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[CI] Upgrade vLLM version (#3139)

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Upgrade vLLM version to the newest commit.
- Fix the break change introduced by
969b4da3a6
- Add a patch to quick fix torhcair
de94289a98
- fix the ut error introduced by
de94289a98

Close: https://github.com/vllm-project/vllm-ascend/issues/3138


- vLLM version: v0.10.2
- vLLM main:
f225ea7dd9

---------

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Signed-off-by: MengqingCao <cmq0113@163.com>
Co-authored-by: MengqingCao <cmq0113@163.com>
This commit is contained in:
wangxiyuan
2025-09-25 07:36:51 +08:00
committed by GitHub
parent 464270e4ca
commit a055183821
9 changed files with 105 additions and 15 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
.github/workflows/format_pr_body.yaml vendored
View File

@@ -36,7 +36,7 @@ jobs:
- name: Get vLLM version
run: |
VLLM_COMMIT=f225ea7dd98e9f29752e5c032cd4a8ee1d712f16
VLLM_COMMIT=b1068903fdca26cf6b4a1a51a32c3365ce3ac636
echo "VLLM_COMMIT=https://github.com/vllm-project/vllm/commit/$VLLM_COMMIT" >> $GITHUB_ENV
- name: Checkout repository

6
.github/workflows/vllm_ascend_test.yaml vendored
View File

@@ -42,7 +42,7 @@ jobs:
lint:
uses: ./.github/workflows/pre-commit.yml
with:
vllm: f225ea7dd98e9f29752e5c032cd4a8ee1d712f16
vllm: b1068903fdca26cf6b4a1a51a32c3365ce3ac636
changes:
runs-on: ubuntu-latest
@@ -83,7 +83,7 @@ jobs:
VLLM_USE_MODELSCOPE: True
strategy:
matrix:
vllm_version: [f225ea7dd98e9f29752e5c032cd4a8ee1d712f16, v0.10.2]
vllm_version: [b1068903fdca26cf6b4a1a51a32c3365ce3ac636, v0.10.2]
steps:
- name: Install packages
run: |
@@ -138,7 +138,7 @@ jobs:
name: e2e-light
strategy:
matrix:
vllm_version: [f225ea7dd98e9f29752e5c032cd4a8ee1d712f16, v0.10.2]
vllm_version: [b1068903fdca26cf6b4a1a51a32c3365ce3ac636, v0.10.2]
# Note (yikun): If CI resource are limited we can split job into two chain jobs
needs: [lint, changes]
# only trigger e2e test after lint passed and the change is e2e related with pull request.

2
.github/workflows/vllm_ascend_test_full.yaml vendored
View File

@@ -68,7 +68,7 @@ jobs:
name: e2e-full
strategy:
matrix:
vllm_version: [f225ea7dd98e9f29752e5c032cd4a8ee1d712f16, v0.10.2]
vllm_version: [b1068903fdca26cf6b4a1a51a32c3365ce3ac636, v0.10.2]
needs: [changes]
if: ${{ needs.changes.outputs.e2e_tracker == 'true' }}
uses: ./.github/workflows/_e2e_test.yaml

4
tests/ut/models/test_qwen2_5_vl.py
View File

@@ -298,6 +298,10 @@ class TestAscendQwen2_5_VisionTransformer(PytestBase):
"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,

4
tests/ut/ops/test_linear.py
View File

@@ -33,6 +33,10 @@ class BaseLinearTest(unittest.TestCase):
return_value=self.mock_group),
patch("vllm_ascend.ops.linear_op.get_tp_group",
return_value=self.mock_group),
patch(
"vllm.distributed.parallel_state.get_tp_group",
return_value=self.mock_group,
),
patch("vllm_ascend.utils.mlp_tp_enable", return_value=True),
patch("vllm_ascend.utils.oproj_tp_enable", return_value=True)
]

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

@@ -22,6 +22,7 @@ if HAS_TRITON:
import vllm_ascend.patch.worker.patch_common.patch_distributed # noqa
import vllm_ascend.patch.worker.patch_common.patch_logits # noqa
import vllm_ascend.patch.worker.patch_common.patch_weight_loader # noqa
# TODO: revert me when triton import is fixed
# import vllm_ascend.patch.worker.patch_common.patch_minicpm # noqa

60
vllm_ascend/patch/worker/patch_common/patch_weight_loader.py Normal file
View File

