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init v0.11.0rc0

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luopingyi
2025-10-14 10:38:28 +08:00
parent 67afd0ea78
commit 66dc16f966
278 changed files with 28130 additions and 11708 deletions
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114
tests/ut/models/conftest.py Normal file
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@@ -0,0 +1,114 @@
from types import SimpleNamespace
from unittest.mock import MagicMock, Mock, patch
import pytest
import torch
from transformers import PretrainedConfig
from vllm.config import CacheConfig, EPLBConfig, ParallelConfig
from vllm.distributed.parallel_state import GroupCoordinator
@pytest.fixture
def base_config():
config = PretrainedConfig(
hidden_size=128,
num_attention_heads=8,
num_hidden_layers=2,
intermediate_size=256,
hidden_act="silu",
rms_norm_eps=1e-6,
rope_theta=10000.0,
max_position_embeddings=2048,
n_routed_experts=4,
n_shared_experts=1,
moe_intermediate_size=256,
num_experts_per_tok=2,
routed_scaling_factor=1.0,
first_k_dense_replace=0,
moe_layer_freq=1,
kv_lora_rank=16,
qk_nope_head_dim=16,
qk_rope_head_dim=16,
v_head_dim=32,
topk_method="noaux_tc",
scoring_func="softmax",
norm_topk_prob=True,
n_group=1,
topk_group=1,
vocab_size=10000,
)
return config
@pytest.fixture
def vllm_config(base_config):
model_config = SimpleNamespace(
hf_config=base_config,
tensor_parallel_size=1,
dtype=torch.float32,
use_mla=True,
quant_config=None,
max_model_len=2048,
)
parallel_config = MagicMock(spec=ParallelConfig)
eplb_config = MagicMock(spec=EPLBConfig)
eplb_config.num_redundant_experts = 0
parallel_config.eplb_config = eplb_config
cache_config = CacheConfig()
vllm_config = Mock()
vllm_config.model_config = model_config
vllm_config.cache_config = cache_config
vllm_config.quant_config = None
vllm_config.parallel_config = parallel_config
return vllm_config
@pytest.fixture
def mock_distributed():
tp_group = Mock(spec=GroupCoordinator)
tp_group.rank_in_group = 0
tp_group.world_size = 1
tp_group.device_group = Mock()
dp_group = Mock(spec=GroupCoordinator)
dp_group.rank_in_group = 0
dp_group.world_size = 1
ep_group = Mock(spec=GroupCoordinator)
ep_group.rank_in_group = 0
ep_group.world_size = 1
ep_group.device_group = Mock()
ep_group.device_group.rank.return_value = 0
ep_group.device_group.size.return_value = 1
pp_group = Mock(spec=GroupCoordinator)
pp_group.rank_in_group = 0
pp_group.world_size = 1
mock_vllm_config = Mock()
mock_vllm_config.scheduler_config = Mock(max_num_seqs=256)
mock_vllm_config.model_config = Mock(max_model_len=2048, quant_config=None)
with patch("vllm_ascend.models.deepseek_v2.get_tensor_model_parallel_rank", return_value=0), \
patch("vllm_ascend.models.deepseek_v2.get_tensor_model_parallel_world_size", return_value=1), \
patch("vllm_ascend.models.deepseek_v2.get_tp_group", return_value=tp_group), \
patch("vllm_ascend.models.deepseek_v2.get_pp_group", return_value=pp_group), \
patch("vllm_ascend.models.deepseek_v2.get_pp_group",
return_value=Mock(is_first_rank=False, is_last_rank=False)), \
patch("vllm_ascend.ops.fused_moe.get_current_vllm_config", return_value=mock_vllm_config), \
patch("vllm_ascend.ops.moe.token_dispatcher.torch.distributed.get_rank", return_value=0), \
patch("vllm_ascend.ops.moe.token_dispatcher.get_ascend_soc_version", return_value=None), \
patch.dict("vllm.distributed.parallel_state.__dict__", _TP=tp_group, _EP=ep_group, _DP=dp_group,
