Ascend scheduler was added for non chunk prefill case before, since that
the npu ops didn't work well with chunked prefill.
Now the ops with chunked prefill work better, it's time to remove the
ascend scheduler to use vLLM default scheduler.
- vLLM version: v0.11.2
---------
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
The expert mapping table and weights of the dynamic EPLB were not
updated, causing the accuracy to be correct but not effective. This bug
has now been fixed.
- vLLM version: v0.11.2
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.2
---------
Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
Fix model run _npu_flash_attention in _forward_prefill_no_cache hang
issue, it was caused by wrong attention mask dtype.
### How was this patch tested?
Yes, tesed on Qwen2.5-VL and Qwen2.5-Omni
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
Signed-off-by: Ting FU <futing10@huawei.com>
### What this PR does / why we need it?
qwen3-next suppot triton chunk_gated_delta_rule ops
### co-owners
@OsirisDuan
- vLLM version: v0.11.2
Signed-off-by: shiyuan680 <917935075@qq.com>
### What this PR does / why we need it?
1.In short, we renamed the existing MooncakeStoreConnector to
AscendStoreConnector and extracted the storage engine interaction logic
into a new Backend class.
Associated RFC：https://github.com/vllm-project/vllm-ascend/issues/4329
2.Fixed the issue where the number of input parameters for the connector
was incorrect, introduced in vllm 0.11.2
### Does this PR introduce _any_ user-facing change?
change MooncakeStoreConnector to AscendStoreConnector
### How was this patch tested?
- vLLM version: v0.11.2
---------
Signed-off-by: fems14 <1804143737@qq.com>
### What this PR does / why we need it?
This PR introduces support for adding custom CANN `aclnn` ops to
`vllm-ascend`, allowing users to define and use their own custom
operators.
Key changes include:
- Building and installing custom ops into the `vllm-ascend`-specified
directory
- Binding the `aclnn` op interface to the `torch.ops._C_ascend` module
- Enabling invocation of these ops within `vllm-ascend`
This PR includes a sample custom op:
`aclnnGroupedMatmulSwigluQuantWeightNzTensorList`, which is adapted from
the CANN operator
[`aclnnGroupedMatmulSwigluQuantWeightNZ`](https://www.hiascend.com/document/detail/zh/canncommercial/83RC1/API/aolapi/context/aclnnGroupedMatmulSwigluQuantWeightNZ.md).
Its input parameters `weight` and `weight_scale` now accept
`list[torch.Tensor]` (i.e., `at::TensorList`).
### Does this PR introduce _any_ user-facing change?
No.
- vLLM version: v0.11.2
---------
Signed-off-by: QianChenxi <chenxi.qian.cq@outlook.com>
### What this PR does / why we need it?
Delete equals sign in doc
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
ut
- vLLM version: v0.11.2
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.2
---------
Signed-off-by: herizhen <you@example.com>
Co-authored-by: herizhen <you@example.com>
### What this PR does / why we need it?
Add readme for PD separation
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By ci
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
---------
Signed-off-by: wangxiaoteng <wangxiaoteng@huawei.com>
Signed-off-by: liziyu <liziyu16@huawei.com>
Co-authored-by: liziyu <liziyu16@huawei.com>
### What this PR does / why we need it?
- [x] Patch `Qwen2_5_VisionAttention` with
`AscendQwen2_5_VisionAttention`.
- [x] Replace `AscendQwen2_5_VisionTransformer` with
`Qwen2_5_VisionTransformer` in vllm.
- [x] Move padding logic (q/k/v and cos/sin) before FA to `forward()` of
`Qwen2_5_VisionAttention`.
- [x] Covert `cu_seqlens` in `Qwen2_5_VisionAttention` from cumulative
form to intervals and move it to cpu (compatible for npu FA).
- [x] Remove Qwen2.5-VL modeling files.
- [x] Remove Qwen2.5-VL (without padding) modeling files.
- [x] Remove related UT.
- [x] Make `set_forward_context` pluggable when getting MM embedding.
Find more details at https://github.com/vllm-project/vllm/pull/29388.
- [x] Simplify padding logic for FA.
