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[CI] Upgrade vllm to newest commit (#3182)

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### What this PR does / why we need it?
Upgrade vLLM to newest commit

- Fix the aclgraph doesn't work problem, caused by
24fab45d96
- Fix PoolerOutput import error, caused by
755ed7b05b
- Fix the aclgraph weight load error to keep the same with torchair fix.
4492e3a554

### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
All test should pass


- vLLM version: v0.10.2
- vLLM main:
52d0cb8458

---------

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
This commit is contained in:
wangxiyuan
2025-09-26 06:18:15 +08:00
committed by GitHub
parent 0794f64a18
commit 2930e4a6bd
9 changed files with 49 additions and 53 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=52d0cb845866869d587fc013a7c59e60a86ebcf2
VLLM_COMMIT=17b4c6685ce62d5652654784d6771a3d38e4273e
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: 52d0cb845866869d587fc013a7c59e60a86ebcf2
vllm: 17b4c6685ce62d5652654784d6771a3d38e4273e
changes:
runs-on: ubuntu-latest
@@ -83,7 +83,7 @@ jobs:
VLLM_USE_MODELSCOPE: True
strategy:
matrix:
vllm_version: [52d0cb845866869d587fc013a7c59e60a86ebcf2, v0.10.2]
vllm_version: [17b4c6685ce62d5652654784d6771a3d38e4273e, v0.10.2]
steps:
- name: Install packages
run: |
@@ -138,7 +138,7 @@ jobs:
name: e2e-light
strategy:
matrix:
vllm_version: [52d0cb845866869d587fc013a7c59e60a86ebcf2, v0.10.2]
vllm_version: [17b4c6685ce62d5652654784d6771a3d38e4273e, 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: [52d0cb845866869d587fc013a7c59e60a86ebcf2, v0.10.2]
vllm_version: [17b4c6685ce62d5652654784d6771a3d38e4273e, v0.10.2]
needs: [changes]
if: ${{ needs.changes.outputs.e2e_tracker == 'true' }}
uses: ./.github/workflows/_e2e_test.yaml

26
vllm_ascend/patch/worker/patch_common/patch_weight_loader.py
View File

@@ -1,9 +1,6 @@
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
@@ -16,27 +13,15 @@ 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)
weight = Parameter(torch.empty(sum(output_partition_sizes),
input_size_per_partition,
dtype=params_dtype),
requires_grad=False)
except torch.cuda.OutOfMemoryError as e:
logger.error("Failed to create unquantized linear weights: %s", e)
if torch.cuda.is_available():
@@ -49,8 +34,7 @@ def create_weights(self, layer: torch.nn.Module, input_size_per_partition: int,
"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})
set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
layer.register_parameter("weight", weight)
set_weight_attrs(weight, extra_weight_attrs)

5
vllm_ascend/platform.py
View File

@@ -209,6 +209,11 @@ class NPUPlatform(Platform):
# set cudaprah sizes before extending `compilation_config.splitting_ops`
vllm_config._set_cudagraph_sizes()
# TODO: Full graph is fully supported later, and the default value will be set to full graph.
if not vllm_version_is("v0.10.2"):
if compilation_config.cudagraph_mode == CUDAGraphMode.FULL_AND_PIECEWISE:
compilation_config.cudagraph_mode = CUDAGraphMode.PIECEWISE
if compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
compilation_config.level = CompilationLevel.NO_COMPILATION
# TODO: Currently MLA does not support FULL_DECODE_ONLY, remove the second condition

5
vllm_ascend/quantization/quant_config.py
View File

@@ -33,7 +33,6 @@ from vllm.model_executor.layers.quantization.base_config import (
from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
from vllm.model_executor.layers.vocab_parallel_embedding import (
UnquantizedEmbeddingMethod, VocabParallelEmbedding)
from vllm.model_executor.parameter import PerTensorScaleParameter
from vllm.model_executor.utils import set_weight_attrs
from vllm_ascend.distributed.parallel_state import (get_mlp_tp_group,
@@ -251,7 +250,6 @@ class AscendLinearMethod(LinearMethodBase):
**extra_weight_attrs,
) -> None:
output_size_per_partition = sum(output_partition_sizes)
weight_loader = extra_weight_attrs.get("weight_loader")
weight_dict = self.quant_method.get_weight(input_size_per_partition,
output_size_per_partition,
@@ -264,8 +262,7 @@ class AscendLinearMethod(LinearMethodBase):
pertensor_dict = self.quant_method.get_pertensor_param(params_dtype)
for pertensor_name, pertensor_param in pertensor_dict.items():
param = PerTensorScaleParameter(data=pertensor_param,
weight_loader=weight_loader)
param = torch.nn.Parameter(pertensor_param, requires_grad=False)
# disable warning
param.ignore_warning = True
layer.register_parameter(pertensor_name, param)

14
vllm_ascend/torchair/torchair_worker.py
View File

@@ -28,20 +28,6 @@ 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."""

