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[Misc] Clean up useless patch (#3320)

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### What this PR does / why we need it?
1. clean up v0.10.2 support in ut and e2e test
2. remove v0.11.0 period job, we're at v0.11.0 now.
3. remove uesless patch for deepseek v3.2. They have been done in vLLM
already.
### Does this PR introduce _any_ user-facing change?

### How was this patch tested?


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

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
This commit is contained in:
wangxiyuan
2025-10-09 14:07:26 +08:00
committed by GitHub
parent a43e2f61e1
commit 1c5b302f0d
10 changed files with 29 additions and 412 deletions
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51
.github/workflows/vllm_ascend_test_full_vllm_0.11.0.yaml vendored
View File

@@ -1,51 +0,0 @@
#
# 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.
# This file is a part of the vllm-ascend project.
#
name: 'ascend test / vllm 0.11.0'
on:
# Run 1-card and 2-cards e2e tests per 2h
schedule:
- cron: '0 */2 * * *'
pull_request:
branches:
- 'main'
paths:
# If we are changing the doctest we should do a PR test
- 'vllm_ascend_test_full_vllm_0.11.0.yaml'
workflow_dispatch:
# Bash shells do not use ~/.profile or ~/.bashrc so these shells need to be explicitly
# declared as "shell: bash -el {0}" on steps that need to be properly activated.
# It's used to activate ascend-toolkit environment variables.
defaults:
run:
shell: bash -el {0}
# only cancel in-progress runs of the same workflow
# and ignore the lint / 1 card / 4 cards test type
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: true
jobs:
e2e-test:
uses: ./.github/workflows/_e2e_test.yaml
with:
vllm: v0.11.0
runner: linux-aarch64-a2
image: swr.cn-southwest-2.myhuaweicloud.com/base_image/ascend-ci/cann:8.2.rc1-910b-ubuntu22.04-py3.11
type: full

9
tests/e2e/conftest.py
View File

@@ -32,14 +32,7 @@ from transformers import (AutoConfig, AutoModelForCausalLM, AutoTokenizer,
BatchEncoding, BatchFeature)
from transformers.models.auto.auto_factory import _BaseAutoModelClass
from vllm import LLM, SamplingParams
from vllm_ascend.utils import vllm_version_is
if vllm_version_is("0.10.2"):
from vllm.config import TaskOption, _get_and_verify_dtype
else:
from vllm.config.model import TaskOption, _get_and_verify_dtype
from vllm.config.model import TaskOption, _get_and_verify_dtype
from vllm.inputs import TextPrompt
from vllm.outputs import RequestOutput
from vllm.transformers_utils.utils import maybe_model_redirect

7
tests/e2e/model_utils.py
View File

@@ -19,12 +19,7 @@
from typing import Dict, List, Optional, Sequence, Tuple, Union
from vllm_ascend.utils import vllm_version_is
if vllm_version_is("0.10.2"):
from vllm.sequence import PromptLogprobs, SampleLogprobs
else:
from vllm.logprobs import PromptLogprobs, SampleLogprobs
from vllm.logprobs import PromptLogprobs, SampleLogprobs
TokensText = Tuple[List[int], str]