_PP=pp_group), \
patch.dict("vllm_ascend.distributed.parallel_state.__dict__", _MC2=ep_group), \
patch("torch.npu.current_device", return_value=0):
yield
@pytest.fixture
def mock_forward_context():
forward_context = Mock(in_profile_run=False, with_prefill=False)
with patch("vllm_ascend.models.deepseek_v2.get_forward_context",
return_value=forward_context):
yield

13
tests/ut/models/test_deepseek_mtp.py
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@@ -13,10 +13,13 @@ from vllm_ascend.models.deepseek_mtp import (
class TestCustomDeepSeekMultiTokenPredictorLayer(PytestBase):
@pytest.fixture
def setup_mtp_layer(self, mocker: MockerFixture):
def setup_mtp_layer(self, mocker: MockerFixture, vllm_config: VllmConfig,
mock_distributed):
config = PretrainedConfig(vocab_size=1000,
hidden_size=768,
rms_norm_eps=1e-5)
mocker.patch("vllm_ascend.models.deepseek_mtp.get_current_vllm_config",
return_value=vllm_config)
mocker.patch(
"vllm.model_executor.layers.vocab_parallel_embedding.VocabParallelEmbedding.__init__",
return_value=None)
@@ -29,15 +32,15 @@ class TestCustomDeepSeekMultiTokenPredictorLayer(PytestBase):
"vllm_ascend.models.deepseek_mtp.CustomDeepSeekShareHead.__init__",
return_value=None)
mocker_deepseek_v2_decode_layer = mocker.patch(
"vllm_ascend.models.deepseek_v2.CustomDeepseekV2DecoderLayer.__init__",
"vllm.model_executor.models.deepseek_v2.DeepseekV2DecoderLayer.__init__",
return_value=None)
mocker.patch(
"vllm_ascend.ops.vocab_parallel_embedding.AscendVocabParallelEmbedding.__init__",
return_value=None)
mocker.patch("vllm_ascend.utils.get_ascend_config",
mocker.patch("vllm_ascend.models.deepseek_v2.get_ascend_config",
return_value=mocker.Mock())
mtp_layer = CustomDeepSeekMultiTokenPredictorLayer(config, "", None)
mtp_layer = CustomDeepSeekMultiTokenPredictorLayer(config, "0", None)
mocker_deepseek_v2_decode_layer.assert_called_once()
return mtp_layer
@@ -165,8 +168,6 @@ class TestCustomDeepSeekMTP(PytestBase):
mocker.patch(
"vllm_ascend.models.deepseek_mtp.CustomDeepSeekMultiTokenPredictorLayer.__call__",
return_value=None)
mocker.patch("vllm.model_executor.layers.sampler.get_sampler",
return_value=None)
mocker.patch(
"vllm_ascend.ops.vocab_parallel_embedding.AscendVocabParallelEmbedding.__init__",
return_value=None)

208
tests/ut/models/test_deepseek_v2.py
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@@ -12,169 +12,23 @@
# limitations under the License.
# This file is a part of the vllm-ascend project.
#
from types import SimpleNamespace
from unittest.mock import Mock, patch
import pytest
import torch
from transformers import PretrainedConfig
from vllm.config import CacheConfig
from vllm.distributed.parallel_state import GroupCoordinator
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
from vllm_ascend.models.deepseek_v2 import (
CustomDeepseekV2MergedReplicatedLinear, CustomDeepseekV2MLAAttention,
CustomDeepseekV2MLP, CustomDeepseekV2MoE,
CustomDeepseekV2RowParallelLinear,
CustomDeepseekV2RowParallelLinearReplaceAllreduce,
CustomDeepseekV2SiluAndMul, LogitsProcessor, ParallelLMHead)
from vllm_ascend.models.deepseek_v2 import (CustomDeepseekV2MLAAttention,
CustomDeepseekV2RowParallelLinear)
@pytest.fixture
def base_config():
config = PretrainedConfig(
hidden_size=128,
num_attention_heads=8,
num_hidden_layers=2,
intermediate_size=256,
hidden_act="silu",
rms_norm_eps=1e-6,