- [x] Add patch for https://github.com/vllm-project/vllm/pull/28798.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
- [x] Functional test (eager mode)
- [x] Functional test (graph mode)
- [x] Benchmark
- vLLM version: v0.11.2
---------
Signed-off-by: shen-shanshan <467638484@qq.com>
### What this PR does / why we need it?
While using the LLM Compressor quantization tool from the VLLM community
to generate quantized weights, the VLLM Ascend engine needs to be
adapted to support the compressed tensors quantization format.
1. Add AscendCompressedTensorsConfig to replace CompressedTensorsConfig
in vllm.
2. Support CompressedTensorsW8A8 static weight.
- weight: per-channel, int8, symmetric; activation: per-tensor, int8,
symmetric.
4. Support CompressedTensorsW8A8Dynamic weight.
- weight: per-channel, int8, symmetric; activation: per-token, int8,
symmetric, dynamic.
5. Modify the override_quantization_method in AscendQuantConfig.
Co-authored-by: taoqun110 taoqun@huawei.com
Co-authored-by: chenxi-hh chen464822955@163.com
- vLLM version: v0.11.2
---------
Signed-off-by: LHXuuu <scut_xlh@163.com>
Signed-off-by: chenxi-hh <chen464822955@163.com>
Signed-off-by: chenxi-hh <32731611+chenxi-hh@users.noreply.github.com>
Co-authored-by: chenxi-hh <chen464822955@163.com>
Co-authored-by: chenxi-hh <32731611+chenxi-hh@users.noreply.github.com>
### What this PR does / why we need it?
Upgrade cann to 8.3rc2
### Does this PR introduce _any_ user-facing change?
Yes, docker image will use 8.3.RC2
- vLLM version: v0.11.2
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.2
---------
Signed-off-by: MrZ20 <2609716663@qq.com>
This PR introduces the `EXEC_NPU_CMD` macro, serving as an adapter layer
to simplify the invocation of `aclnn` operators on Ascend NPUs.
**Key Changes:**
* **Adapter Layer:** Added `EXEC_NPU_CMD` macro and related dependencies
to standardize `aclnn` calls.
* **Operator Support:** Integrated `grouped_matmul_swiglu_quant` as a
reference implementation to demonstrate the usage of the new macro.
---
- vLLM version: v0.11.2
---------
Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
### What this PR does / why we need it?
This patch mainly doing the following things:
1. Make k8s/lws optional for multi-node testing, allowing developers to
run multi-node tests locally by actively passing in the IP addresses of
all nodes.
2. Allows passing a custom proxy script path in the config file to load
the proxy.
- vLLM version: v0.11.2
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
| | cpu envir | npu envir |
|---|---|---|
| set `SOC_VERSION` | check if `SOC_VERSION` is in dict `soc_to_device`,
if not, raise an error that can not support current chip type. | print a
warning log when `SOC_VERSION` is not equal to chip type from `npu-smi`,
same as left for others. |
| not set `SOC_VERSION` | raise an error that `SOC_VERSION` is necessary
when compiling in a cpu envir. | use chip type from `npu-smi` to compile
vllm-ascend. |
### Does this PR introduce _any_ user-facing change?
Now we must set env `SOC_VERSION` when compiling in cpu envir.
### How was this patch tested?
- vLLM version: v0.11.2
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.2
Signed-off-by: zzzzwwjj <1183291235@qq.com>
### What this PR does / why we need it?
When running 'python example.py',connection issues often occur.The
solution is to comment out the first line the code.
Complete the specific names of machines A2 and A3.
Standardize document format,a space should be added after the colon.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
ut
- vLLM version: v0.11.2
---------
Signed-off-by: herizhen <you@example.com>
Co-authored-by: herizhen <you@example.com>
### What this PR does / why we need it?
Currently, there are two paths to judge the chip type in code,
`get_ascend_soc_version` use `get_soc_version` api in torch_npu, and
`is_310p` `use _build_info.__soc_version__`, which generate when
install. We need to unify the two paths.
We need to unify these codes based on the following points:
1. We need to ensure consistency in chip type judgment between compiling
and running states;
2. In compiling state, we need chip type to complete op's compilation,
but in running state, we only need device
type(910B/910_93/310P/910_95/etc) to make code branch judgement;
3. In compiling state, torch_npu may not have been installed yet, so we
can't use torch_npu's api.