70
tests/e2e/singlecard/test_guided_decoding.py
View File

@@ -22,15 +22,8 @@ from typing import Any, Dict
import jsonschema
import pytest
import regex as re
from vllm_ascend.utils import vllm_version_is
if vllm_version_is("0.10.2"):
from vllm.sampling_params import GuidedDecodingParams, SamplingParams
else:
from vllm.sampling_params import SamplingParams, StructuredOutputsParams
from vllm.outputs import RequestOutput
from vllm.sampling_params import SamplingParams, StructuredOutputsParams
from tests.e2e.conftest import VllmRunner
@@ -91,27 +84,16 @@ def sample_json_schema():
def test_guided_json_completion(guided_decoding_backend: str,
sample_json_schema):
runner_kwargs: Dict[str, Any] = {}
if vllm_version_is("0.10.2"):
sampling_params = SamplingParams(
temperature=1.0,
max_tokens=500,
guided_decoding=GuidedDecodingParams(json=sample_json_schema))
runner_kwargs = {
"seed": 0,
"guided_decoding_backend": guided_decoding_backend,
}
else:
sampling_params = SamplingParams(
temperature=1.0,
max_tokens=500,
structured_outputs=StructuredOutputsParams(
json=sample_json_schema))
runner_kwargs = {
"seed": 0,
"structured_outputs_config": {
"backend": guided_decoding_backend
},
}
sampling_params = SamplingParams(
temperature=1.0,
max_tokens=500,
structured_outputs=StructuredOutputsParams(json=sample_json_schema))
runner_kwargs = {
"seed": 0,
"structured_outputs_config": {
"backend": guided_decoding_backend
},
}
with VllmRunner(MODEL_NAME, **runner_kwargs) as vllm_model:
prompts = [
f"Give an example JSON for an employee profile "
@@ -141,26 +123,16 @@ def test_guided_regex(guided_decoding_backend: str, sample_regex):
if guided_decoding_backend == "outlines":
pytest.skip("Outlines doesn't support regex-based guided decoding.")
runner_kwargs: Dict[str, Any] = {}
if vllm_version_is("0.10.2"):
sampling_params = SamplingParams(
temperature=0.8,
top_p=0.95,
guided_decoding=GuidedDecodingParams(regex=sample_regex))
runner_kwargs = {
"seed": 0,
"guided_decoding_backend": guided_decoding_backend,
}
else:
sampling_params = SamplingParams(
temperature=0.8,
top_p=0.95,
structured_outputs=StructuredOutputsParams(regex=sample_regex))
runner_kwargs = {
"seed": 0,
"structured_outputs_config": {
"backend": guided_decoding_backend
},
}
sampling_params = SamplingParams(
temperature=0.8,
top_p=0.95,
structured_outputs=StructuredOutputsParams(regex=sample_regex))
runner_kwargs = {
"seed": 0,
"structured_outputs_config": {
"backend": guided_decoding_backend
},
}
with VllmRunner(MODEL_NAME, **runner_kwargs) as vllm_model:
prompts = [

9
tests/ut/ops/test_fused_moe_prepare_and_finalize.py
View File

@@ -8,7 +8,6 @@ from vllm_ascend.ops.moe.fused_moe_prepare_and_finalize import (
FusedMoEPrepareAndFinalizeWithAll2All,
FusedMoEPrepareAndFinalizeWithAllGather, FusedMoEPrepareAndFinalizeWithMC2,
FusedMoEPrepareAndFinalizeWithNaiveMulticast)
from vllm_ascend.utils import vllm_version_is
class TestFusedMoEPrepareAndFinalize(unittest.TestCase):
@@ -231,12 +230,8 @@ class TestFusedMoEPrepareAndFinalize(unittest.TestCase):
mock_get_dp_group):
# Mock forward context with DP metadata
mock_context = MagicMock()
if vllm_version_is("0.10.2"):
mock_context.dp_metadata.cu_tokens_across_dp_cpu = torch.tensor(
[2, 5, 7])
else:
mock_context.dp_metadata.cu_tokens_across_sp.return_value = torch.tensor(
[2, 5, 7])
mock_context.dp_metadata.cu_tokens_across_sp.return_value = torch.tensor(
[2, 5, 7])
mock_get_forward_context.return_value = mock_context
# Setup DP group mock

8
tests/ut/ops/test_fused_ops.py
View File

@@ -28,7 +28,7 @@ from vllm_ascend.ops.fused_moe import (AscendFusedMoE,
AscendUnquantizedFusedMoEMethod)
from vllm_ascend.ops.moe.experts_selector import select_experts
from vllm_ascend.ops.moe.moe_mlp import cumsum_group_list, unified_apply_mlp
from vllm_ascend.utils import AscendSocVersion, adapt_patch, vllm_version_is
from vllm_ascend.utils import AscendSocVersion, adapt_patch
adapt_patch(True)
@@ -92,11 +92,7 @@ def mock_dist_env(mocker: MockerFixture):
return hidden_states
mock_moe_comm_method.finalize.side_effect = mock_finalize
if vllm_version_is("0.10.2"):
dp_metadata = MagicMock(cu_tokens_across_dp_cpu=[5, 10])
else:
dp_metadata = MagicMock(num_tokens_across_dp_cpu=[5, 5])
dp_metadata = MagicMock(num_tokens_across_dp_cpu=[5, 5])
mock_forward_context_obj = MagicMock(moe_comm_method=mock_moe_comm_method,
moe_comm_type=MoECommType.MC2,
max_tokens_across_dp=10,

