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[NPU] upgrade to v0.30.0 (#8027)

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Signed-off-by: Weiming Liao <liaowm5@gmail.com>
This commit is contained in:
Weiming Liao authored and GitHub committed 2026-09-23 14:59:29 +00:00
1 parent d319d276f9
commit 81bc7116c1
6 files changed
+22 -21
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docker/Dockerfile.npu
+1 -1
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@@ -1,5 +1,5 @@
ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
ARG VLLM_ASCEND_VERSION=v0.29.0
ARG VLLM_ASCEND_VERSION=v0.30.0
ARG VLLM_ASCEND_DEVICE_SUFFIX=
FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_VERSION}${VLLM_ASCEND_DEVICE_SUFFIX}
docker/Dockerfile.npu.ci
+1 -1
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@@ -1,5 +1,5 @@
ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
ARG VLLM_ASCEND_VERSION=v0.29.0
ARG VLLM_ASCEND_VERSION=v0.30.0
ARG VLLM_ASCEND_DEVICE_SUFFIX=
FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_VERSION}${VLLM_ASCEND_DEVICE_SUFFIX}
docs/getting_started/installation/npu/npu.inc.md
+11 -11
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@@ -10,25 +10,25 @@ The recommended way to use vLLM-Omni on NPU is through the vLLM-Ascend pre-built
=== "A2"
```bash
export IMAGE=quay.io/atlas-ci/vllm-ascend:v0.29.0
export IMAGE=quay.io/atlas-ci/vllm-ascend:v0.30.0
```
=== "A3"
```bash
export IMAGE=quay.io/atlas-ci/vllm-ascend:v0.29.0-a3
export IMAGE=quay.io/atlas-ci/vllm-ascend:v0.30.0-a3
```
=== "A5"
```bash
export IMAGE=quay.io/atlas-ci/vllm-ascend:v0.29.0-a5
export IMAGE=quay.io/atlas-ci/vllm-ascend:v0.30.0-a5
```
=== "310P (Experimental)"
```bash
export IMAGE=quay.io/atlas-ci/vllm-ascend:v0.29.0-310p
export IMAGE=quay.io/atlas-ci/vllm-ascend:v0.30.0-310p
```
```bash
@@ -53,7 +53,7 @@ docker run --rm \
# Inside the container, install vLLM-Omni from source
cd /vllm-workspace
git clone -b v0.29.0rc1 https://github.com/vllm-project/vllm-omni.git
git clone -b v0.30.0rc1 https://github.com/vllm-project/vllm-omni.git
cd vllm-omni
pip install -v -e . --no-build-isolation
# or VLLM_OMNI_TARGET_DEVICE=npu pip install -v -e .
@@ -71,10 +71,10 @@ We are keeping [issue #886](https://github.com/vllm-project/vllm-omni/issues/886
# --8<-- [start:installation-main]
You can also install vLLM-Omni from the latest main branch if you want to use the latest features or bug fixes. Use the aligned vLLM-Ascend v0.29.0 image described above, and check [issue #886](https://github.com/vllm-project/vllm-omni/issues/886) for the status of the latest vLLM-Omni commit on NPU.
You can also install vLLM-Omni from the latest main branch if you want to use the latest features or bug fixes. Use the aligned vLLM-Ascend v0.30.0 image described above, and check [issue #886](https://github.com/vllm-project/vllm-omni/issues/886) for the status of the latest vLLM-Omni commit on NPU.