Based on the above points, we have made the following changes:
1. When user set env `SOC_VERSION`, use it; when not set, query
soc_version by `npu-smi`;
2. generate device_type based on soc_version when compiling, and write
`__device_type__` instead of `__soc_version__` in `_build_info.py`;
3. In running state, use `__device_type__` to judge code branch.
### Does this PR introduce _any_ user-facing change?
When not set env `SOC_VERSION`, it will not be `ASCEND910B1` by default,
we will query soc_version by `npu-smi`. And env `SOC_VERSION` must be in
the list `soc_to_device` in `setup.py`.
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
Signed-off-by: zzzzwwjj <1183291235@qq.com>
1. Run 4-card test only when single and 2-card test passed
2. rename file to make it more clear
3. remove useless pd workflow, it has been managed by nightly test
already.
- vLLM version: v0.11.2
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.2
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
mkdir triton package and move triton files
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
Signed-off-by: shiyuan680 <917935075@qq.com>
### What this PR does / why we need it?
Delete useless comments.
### Does this PR introduce _any_ user-facing change?
No
- vLLM main:
2918c1b49c
Signed-off-by: GDzhu01 <809721801@qq.com>
Torch-npu 2.7.1 has fixed the device check bug. This patch can be
removed now.
- vLLM main:
2918c1b49c
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
To fix ops test, where `model_config` has been set to `None` and doesn't
has `hf_config` attribute, we have added a check for `model_config` to
guarantee it is not `None_Type`.
- vLLM main:
2918c1b49c
Signed-off-by: shen-shanshan <467638484@qq.com>
### What this PR does / why we need it?
**Problem**: The Qwen3Next model implementation currently imports
chunk_gated_delta_rule directly using `from ... import ...`
In frameworks like `verl`, the model file is often imported before
`vllm-ascend` initializes and applies its patches. This causes the model
to permanently hold a reference to the original (unpatched) vLLM kernel,
resulting in execution errors on Ascend devices even if the patch runs
later.
**Solution**: Changed the import style to `from vllm...ops import chunk`
and call `chunk.chunk_gated_delta_rule().`
This ensures that the function lookup happens at runtime (dynamic
dispatch), allowing the model to correctly pick up the patched function
regardless of import order.
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
Signed-off-by: zjchenn <zjchenn@gmail.com>
### What this PR does / why we need it?
This PR pins the transformers dependency to 4.57.1.
Reason: CI tests (specifically test_completion_with_prompt_embeds.py)
are failing with an AttributeError: 'dict' object has no attribute
'model_type' when using newer versions of transformers.
The issue stems from a bug in tokenization_utils_base.py where the code
attempts to access the model_type field of a configuration dictionary
(_config) using dot notation (_config.model_type) instead of dictionary
key lookup (_config["model_type"] or _config.get("model_type")). This
occurs in the logic block checking for transformers_version <= 4.57.2.
Pinning the version to 4.57.1 bypasses this buggy code path and restores
CI stability.
Error Traceback:
``` shell
/usr/local/python3.11.13/lib/python3.11/site-packages/transformers/tokenization_utils_base.py:2419:
if _is_local and _config.model_type not in [
E AttributeError: 'dict' object has no attribute 'model_type'
```
- vLLM main:
2918c1b49c
Signed-off-by: MrZ20 <2609716663@qq.com>
### What this PR does / why we need it?
Fix a bug caused by this pr:
https://github.com/vllm-project/vllm-ascend/pull/4223
The bug makes
vllm-ascend/vllm_ascend/patch/platform/patch_multiproc_executor.py patch
in a wrong way
### How was this patch tested?
Tested in a single node. When the environment DYNAMIC_EPLB is set to
true, the patch works correctly. When it's set to false, the patch do
not patch
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
Signed-off-by: 白永斌 <baiyongbin3@h-partners.com>
Co-authored-by: 白永斌 <baiyongbin3@h-partners.com>
### What this PR does / why we need it?
The "g" at the beginning of the current sentence is redundant and needs
to be deleted
"MindIE Turbo" is no longer required to be displayed.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
ut
- vLLM main:
2918c1b49c
---------
Signed-off-by: herizhen <you@example.com>
Co-authored-by: herizhen <you@example.com>
### What this PR does / why we need it?
When cudagraph_mode is set to FULL_DECODE_ONLY, if dp > 1, the dummy-run
process will be triggered. When calling the update_attn_params function,
the num_tokens parameter needs to be passed, and this value is obtained
through positions.shape[0]. However, the multimodal model uses mRope
(multi-dimensional rotary positional embeddings), which causes the shape
of positions to be 2. As a result, the value obtained from
positions.shape[0] is incorrect. We solve this problem by replacing
positions.shape[0] with num_tokens.