@@ -0,0 +1,60 @@
import torch
from torch.nn.parameter import Parameter
from vllm.logger import init_logger
# yapf: disable
from vllm.model_executor.parameter import ModelWeightParameter
# yapf: enable
from vllm.model_executor.utils import set_weight_attrs
from vllm.utils import GiB_bytes
from vllm_ascend.utils import vllm_version_is
logger = init_logger(__name__)
def create_weights(self, layer: torch.nn.Module, input_size_per_partition: int,
output_partition_sizes: list[int], input_size: int,
output_size: int, params_dtype: torch.dtype,
**extra_weight_attrs):
from vllm_ascend.ascend_config import get_ascend_config
ascend_config = get_ascend_config()
# This method creates unquantized linear weights.
# The weights are not quantized, and they are not sharded.
# The amount of memory allocated for the weights is
# sum(output_partition_sizes) * input_size_per_partition.
try:
if ascend_config.torchair_graph_config.enabled:
weight = Parameter(torch.empty(sum(output_partition_sizes),
input_size_per_partition,
dtype=params_dtype),
requires_grad=False)
else:
weight_loader = extra_weight_attrs.pop("weight_loader")
weight = ModelWeightParameter(data=torch.empty(
sum(output_partition_sizes),
input_size_per_partition,
dtype=params_dtype),
input_dim=1,
output_dim=0,
weight_loader=weight_loader)
except torch.cuda.OutOfMemoryError as e:
logger.error("Failed to create unquantized linear weights: %s", e)
if torch.cuda.is_available():
logger.debug("CUDA device: %s", torch.cuda.current_device())
logger.debug("Allocated: %.2f GiB",
torch.cuda.memory_allocated() / GiB_bytes)
logger.debug("Reserved: %.2f GiB",
torch.cuda.memory_reserved() / GiB_bytes)
raise RuntimeError(
"Failed to create unquantized linear weights. "
"This may be caused by insufficient memory to allocate "
"the weight.") from e
if ascend_config.torchair_graph_config.enabled:
set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
layer.register_parameter("weight", weight)
set_weight_attrs(weight, extra_weight_attrs)
if not vllm_version_is("0.10.2"):
from vllm.model_executor.layers.linear import UnquantizedLinearMethod
UnquantizedLinearMethod.create_weights = create_weights

14
vllm_ascend/torchair/torchair_worker.py
View File

@@ -28,6 +28,20 @@ from vllm_ascend.worker.worker_v1 import NPUWorker
class NPUTorchairWorker(NPUWorker):
"""Torchair worker bases on NPUWorker. Only torchair specified code should be added in this class."""
def __init__(self,
vllm_config,
local_rank,
rank,
distributed_init_method,
is_driver_worker=False,
**kwargs):
super().__init__(vllm_config, local_rank, rank,
distributed_init_method, is_driver_worker, **kwargs)
from vllm.model_executor.layers.linear import \
WEIGHT_LOADER_V2_SUPPORTED
if "UnquantizedLinearMethod" in WEIGHT_LOADER_V2_SUPPORTED:
WEIGHT_LOADER_V2_SUPPORTED.remove("UnquantizedLinearMethod")
def determine_available_memory(self) -> int:
"""Override determine_available_memory to use cached torchair kv_cache_bytes."""

27
vllm_ascend/worker/model_runner_v1.py
View File

@@ -304,17 +304,24 @@ class NPUModelRunner(LoRAModelRunnerMixin):
(self.max_num_tokens, self.model_config.get_hidden_size()),
dtype=self.dtype,
device=self.device)
# Set up Attention
self.attn_backend = get_attn_backend(
0,
self.dtype,
None,
self.block_size,
self.model_config.is_attention_free,
use_mla=self.model_config.use_mla,
)
if vllm_version_is("0.10.2"):
self.attn_backend = get_attn_backend(
0,
self.dtype,
None,
self.block_size,
self.model_config.is_attention_free,
use_mla=self.model_config.use_mla,
)
else:
self.attn_backend = get_attn_backend(
0,
self.dtype,
None,
self.block_size,
use_mla=self.model_config.use_mla,
)
if torch.version.cann.startswith("8.3"):
self.attn_mask_builder = AttentionMaskBuilder(
self.scheduler_config.max_num_batched_tokens, self.dtype,