```bash
# Inside the vLLM-Ascend v0.29.0 container, install vLLM-Omni from main
# Inside the vLLM-Ascend v0.30.0 container, install vLLM-Omni from main
cd /vllm-workspace
git clone https://github.com/vllm-project/vllm-omni.git
cd vllm-omni
@@ -103,7 +103,7 @@ Here's an example deployment command that has been verified on 4 x NPUs:
=== "A2"
```bash
export IMAGE=quay.io/ascend/vllm-omni:v0.29.0
export IMAGE=quay.io/ascend/vllm-omni:v0.30.0
docker run --rm \
--name vllm-omni-a2 \
--shm-size=64g \
@@ -127,7 +127,7 @@ Here's an example deployment command that has been verified on 4 x NPUs:
=== "A3"
```bash
export IMAGE=quay.io/ascend/vllm-omni:v0.29.0-a3
export IMAGE=quay.io/ascend/vllm-omni:v0.30.0-a3
docker run --rm \
--name vllm-omni-a3 \
--shm-size=64g \
@@ -151,7 +151,7 @@ Here's an example deployment command that has been verified on 4 x NPUs:
=== "A5"
```bash
export IMAGE=quay.io/ascend/vllm-omni:v0.29.0-a5
export IMAGE=quay.io/ascend/vllm-omni:v0.30.0-a5
docker run --rm \
--name vllm-omni-a5 \
--shm-size=64g \
@@ -175,7 +175,7 @@ Here's an example deployment command that has been verified on 4 x NPUs:
=== "310P (Experimental)"
```bash
export IMAGE=quay.io/ascend/vllm-omni:v0.29.0-310p
export IMAGE=quay.io/ascend/vllm-omni:v0.30.0-310p
docker run --rm \
--name vllm-omni-310p \
--shm-size=64g \
vllm_omni/platforms/npu/worker/npu_ar_model_runner.py
+1 -1
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@@ -544,7 +544,7 @@ class NPUARModelRunner(OmniNPUModelRunner, OmniConnectorModelRunnerMixin, Duplex
self.input_batch,
self.requests,
self.compilation_config.static_forward_context,
self.model.get_mamba_state_copy_func(),
self._get_mamba_state_copy_funcs(),
preprocess_bufs,
)
# preprocess_mamba resets num_accepted_tokens_cpu to 1
vllm_omni/platforms/npu/worker/npu_generation_model_runner.py
+1 -3
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@@ -269,7 +269,7 @@ class NPUGenerationModelRunner(OmniNPUModelRunner, OmniConnectorModelRunnerMixin
self.input_batch,
self.requests,
self.compilation_config.static_forward_context,
self.model.get_mamba_state_copy_func(),
self._get_mamba_state_copy_funcs(),
preprocess_bufs,
)
# preprocess_mamba resets num_accepted_tokens_cpu to 1
@@ -854,8 +854,6 @@ class NPUGenerationModelRunner(OmniNPUModelRunner, OmniConnectorModelRunnerMixin
if self.uses_mrope:
positions = self.mrope_positions.gpu[:, :num_tokens_padded]
elif self.uses_xdrope_dim > 0:
positions = self.xdrope_positions.gpu[:, :num_tokens_padded]
else:
positions = self.positions[:num_tokens_padded]
vllm_omni/platforms/npu/worker/npu_model_runner.py
+7 -4
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@@ -1,6 +1,7 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
from contextlib import AbstractContextManager
from functools import partial
from typing import Any
@@ -35,9 +36,13 @@ else:
class OmniNPUModelRunner(OmniGPUModelRunner, NPUModelRunner):
def initialize_kv_cache(self, kv_cache_config) -> None:
def initialize_kv_cache(
self, kv_cache_config, kv_cache_allocation_context: AbstractContextManager | None = None
) -> None:
"""Stage the omni prefix-cache config (hidden / mm tensors reused on hits)."""
NPUModelRunner.initialize_kv_cache(self, kv_cache_config)
NPUModelRunner.initialize_kv_cache(
self, kv_cache_config, kv_cache_allocation_context=kv_cache_allocation_context
)
if getattr(self, "_omni_prefix_cache_cfg", None) is None:
# Same gate as the GPU runner (pooling stage, kv_consumer /
# kv_both, hybrid kv groups). Read the config back off
@@ -332,8 +337,6 @@ class OmniNPUModelRunner(OmniGPUModelRunner, NPUModelRunner):
if self.uses_mrope:
positions = self.mrope_positions.gpu[:, :num_tokens_padded]
elif self.uses_xdrope_dim > 0:
positions = self.xdrope_positions.gpu[:, :num_tokens_padded]
else:
positions = self.positions[:num_tokens_padded]