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
vLLM version: v0.11.0rc3
vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
---------
Signed-off-by: wujinyuan1 <wjy9595@qq.com>
Co-authored-by: wujinyuan1 <wjy9595@qq.com>
### What this PR does / why we need it?
vllm-ascend need to dump data during model execution to debug some
precision problems, here msprobe provide the corresponding abilities, so
msprobe will join vllm-ascend to make debug easier
### Does this PR introduce _any_ user-facing change?
```
'dump_config': '/path/to/config.json'
```
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
---------
Signed-off-by: Tjh-UKN <2559659915@qq.com>
### What this PR does / why we need it?
Temporarily fix the oom issue, will update to vllm's plan later.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
e2e&ut
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
The main purposes of this PR are as follows:
1. Remove the multicast-related code;
Reason:
1. In the scenario like a2 Dual-System Back-to-Back Networking，the
performance is worse than all_gather. Before the modification, in e2e
test, it was 3 tps; after the modification, it is 10 tps.
2. At the same time, we usually enable the SP feature，it is consistent
with the current logic.
3. The advantage of broadcast communication lies in the fact that it
does not suffer from uneven DP load and does not require the prefill ACL
graph to be enabled. But we support prefill Acl graph recently.
So we think there is no need to maintain the multicast as one choice in
moe communication.
Performance benefits are as follows:
When not enable_flashcomm1, TTFT remains relatively stable at around
43000ms, which is approximately 15000ms faster than before the
modification.
When enable_flashcomm1, there is no diffenence, TTFT remains relatively
stable at around 29000ms.
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
---------
Signed-off-by: weijinqian_v1 <weijinqian@huawei.com>
Signed-off-by: weijinqian0 <1184188277@qq.com>
Co-authored-by: weijinqian_v1 <weijinqian@huawei.com>
### What this PR does / why we need it?
Due to the inconsistency between the attention operators used in eager
mode and graph mode, the accumulation order of the operator cannot be
guaranteed to be deterministic. Therefore, we modify the test to compare
with given outputs.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
Signed-off-by: wangxiaoxin-sherie <wangxiaoxin7@huawei.com>
Co-authored-by: wangxiaoxin-sherie <wangxiaoxin7@huawei.com>
There is a lot hack code for v0.11.0, which makes the code hard to
upgrade to newer vLLM version. Since v0.11.0 will release soon. Let's
drop v0.11.0 support first. Then we'll upgrade to v0.11.2 soon.
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
This PR updates the acc standard for deepseek mtpx cases, according to
inner standard
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running the test
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
Signed-off-by: jiangyunfan1 <jiangyunfan1@h-partners.com>
### What this PR does / why we need it?
Remove unnecessary NPU synchronization in MTP proposer to improve
performances.
Removing this synchronization point improves pipeline efficiency by
allowing for better overlap between CPU and NPU operations. A more
proper one is already implemented in #4233
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
None.
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
The first letter of the English title should be capitalized
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
ut
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
Signed-off-by: herizhen <you@example.com>
Co-authored-by: herizhen <you@example.com>
### What this PR does / why we need it?
When we are using `Ascend scheduler`, the param `max_num_batched_tokens`
should be larger than `max_model_len`, otherwise, will encountered the
follow error:
```shell
Value error, Ascend scheduler is enabled without chunked prefill feature. Argument max_num_batched_tokens (4096) is smaller than max_model_len (32768). This effectively limits the maximum sequence length to max_num_batched_tokens and makes vLLM reject longer sequences. Please increase max_num_batched_tokens or decrease max_model_len. [type=value_error, input_value=ArgsKwargs((), {'model_co...g': {'enabled': True}}}), input_type=ArgsKwargs]
```
### Does this PR introduce _any_ user-facing change?
Users/Developers who running the model according to the
[tutorial](https://docs.vllm.ai/projects/ascend/en/latest/tutorials/multi_node.html),
the parameters can be specified correctly.
### How was this patch tested?
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