rope_theta=10000.0,
max_position_embeddings=2048,
n_routed_experts=4,
n_shared_experts=1,
moe_intermediate_size=256,
num_experts_per_tok=2,
routed_scaling_factor=1.0,
first_k_dense_replace=0,
moe_layer_freq=1,
kv_lora_rank=16,
qk_nope_head_dim=16,
qk_rope_head_dim=16,
v_head_dim=32,
topk_method="noaux_tc",
scoring_func="softmax",
norm_topk_prob=True,
n_group=1,
topk_group=1,
vocab_size=10000,
)
return config
@pytest.fixture
def vllm_config(base_config):
model_config = SimpleNamespace(
hf_config=base_config,
tensor_parallel_size=1,
dtype=torch.float32,
use_mla=False,
quant_config=None,
max_model_len=2048,
)
cache_config = CacheConfig()
vllm_config = Mock()
vllm_config.model_config = model_config
vllm_config.cache_config = cache_config
vllm_config.quant_config = None
return vllm_config
@pytest.fixture
def mock_distributed():
tp_group = Mock(spec=GroupCoordinator)
tp_group.rank_in_group = 0
tp_group.world_size = 1
tp_group.device_group = Mock()
dp_group = Mock(spec=GroupCoordinator)
dp_group.rank_in_group = 0
dp_group.world_size = 1
ep_group = Mock(spec=GroupCoordinator)
ep_group.rank_in_group = 0
ep_group.world_size = 1
pp_group = Mock(spec=GroupCoordinator)
pp_group.rank_in_group = 0
pp_group.world_size = 1
mock_vllm_config = Mock()
mock_vllm_config.scheduler_config = Mock(max_num_seqs=256)
mock_vllm_config.model_config = Mock(max_model_len=2048, quant_config=None)
with patch("vllm_ascend.models.deepseek_v2.get_tensor_model_parallel_rank", return_value=0), \
patch("vllm_ascend.models.deepseek_v2.get_tensor_model_parallel_world_size", return_value=1), \
patch("vllm_ascend.models.deepseek_v2.get_tp_group", return_value=tp_group), \
patch("vllm_ascend.models.deepseek_v2.get_ep_group", return_value=ep_group), \
patch("vllm_ascend.models.deepseek_v2.get_dp_group", return_value=dp_group), \
patch("vllm_ascend.models.deepseek_v2.get_pp_group", return_value=pp_group), \
patch("vllm_ascend.models.deepseek_v2.get_pp_group",
return_value=Mock(is_first_rank=False, is_last_rank=False)), \
patch("vllm_ascend.ops.fused_moe.get_current_vllm_config", return_value=mock_vllm_config), \
patch.dict("vllm.distributed.parallel_state.__dict__", _TP=tp_group, _EP=ep_group, _DP=dp_group,
_PP=pp_group), \
patch.dict("vllm_ascend.distributed.parallel_state.__dict__", _MC2=ep_group), \
patch("torch.npu.current_device", return_value=0):
yield
@pytest.fixture
def mock_forward_context():
forward_context = Mock(in_profile_run=False, with_prefill=False)
with patch("vllm_ascend.models.deepseek_v2.get_forward_context",
return_value=forward_context):
yield
def test_custom_deepseek_v2_silu_and_mul():
torch.set_default_device("cpu")
silu = CustomDeepseekV2SiluAndMul()
assert silu.weight_scale is None
x = torch.randn(2, 4)
output = silu.forward_oot(x)
assert output.shape == (2, 2)
weight_scale = Mock(return_value=torch.tensor(0.1))
silu = CustomDeepseekV2SiluAndMul(weight_scale=weight_scale)
quant_x = torch.randint(-128, 127, (2, 4), dtype=torch.int32)
dynamic_scale = torch.randn(2, 1)
with patch("torch_npu.npu_dequant_swiglu_quant",
return_value=torch.randn(2, 4)):
output = silu.forward_oot((quant_x, dynamic_scale))
assert output.shape == (2, 4)
def test_custom_deepseek_v2_merged_replicated_linear(mock_distributed):
linear = CustomDeepseekV2MergedReplicatedLinear(input_size=128,
output_sizes=[64, 64],
bias=False,
quant_config=None)
assert linear.output_sizes == [64, 64]