7
tests/ut/torchair/ops/test_torchair_fused_moe.py
View File

@@ -27,7 +27,7 @@ from vllm_ascend.quantization.quant_config import AscendFusedMoEMethod
from vllm_ascend.torchair.ops.torchair_fused_moe import (
TorchairAscendFusedMoE, TorchairAscendUnquantizedFusedMoEMethod)
from vllm_ascend.utils import adapt_patch # noqa E402
from vllm_ascend.utils import AscendSocVersion, vllm_version_is
from vllm_ascend.utils import AscendSocVersion
adapt_patch(True)
@@ -54,10 +54,7 @@ def mock_dp_and_tp_group(mocker):
@pytest.fixture
def mock_dist_env(mocker: MockerFixture):
# init dist env patch
if vllm_version_is("0.10.2"):
dp_metadata = MagicMock(cu_tokens_across_dp_cpu=[5, 10])
else:
dp_metadata = MagicMock(num_tokens_across_dp_cpu=[5, 5])
dp_metadata = MagicMock(num_tokens_across_dp_cpu=[5, 5])
with patch('torch.distributed.get_rank', return_value=0), \
patch('torch.distributed.get_world_size', return_value=4), \

1
vllm_ascend/patch/platform/patch_common/__init__.py
View File

@@ -19,6 +19,5 @@ import vllm_ascend.patch.platform.patch_common.patch_config # noqa
import vllm_ascend.patch.platform.patch_common.patch_distributed # noqa
import vllm_ascend.patch.platform.patch_common.patch_mamba_config # noqa
import vllm_ascend.patch.platform.patch_common.patch_multimodal_merge # noqa
import vllm_ascend.patch.platform.patch_common.patch_transformers_utils # noqa
import vllm_ascend.patch.worker.patch_common.patch_attention_selector # noqa
import vllm_ascend.patch.worker.patch_common.patch_attentionspec # noqa

79
vllm_ascend/patch/platform/patch_common/patch_config.py
View File

@@ -1,87 +1,10 @@
import ast
import vllm.envs as envs
from transformers import PretrainedConfig
from vllm.config import ModelConfig
from vllm.config.speculative import SpeculativeConfig
from vllm.logger import logger
# mypy: ignore-errors
@property
def is_deepseek_mla(self: ModelConfig):
if not hasattr(self.hf_text_config, "model_type"):
return False
elif self.hf_text_config.model_type in \
('deepseek_v2', 'deepseek_v3', 'deepseek_mtp',
'kimi_k2', 'longcat_flash', 'deepseek_v32'):
return self.hf_text_config.kv_lora_rank is not None
elif self.hf_text_config.model_type == 'eagle':
# if the model is an EAGLE module, check for the
# underlying architecture
return self.hf_text_config.model.model_type in \
('deepseek_v2', 'deepseek_v3', 'deepseek_v32') \
and self.hf_text_config.kv_lora_rank is not None
return False
@staticmethod
def hf_config_override(hf_config: PretrainedConfig) -> PretrainedConfig:
if hf_config.model_type in ("deepseek_v3", "deepseek_v32"):
hf_config.model_type = "deepseek_mtp"
if hf_config.model_type == "deepseek_mtp":
n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
hf_config.update({
"n_predict": n_predict,
"architectures": ["DeepSeekMTPModel"]
})
if hf_config.architectures[0] == "MiMoForCausalLM":
hf_config.model_type = "mimo_mtp"
n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
hf_config.update({
"num_hidden_layers": 0,
"n_predict": n_predict,
"architectures": ["MiMoMTPModel"]
})
if hf_config.architectures[0] == "Glm4MoeForCausalLM":
hf_config.model_type = "glm4_moe_mtp"
n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
hf_config.update({
"num_hidden_layers": 0,
"n_predict": n_predict,
"architectures": ["Glm4MoeMTPModel"]
})
if hf_config.model_type == "ernie4_5_moe":
hf_config.model_type = "ernie_mtp"
if hf_config.model_type == "ernie_mtp":
n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
hf_config.update({
"n_predict": n_predict,
"architectures": ["ErnieMTPModel"]
})
if hf_config.model_type == "qwen3_next":
hf_config.model_type = "qwen3_next_mtp"
if hf_config.model_type == "qwen3_next_mtp":
n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
hf_config.update({
"n_predict": n_predict,
"architectures": ["Qwen3NextMTP"]
})
if hf_config.model_type == "longcat_flash":
hf_config.model_type = "longcat_flash_mtp"
n_predict = getattr(hf_config, "num_nextn_predict_layers", 1)
hf_config.update({
"n_predict": n_predict,
"architectures": ["LongCatFlashMTPModel"]
})
return hf_config
def __post_init__(self):
# Note: "method" is a new parameter that helps to extend the
@@ -308,6 +231,4 @@ def __post_init__(self):
self.draft_tensor_parallel_size))
ModelConfig.is_deepseek_mla = is_deepseek_mla
SpeculativeConfig.__post_init__ = __post_init__
SpeculativeConfig.hf_config_override = hf_config_override

