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[Main2Main] Upgrade vllm commit to 0105 (#5595)

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### What this PR does / why we need it?

Upgrade vllm commit to 0105 (8be6432bdaf6275664d857b1e5e9bf8ed1ce299e)

1. Remove `maybe_padded_num_tokens` arg in `model_runner_v1.py` since
https://github.com/vllm-project/vllm/pull/31517 deleted unused arg

2. Remove dense `Qwen/Qwen3-0.6B` in
`tests/e2e/multicard/test_aclgraph_capture_replay.py` and
`tests/e2e/multicard/test_data_parallel.py` due to
https://github.com/vllm-project/vllm/pull/30739
where offline data parallel mode will not be supported/useful for dense
models

3. Adapt `vllm_ascend/worker/worker.py` due to
https://github.com/vllm-project/vllm/pull/31584

4. Adapt `self.block_size` calling due to
https://github.com/vllm-project/vllm/pull/31540

5. Modify `test_mla_v1.py` due to
https://github.com/vllm-project/vllm/pull/28454 , which refactorred
`get_head_size()`

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.13.0
- vLLM main:
7157596103

Signed-off-by: wjunLu <wjunlu217@gmail.com>
This commit is contained in:
wjunLu
2026-01-06 08:44:29 +08:00
committed by GitHub
parent c5e2f48510
commit 3cf059a72b
15 changed files with 61 additions and 38 deletions
Download Patch File Download Diff File Expand all files Collapse all files

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

@@ -34,7 +34,7 @@ jobs:
steps:
- name: Get vLLM version
run: |
VLLM_COMMIT=7157596103666ee7ccb7008acee8bff8a8ff1731
VLLM_COMMIT=8be6432bdaf6275664d857b1e5e9bf8ed1ce299e
echo "VLLM_COMMIT=https://github.com/vllm-project/vllm/commit/$VLLM_COMMIT" >> $GITHUB_ENV
- name: Checkout repository

2
.github/workflows/pr_test_full.yaml vendored
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@@ -74,7 +74,7 @@ jobs:
name: e2e-full
strategy:
matrix:
vllm_version: [7157596103666ee7ccb7008acee8bff8a8ff1731, v0.13.0]
vllm_version: [8be6432bdaf6275664d857b1e5e9bf8ed1ce299e, v0.13.0]
needs: [changes]
if: ${{ needs.changes.outputs.e2e_tracker == 'true' }}
uses: ./.github/workflows/_e2e_test.yaml

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

@@ -42,7 +42,7 @@ jobs:
lint:
uses: ./.github/workflows/_pre_commit.yml
with:
vllm: 7157596103666ee7ccb7008acee8bff8a8ff1731
vllm: 8be6432bdaf6275664d857b1e5e9bf8ed1ce299e
changes:
runs-on: linux-aarch64-a2-0
outputs:
@@ -90,7 +90,7 @@ jobs:
SOC_VERSION: ascend910b1
strategy:
matrix:
vllm_version: [7157596103666ee7ccb7008acee8bff8a8ff1731, v0.13.0]
vllm_version: [8be6432bdaf6275664d857b1e5e9bf8ed1ce299e, v0.13.0]
steps:
- name: Free up disk space
@@ -163,7 +163,7 @@ jobs:
name: e2e-light
strategy:
matrix:
vllm_version: [7157596103666ee7ccb7008acee8bff8a8ff1731, v0.13.0]
vllm_version: [8be6432bdaf6275664d857b1e5e9bf8ed1ce299e, v0.13.0]
# 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
docs/source/community/versioning_policy.md
View File

@@ -51,7 +51,7 @@ If you're using v0.7.3, don't forget to install [mindie-turbo](https://pypi.org/
For main branch of vLLM Ascend, we usually make it compatible with the latest vLLM release and a newer commit hash of vLLM. Please note that this table is usually updated. Please check it regularly.
| vLLM Ascend | vLLM | Python | Stable CANN | PyTorch/torch_npu |
|-------------|--------------|------------------|-------------|--------------------|
| main | 7157596103666ee7ccb7008acee8bff8a8ff1731, v0.13.0 tag | >= 3.10, < 3.12 | 8.3.RC2 | 2.8.0 / 2.8.0 |
| main | 8be6432bdaf6275664d857b1e5e9bf8ed1ce299e, v0.13.0 tag | >= 3.10, < 3.12 | 8.3.RC2 | 2.8.0 / 2.8.0 |
## Release cadence