param = Mock()
param.data = torch.zeros(128, 128)
param.output_dim = 1
param.is_gguf_weight = False
param.is_gguf_weight_type = False
loaded_weight = torch.randn(128, 64)
linear.weight_loader(param, loaded_weight, loaded_shard_id=0)
with pytest.raises(AssertionError):
linear.weight_loader(param, torch.randn(128, 32), loaded_shard_id=0)
@pytest.mark.parametrize("cls", [
CustomDeepseekV2RowParallelLinearReplaceAllreduce,
CustomDeepseekV2RowParallelLinear
])
@pytest.mark.parametrize("cls", [CustomDeepseekV2RowParallelLinear])
def test_row_parallel_linear(cls, mock_distributed):
linear = cls(input_size=128, output_size=64, bias=False, quant_config=None)
linear.quant_method = Mock()
linear.quant_method.apply.return_value = torch.randn(2, 4, 64)
input_ = torch.randn(2, 4, 128)
with patch("vllm_ascend.models.deepseek_v2.split_tensor_along_last_dim",
return_value=[torch.randn(2, 4, 64)]):
@@ -187,52 +41,10 @@ def test_row_parallel_linear(cls, mock_distributed):
assert output[0].shape == (2, 4, 64)
def test_custom_deepseek_v2_mlp(mock_distributed, base_config):
mlp = CustomDeepseekV2MLP(hidden_size=128,
intermediate_size=256,
hidden_act="silu",
quant_config=None)
assert isinstance(mlp.act_fn, CustomDeepseekV2SiluAndMul)
x = torch.randn(2, 4, 128)
output = mlp(x)
assert output.shape == (2, 4, 128)
with patch("vllm_ascend.models.deepseek_v2.QuantizationConfig"
) as mock_quant_config:
mock_quant_config.name = "w8a8dynamic"
with pytest.raises(NotImplementedError):
CustomDeepseekV2MLP(hidden_size=128,
intermediate_size=256,
hidden_act="silu",
quant_config=mock_quant_config,
force_replicate=False)
with pytest.raises(ValueError):
CustomDeepseekV2MLP(hidden_size=128,
intermediate_size=256,
hidden_act="relu",
quant_config=None)
def test_custom_deepseek_v2_moe(mock_distributed, base_config,
mock_forward_context):
base_config.n_shared_experts = 1
moe = CustomDeepseekV2MoE(config=base_config,
quant_config=None,
prefix="mlp")
assert moe.top_k == 2
x = torch.randn(2, 4, 128)
attn_metadata = Mock(num_prefills=1)
with patch("vllm_ascend.ops.fused_moe.AscendFusedMoE.__call__",
return_value=(torch.randn(2, 4, 128), torch.randn(2, 4, 128))):
output = moe(x, attn_metadata)
assert output.shape == (2, 4, 128)
@patch("torch.ops.vllm.mla_forward")
@patch("torch_npu.npu_rms_norm")
def test_custom_deepseek_v2_mla_attention(mock_rms_norm, mock_distributed,
base_config):
def test_custom_deepseek_v2_mla_attention(mock_rms_norm, mock_mla_forward,
mock_distributed, base_config):
mock_rms_norm.return_value = (torch.randn(2, 128), torch.randn(2, 128))
attn = CustomDeepseekV2MLAAttention(config=base_config,
@@ -253,8 +65,8 @@ def test_custom_deepseek_v2_mla_attention(mock_rms_norm, mock_distributed,
with patch.object(attn.mla_attn,
"__call__",
return_value=torch.randn(2, 4, 128)):
with pytest.raises(AssertionError):
attn(positions, x)
attn(positions, x)
mock_mla_forward.assert_called_once()
attn = CustomDeepseekV2MLAAttention(config=base_config,
hidden_size=128,

56
tests/ut/models/test_qwen2_5_vl.py
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@@ -286,6 +286,22 @@ class TestAscendQwen2_5_VisionTransformer(PytestBase):
"vllm_ascend.models.qwen2_5_vl.parallel_state.get_tensor_model_parallel_world_size",
return_value=2,
)
mocker.patch(
"vllm_ascend.ops.linear.divide",
return_value=2,
)
mock_group = mocker.MagicMock()
mock_group.rank_in_group = 0
mock_group.world_size = 2
