00
committed by GitHub
parent 41ddb06554
commit a1f142b7ad
80 changed files with 467 additions and 1755 deletions
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137
tests/ut/models/test_mla.py
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@@ -9,7 +9,6 @@ from vllm.model_executor.layers.mla import MLAModules
from tests.ut.base import TestBase
from vllm_ascend.models.layers.mla import (AscendMultiHeadLatentAttention,
IndexerWrapper)
from vllm_ascend.utils import vllm_version_is
class TestIndexerWrapper(TestBase):
@@ -85,68 +84,35 @@ class TestAscendMultiHeadLatentAttention(TestBase):
"vllm_ascend.models.layers.mla.get_tensor_model_parallel_world_size")
def test_initialization(self, mock_tp_size, mock_ascend_config,
mock_get_vllm_config):
if vllm_version_is("0.11.0"):
with patch("vllm_ascend.models.layers.mla.Attention",
return_value=True):
mock_tp_size.return_value = 1
mock_ascend_config.return_value.enable_shared_expert_dp = False
mock_vllm_config = MagicMock(spec=VllmConfig)
mock_vllm_config.model_config.hf_config = MagicMock(
num_hidden_layers=32, first_k_dense_replace=False)
mock_get_vllm_config.return_value = mock_vllm_config
mock_vllm_config.compilation_config = CompilationConfig()
attn = AscendMultiHeadLatentAttention(
hidden_size=self.hidden_size,
num_heads=self.num_heads,
scale=self.scale,
qk_nope_head_dim=self.qk_nope_head_dim,
qk_rope_head_dim=self.qk_rope_head_dim,
v_head_dim=self.v_head_dim,
q_lora_rank=self.q_lora_rank,
kv_lora_rank=self.kv_lora_rank,
mla_modules=self.mock_mla_modules,
cache_config=self.mock_cache_config,
quant_config=self.mock_quant_config,
prefix=self.prefix,
)
with patch("vllm_ascend.models.layers.mla.MLAAttention",
return_value=True):
mock_tp_size.return_value = 2
mock_ascend_config.return_value.enable_shared_expert_dp = True
mock_vllm_config = MagicMock(spec=VllmConfig)
mock_vllm_config.model_config.hf_config = MagicMock(
num_hidden_layers=32, first_k_dense_replace=True)
mock_get_vllm_config.return_value = mock_vllm_config
mock_vllm_config.compilation_config = CompilationConfig()
self.assertEqual(attn.hidden_size, self.hidden_size)
self.assertEqual(attn.kv_lora_rank, self.kv_lora_rank)
self.assertEqual(attn.debug_layer_idx, 0)
self.assertIsNotNone(attn.mla_attn)
self.assertIn(
self.prefix,
mock_vllm_config.compilation_config.static_forward_context)
else:
with patch("vllm_ascend.models.layers.mla.MLAAttention",
return_value=True):
mock_tp_size.return_value = 2
mock_ascend_config.return_value.enable_shared_expert_dp = True
mock_vllm_config = MagicMock(spec=VllmConfig)
mock_vllm_config.model_config.hf_config = MagicMock(
num_hidden_layers=32, first_k_dense_replace=True)
mock_get_vllm_config.return_value = mock_vllm_config
mock_vllm_config.compilation_config = CompilationConfig()
attn = AscendMultiHeadLatentAttention(
hidden_size=self.hidden_size,
num_heads=self.num_heads,
scale=self.scale,
qk_nope_head_dim=self.qk_nope_head_dim,
qk_rope_head_dim=self.qk_rope_head_dim,
v_head_dim=self.v_head_dim,
q_lora_rank=self.q_lora_rank,
kv_lora_rank=self.kv_lora_rank,
mla_modules=self.mock_mla_modules,
cache_config=self.mock_cache_config,
quant_config=self.mock_quant_config,
prefix=self.prefix,
)
attn = AscendMultiHeadLatentAttention(
hidden_size=self.hidden_size,
num_heads=self.num_heads,
scale=self.scale,
qk_nope_head_dim=self.qk_nope_head_dim,
qk_rope_head_dim=self.qk_rope_head_dim,
v_head_dim=self.v_head_dim,
q_lora_rank=self.q_lora_rank,
kv_lora_rank=self.kv_lora_rank,
mla_modules=self.mock_mla_modules,
cache_config=self.mock_cache_config,
quant_config=self.mock_quant_config,
prefix=self.prefix,
)
self.assertEqual(attn.tp_size, 2)
self.assertTrue(attn.enable_shared_expert_dp)
self.assertIsNotNone(attn.mla_attn)
self.assertEqual(attn.tp_size, 2)
self.assertTrue(attn.enable_shared_expert_dp)
self.assertIsNotNone(attn.mla_attn)
@patch("vllm_ascend.models.layers.mla.torch.ops.vllm.mla_forward")
@patch("vllm_ascend.models.layers.mla.get_current_vllm_config")
@@ -164,41 +130,22 @@ class TestAscendMultiHeadLatentAttention(TestBase):
num_hidden_layers=32, first_k_dense_replace=False)
mock_get_vllm_config.return_value = mock_vllm_config
mock_vllm_config.compilation_config = CompilationConfig()
if vllm_version_is("0.11.0"):
with patch("vllm_ascend.models.layers.mla.Attention",
return_value=True):
attn = AscendMultiHeadLatentAttention(
hidden_size=self.hidden_size,
num_heads=self.num_heads,
scale=self.scale,
qk_nope_head_dim=self.qk_nope_head_dim,
qk_rope_head_dim=self.qk_rope_head_dim,
v_head_dim=self.v_head_dim,
q_lora_rank=self.q_lora_rank,
kv_lora_rank=self.kv_lora_rank,
mla_modules=self.mock_mla_modules,
cache_config=self.mock_cache_config,
quant_config=self.mock_quant_config,
prefix=self.prefix,
)
else:
with patch("vllm_ascend.models.layers.mla.MLAAttention",
return_value=True):
attn = AscendMultiHeadLatentAttention(
hidden_size=self.hidden_size,
num_heads=self.num_heads,
scale=self.scale,
qk_nope_head_dim=self.qk_nope_head_dim,
qk_rope_head_dim=self.qk_rope_head_dim,
v_head_dim=self.v_head_dim,
q_lora_rank=self.q_lora_rank,
kv_lora_rank=self.kv_lora_rank,
mla_modules=self.mock_mla_modules,
cache_config=self.mock_cache_config,
quant_config=self.mock_quant_config,
prefix=self.prefix,
)
with patch("vllm_ascend.models.layers.mla.MLAAttention",
return_value=True):
attn = AscendMultiHeadLatentAttention(
hidden_size=self.hidden_size,
num_heads=self.num_heads,
scale=self.scale,
qk_nope_head_dim=self.qk_nope_head_dim,
qk_rope_head_dim=self.qk_rope_head_dim,
v_head_dim=self.v_head_dim,
q_lora_rank=self.q_lora_rank,
kv_lora_rank=self.kv_lora_rank,
mla_modules=self.mock_mla_modules,
cache_config=self.mock_cache_config,
quant_config=self.mock_quant_config,
prefix=self.prefix,
)
positions = torch.tensor([0, 1, 2])
hidden_states = torch.randn(3, self.hidden_size)