200
vllm_ascend/patch/platform/patch_common/patch_transformers_utils.py
View File

@@ -1,200 +0,0 @@
import vllm.transformers_utils.configs
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
from vllm.transformers_utils import config
logger = logging.get_logger(__name__)
class DeepseekV3Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`DeepseekV3Model`]. It is used to instantiate an DeepSeek
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the DeepSeek-V3.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 129280):
Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`DeepseekV3Model`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 11008):
Dimension of the MLP representations.
moe_intermediate_size (`int`, *optional*, defaults to 1407):
Dimension of the MoE representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer decoder.
num_nextn_predict_layers (`int`, *optional*, defaults to 1):
Number of nextn predict layers in the DeepSeekV3 Model.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer decoder.
n_shared_experts (`int`, *optional*, defaults to None):
Number of shared experts, None means dense model.
n_routed_experts (`int`, *optional*, defaults to None):
Number of routed experts, None means dense model.
routed_scaling_factor (`float`, *optional*, defaults to 1.0):
Scaling factor or routed experts.
topk_method (`str`, *optional*, defaults to `gready`):
Topk method used in routed gate.
n_group (`int`, *optional*, defaults to None):
Number of groups for routed experts.
topk_group (`int`, *optional*, defaults to None):
Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
num_experts_per_tok (`int`, *optional*, defaults to None):
Number of selected experts, None means dense model.
moe_layer_freq (`int`, *optional*, defaults to 1):
The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers.
first_k_dense_replace (`int`, *optional*, defaults to 0):
Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
\--k dense layers--/
norm_topk_prob (`bool`, *optional*, defaults to False):
Whether to normalize the weights of the routed experts.
scoring_func (`str`, *optional*, defaults to 'softmax'):
Method of computing expert weights.
aux_loss_alpha (`float`, *optional*, defaults to 0.001):
Auxiliary loss weight coefficient.
seq_aux = (`bool`, *optional*, defaults to True):
Whether to compute the auxiliary loss for each individual sample.
num_key_value_heads (`int`, *optional*):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 1):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 2):
End of stream token id.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
`max_position_embeddings` to the expected new maximum.
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
```python
>>> from transformers import DeepseekV3Model, DeepseekV3Config
>>> # Initializing a Deepseek-V3 style configuration
>>> configuration = DeepseekV3Config()
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "deepseek_v3"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=129280,
hidden_size=7168,
intermediate_size=18432,
moe_intermediate_size=2048,
num_hidden_layers=61,
num_nextn_predict_layers=1,
num_attention_heads=128,
num_key_value_heads=128,
n_shared_experts=1,
n_routed_experts=256,
ep_size=1,
routed_scaling_factor=2.5,
kv_lora_rank=512,
q_lora_rank=1536,
qk_rope_head_dim=64,
v_head_dim=128,
qk_nope_head_dim=128,
topk_method='noaux_tc',
n_group=8,
topk_group=4,
num_experts_per_tok=8,
moe_layer_freq=1,
first_k_dense_replace=3,
norm_topk_prob=True,
scoring_func='sigmoid',
hidden_act="silu",
max_position_embeddings=4096,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=None,
bos_token_id=0,
eos_token_id=1,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.moe_intermediate_size = moe_intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_nextn_predict_layers = num_nextn_predict_layers
self.num_attention_heads = num_attention_heads
self.n_shared_experts = n_shared_experts
self.n_routed_experts = n_routed_experts
self.ep_size = ep_size
self.routed_scaling_factor = routed_scaling_factor
self.kv_lora_rank = kv_lora_rank
self.q_lora_rank = q_lora_rank
self.qk_rope_head_dim = qk_rope_head_dim
self.v_head_dim = v_head_dim
self.qk_nope_head_dim = qk_nope_head_dim
self.topk_method = topk_method
self.n_group = n_group
self.topk_group = topk_group
self.num_experts_per_tok = num_experts_per_tok
self.moe_layer_freq = moe_layer_freq
self.first_k_dense_replace = first_k_dense_replace
self.norm_topk_prob = norm_topk_prob
self.scoring_func = scoring_func
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
vllm.transformers_utils.configs.__all__.append("DeepseekV3Config")
vllm.transformers_utils.configs.DeepseekV3Config = DeepseekV3Config
config._CONFIG_REGISTRY["deepseek_v32"] = "DeepseekV3Config"