mocker.patch(
"vllm_ascend.ops.linear_op.get_tp_group",
return_value=mock_group,
)
mocker.patch(
"vllm.distributed.parallel_state.get_tp_group",
return_value=mock_group,
)
vision_transformer = AscendQwen2_5_VisionTransformer(
vision_config,
@@ -341,6 +357,46 @@ class TestAscendQwen2_5_VisionTransformer(PytestBase):
cos_new, _ = vision_transformer.cal_cos_sin(self.input_data)
assert cos_new.shape == (1, 32, 1, 2)
def test_pad_qkv_bias(self, mocker: MockerFixture):
attention = self.init_vision_transformer(mocker)
mocker.patch("torch.nn.Module.__setattr__")
mocker.patch("torch.nn.Module.__getattr__")
mocker.patch("torch.nn.Module.__delattr__")
res = attention.pad_qkv_bias(torch.rand((300)))
assert res.shape[0] == 384
def test_pad_qkv_weight(self, mocker: MockerFixture):
attention = self.init_vision_transformer(mocker)
mocker.patch("torch.nn.Module.__setattr__")
mocker.patch("torch.nn.Module.__getattr__")
mocker.patch("torch.nn.Module.__delattr__")
res = attention.pad_qkv_weight(torch.rand((300, 300)))
assert res.shape == (384, 300)
def test_pad_proj_weight(self, mocker: MockerFixture):
attention = self.init_vision_transformer(mocker)
mocker.patch("torch.nn.Module.__setattr__")
mocker.patch("torch.nn.Module.__getattr__")
mocker.patch("torch.nn.Module.__delattr__")
res = attention.pad_proj_weight(torch.rand((300, 300)))
assert res.shape == (300, 384)
def test_pad_qkv_weight_scale_offset(self, mocker: MockerFixture):
attention = self.init_vision_transformer(mocker)
mocker.patch("torch.nn.Module.__setattr__")
mocker.patch("torch.nn.Module.__getattr__")
mocker.patch("torch.nn.Module.__delattr__")
res = attention.pad_qkv_weight_scale_offset(torch.rand((300, 1)))
assert res.shape == (384, 1)
def test_pad_qkv_deq_scale_quant_bias(self, mocker: MockerFixture):
attention = self.init_vision_transformer(mocker)
mocker.patch("torch.nn.Module.__setattr__")
mocker.patch("torch.nn.Module.__getattr__")
mocker.patch("torch.nn.Module.__delattr__")
res = attention.pad_qkv_deq_scale_quant_bias(torch.rand((300)))
assert res.shape[0] == 384
def test_forward(self, mocker: MockerFixture):
vision_transformer = self.init_vision_transformer(mocker)
mocker.patch("torch.nn.Module.__setattr__")

30
tests/ut/models/test_qwen3_moe.py
View File

@@ -15,41 +15,11 @@
import math
import unittest
import pytest
import torch
from vllm.model_executor.models.qwen3_moe import Qwen3MoeForCausalLM
from vllm_ascend.models.qwen3_moe import CustomQwen3MoeForCausalLM
from vllm_ascend.torchair.models.qwen3_moe import CustomQwen3MoeAttention
class TestCustomQwen3MoeForCausalLM:
def test_class_inheritance(self):
assert issubclass(CustomQwen3MoeForCausalLM, Qwen3MoeForCausalLM)
@pytest.mark.parametrize("key, expected", [
("qkv_proj", ["q_proj", "k_proj", "v_proj"]),
("gate_up_proj", ["gate_proj", "up_proj"]),
("experts",
["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"]),
])
def test_packed_modules_mapping(self, key, expected):
assert CustomQwen3MoeForCausalLM.packed_modules_mapping[
key] == expected
def test_packed_modules_mapping_structure(self):
expected_mapping = {
"qkv_proj": ["q_proj", "k_proj", "v_proj"],
"gate_up_proj": ["gate_proj", "up_proj"],
"experts": [
"experts.0.gate_proj", "experts.0.up_proj",
"experts.0.down_proj"
]
}
assert CustomQwen3MoeForCausalLM.packed_modules_mapping == expected_mapping
class DummyRMSNorm:
def __init__(self, dim: int, eps: float = 1e-6):