3
tests/e2e/multicard/test_aclgraph_capture_replay.py
View File

@@ -28,7 +28,8 @@ from vllm.utils.network_utils import get_open_port
from vllm_ascend.utils import AscendDeviceType, get_ascend_device_type
MODELS = [
"Qwen/Qwen3-0.6B",
# Offline data parallel mode will be not supported/useful for dense models
# "Qwen/Qwen3-0.6B",
"vllm-ascend/DeepSeek-V2-Lite-W8A8",
]

4
tests/e2e/multicard/test_data_parallel.py
View File

@@ -27,9 +27,7 @@ from unittest.mock import patch
import pytest
MODELS = [
"Qwen/Qwen3-0.6B", "Qwen/Qwen3-30B-A3B", "vllm-ascend/Qwen3-30B-A3B-W8A8"
]
MODELS = ["Qwen/Qwen3-30B-A3B", "vllm-ascend/Qwen3-30B-A3B-W8A8"]
@pytest.mark.parametrize("model", MODELS)

2
tests/e2e/multicard/test_data_parallel_tp2.py
View File

@@ -9,7 +9,7 @@ from unittest.mock import patch
import pytest
MODELS = ["Qwen/Qwen3-0.6B"]
MODELS = ["Qwen/Qwen3-30B-A3B"]
@pytest.mark.parametrize("model", MODELS)

6
tests/ut/attention/test_mla_v1.py
View File

@@ -17,6 +17,7 @@ from vllm_ascend.attention.mla_v1 import (AscendMLABackend,
AscendMLAPrefillMetadata,
ChunkedContextMetadata)
from vllm_ascend.attention.utils import AscendCommonAttentionMetadata
from vllm_ascend.utils import vllm_version_is
class TestAscendMLABackend(TestBase):
@@ -392,7 +393,10 @@ class TestAscendMLAMetadataBuilderBuild(TestBase):
self.mock_vllm_config.model_config = model_config
self.kv_cache_spec = MagicMock()
self.kv_cache_spec.num_layers = 32
self.kv_cache_spec.head_size = 128
if vllm_version_is('0.13.0'):
self.kv_cache_spec.head_size = 128
else:
self.kv_cache_spec.head_size = 64
self.kv_cache_spec.num_heads = 32
@patch("vllm_ascend.attention.mla_v1.get_cos_and_sin_mla")

14
tests/ut/conftest.py
View File

@@ -18,13 +18,6 @@
import sys
from unittest.mock import MagicMock
from vllm_ascend.utils import adapt_patch # noqa E402
from vllm_ascend.utils import register_ascend_customop
# triton and torch_npu is not available in the environment, so we need to mock them
sys.modules['torch_npu'].npu.current_device = MagicMock(return_value=0)
sys.modules['torch_npu._inductor'] = MagicMock()
triton_runtime = MagicMock()
triton_runtime.driver.active.utils.get_device_properties.return_value = {
'num_aic': 8,
@@ -32,6 +25,13 @@ triton_runtime.driver.active.utils.get_device_properties.return_value = {
}
sys.modules['triton.runtime'] = triton_runtime
from vllm_ascend.utils import adapt_patch # noqa E402
from vllm_ascend.utils import register_ascend_customop # noqa E402
# triton and torch_npu is not available in the environment, so we need to mock them
sys.modules['torch_npu'].npu.current_device = MagicMock(return_value=0)
sys.modules['torch_npu._inductor'] = MagicMock()
adapt_patch()
adapt_patch(True)

1
tests/ut/spec_decode/test_eagle_proposer.py
View File

@@ -58,7 +58,6 @@ class TestEagleProposerInitialization(TestBase):
device=self.device,
runner=self.runner)
self.assertEqual(proposer.block_size, 16)
self.assertEqual(proposer.hidden_size, 4096)
self.assertTrue(proposer.use_cuda_graph)

1
tests/ut/spec_decode/test_mtp_proposer.py
View File

@@ -86,7 +86,6 @@ class TestMtpProposer:
assert proposer.dtype == torch.float16
assert proposer.num_speculative_tokens == 2
assert proposer.hidden_size == 4096
assert proposer.block_size == 16
# Test with mrope enabled
assert hasattr(proposer, "positions")

1
vllm_ascend/attention/attention_v1.py
View File

@@ -197,6 +197,7 @@ class AscendAttentionMetadataBuilder(AttentionMetadataBuilder[AscendMetadata]):
vllm_config: VllmConfig,
device: torch.device,
):
super().__init__(kv_cache_spec, layer_names, vllm_config, device)
self.vllm_config = vllm_config
self.model_config = vllm_config.model_config
self.compilation_config = vllm_config.compilation_config