36
vllm_ascend/worker/model_runner_v1.py
View File

@@ -64,11 +64,12 @@ from vllm.multimodal.inputs import MultiModalKwargsItem, PlaceholderRange
from vllm.multimodal.utils import group_mm_kwargs_by_modality
from vllm.pooling_params import PoolingParams
from vllm.sampling_params import SamplingType
from vllm.sequence import IntermediateTensors, PoolerOutput
from vllm.sequence import IntermediateTensors
from vllm.tasks import GenerationTask, PoolingTask, SupportedTask
from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler,
LazyLoader, cdiv, get_dtype_size,
is_pin_memory_available)
from vllm.utils.jsontree import json_map_leaves
from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadataBuilder
from vllm.v1.attention.backends.utils import (
AttentionCGSupport, reorder_batch_to_split_decodes_and_prefills)
@@ -144,7 +145,9 @@ else:
if not vllm_version_is("0.10.2"):
from vllm.v1.kv_cache_interface import UniformTypeKVCacheSpecs
from vllm.v1.outputs import PoolerOutput
else:
from vllm.sequence import PoolerOutput
UniformTypeKVCacheSpecs = None
@@ -1806,18 +1809,30 @@ class NPUModelRunner(LoRAModelRunnerMixin):
device=hidden_states.device)
seq_lens_cpu = self.seq_lens_cpu[:self.input_batch.num_reqs]
# Pooling models D2H & synchronize occurs in pooler.py:build_output
raw_pooler_output = self.model.pooler(
hidden_states=hidden_states, pooling_metadata=pooling_metadata)
if vllm_version_is("0.10.2"):
# Pooling models D2H & synchronize occurs in pooler.py:build_output
raw_pooler_output = self.model.pooler(
hidden_states=hidden_states, pooling_metadata=pooling_metadata)
else:
model = cast(VllmModelForPooling, self.model)
raw_pooler_output = model.pooler(
hidden_states=hidden_states,
pooling_metadata=pooling_metadata,
)
raw_pooler_output = json_map_leaves(
lambda x: x.to("cpu", non_blocking=True),
raw_pooler_output,
)
torch.npu.synchronize()
pooler_output: list[Optional[torch.Tensor]] = []
for raw_output, seq_len, prompt_len in zip(
raw_pooler_output, seq_lens_cpu, pooling_metadata.prompt_lens):
if seq_len == prompt_len:
pooler_output.append(raw_output.data)
if vllm_version_is("0.10.2"):
output = raw_output.data if seq_len == prompt_len else None
else:
pooler_output.append(None)
output = raw_output if seq_len == prompt_len else None
pooler_output.append(output)
return ModelRunnerOutput(
req_ids=self.input_batch.req_ids,
@@ -2582,7 +2597,10 @@ class NPUModelRunner(LoRAModelRunnerMixin):
for task in self.get_supported_pooling_tasks():
# Run a full batch with each task to ensure none of them OOMs
output = self._dummy_pooler_run_task(hidden_states, task)
output_size[task] = output.get_data_nbytes()
if vllm_version_is("0.10.2"):
output_size[task] = output.get_data_nbytes()
else:
output_size[task] = sum(o.nbytes for o in output)
del output # Allow GC
max_task = max(output_size.items(), key=lambda x: x[1])[0]

6
vllm_ascend/worker/worker_v1.py
View File

@@ -116,6 +116,12 @@ class NPUWorker(WorkerBase):
# Buffers saved before sleep
self._sleep_saved_buffers: dict[str, torch.Tensor] = {}
# FixMe: this is a patch to fix the issue cause by https://github.com/vllm-project/vllm/commit/de94289a98d7ec52a5ef02719e01a1db8b505170
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 sleep(self, level: int = 1) -> None:
if not sleep_mode_enabled():
raise ValueError(