14
vllm_ascend/spec_decode/eagle_proposer.py
View File

@@ -136,6 +136,7 @@ class EagleProposer(VllmEagleProposer):
draft_attn_layer_names = draft_attn_layer_names - draft_indexer_layer_names
assert len(draft_attn_layer_names) == 1
self.attn_layer_name = list(draft_attn_layer_names)
self.attn_layer_names = self.attn_layer_name
# share embed_tokens with the target model if needed
if get_pp_group().world_size == 1:
@@ -442,14 +443,19 @@ class EagleProposer(VllmEagleProposer):
# For the requests that exceed the max model length, we set the
# TODO: sequence length to 1 to minimize their overheads in attention.
if self.attn_metadata_builder is None:
attn_metadata_builder = self._get_attention_metadata_builder()
else:
attn_metadata_builder = self.attn_metadata_builder
block_size = attn_metadata_builder.kv_cache_spec.block_size
# Compute the slot mapping.
block_numbers = (clamped_positions // self.block_size)
block_numbers = (clamped_positions // block_size)
block_ids = attn_metadata.block_tables.gather(
dim=1, index=block_numbers.view(-1, 1))
block_ids = block_ids.view(-1)
slot_mapping_tmp = (
block_ids * self.vllm_config.cache_config.block_size +
clamped_positions % self.block_size)
slot_mapping_tmp = (block_ids * block_size +
clamped_positions % block_size)
# Mask out the slot mappings that exceed the max model length.
# Otherwise, the KV cache will be inadvertently updated with the

36
vllm_ascend/worker/model_runner_v1.py
View File

@@ -107,7 +107,7 @@ from vllm_ascend.spec_decode.mtp_proposer import MtpProposer
from vllm_ascend.utils import (AscendDeviceType, ProfileExecuteDuration,
enable_sp, get_ascend_device_type, is_moe_model,
lmhead_tp_enable, maybe_trans_nz,
set_weight_prefetch_method)
set_weight_prefetch_method, vllm_version_is)
from vllm_ascend.worker.npu_input_batch import NPUInputBatch
from vllm_ascend.worker.pcp_utils import PCPManager
@@ -1097,12 +1097,20 @@ class NPUModelRunner(GPUModelRunner):
intermediate_tensors,
inputs_embeds):
assert self.model is not None
hidden_states = self.model(
input_ids=input_ids,
positions=positions,
intermediate_tensors=intermediate_tensors,
inputs_embeds=inputs_embeds,
**self._init_model_kwargs(maybe_padded_num_tokens))
if vllm_version_is('0.13.0'):
hidden_states = self.model(
input_ids=input_ids,
positions=positions,
intermediate_tensors=intermediate_tensors,
inputs_embeds=inputs_embeds,
**self._init_model_kwargs(maybe_padded_num_tokens))
else:
hidden_states = self.model(
input_ids=input_ids,
positions=positions,
intermediate_tensors=intermediate_tensors,
inputs_embeds=inputs_embeds,
**self._init_model_kwargs())
forward_context = get_forward_context()
if forward_context.cudagraph_runtime_mode == CUDAGraphMode.FULL \
@@ -1548,10 +1556,16 @@ class NPUModelRunner(GPUModelRunner):
logits = None
else:
if self.input_batch.pooling_params:
pool_output = self._pool(
hidden_states,
scheduler_output.total_num_scheduled_tokens,
num_scheduled_tokens_np)
if vllm_version_is('0.13.0'):
pool_output = self._pool(
hidden_states,
scheduler_output.total_num_scheduled_tokens,
num_scheduled_tokens_np)
else:
pool_output = self._pool(
hidden_states,
scheduler_output.total_num_scheduled_tokens,
num_scheduled_tokens_np, kv_connector_output)
if self.debugger is not None:
self.debugger.stop()
self.debugger.step()

5
vllm_ascend/worker/worker.py
View File

@@ -299,7 +299,7 @@ class NPUWorker(WorkerBase):
def execute_model(
self,
scheduler_output: "SchedulerOutput",
) -> ModelRunnerOutput | None:
) -> ModelRunnerOutput | AsyncModelRunnerOutput | None:
# enable msMonitor to monitor the performance of vllm-ascend
if envs_ascend.MSMONITOR_USE_DAEMON:
dp.step()
@@ -318,7 +318,8 @@ class NPUWorker(WorkerBase):
output = self.model_runner.execute_model(scheduler_output,
intermediate_tensors)
if isinstance(output, (ModelRunnerOutput, NoneType)):
if isinstance(output,
(ModelRunnerOutput, AsyncModelRunnerOutput, NoneType)):
return output
assert isinstance(output, IntermediateTensors)