### What this PR does / why we need it?
Really strange that `register_oot` doesn't work with `SharedFusedMoE`,
so we have to add this patch, for now.
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
This PR won't have any effect in DeepSeek since we currently still stick
with the old `CustomDeepseekV2`.
- vLLM version: v0.10.1.1
- vLLM main:
0cdd213641
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
Cleanup useless file in patch module. Update the lora support list is OK
in vLLM Ascend, no need to patch vLLM
- vLLM version: v0.10.1.1
- vLLM main:
f4962a6d55
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
This PR introduces Oproj matrix tensor model parallel to achieve
decreasing of memory consumption. It only support graph mode in pure DP
scenario.
In deepseek r1 w8a8 PD disagregated Decode instance, using pure DP, with
oproj_tensor_parallel_size = 8, we have 1 ms TPOT increasing, saved 5.8
GB NPU memory per RANK. We got best performance when
oproj_tensor_parallel_size=4 without TPOT increasing.
performance data:
<img width="1442" height="442" alt="image"
src="https://github.com/user-attachments/assets/83270fc5-868a-4387-b0a9-fac29b4a376d"
/>
### Does this PR introduce _any_ user-facing change?
This PR introduces one new config in `additional_config`.
| Name | Effect | Required | Type | Constraints |
| :---------------------------- |
:--------------------------------------- | :------- | :--- |
:----------------- |
| oproj_tensor_parallel_size | Split the o_proj matrix along the row
dimension (head num * head dim) into oproj_tensor_parallel_size pieces.
| No | int | default value is None, once this value is set, the feature
will be enabled, head num * head dim must be divisible by this value. |
example
`--additional_config={"oproj_tensor_parallel_size": 8}`
### How was this patch tested?
- vLLM version: v0.10.1.1
- vLLM main:
eddaafc1c7
---------
Signed-off-by: zzhx1 <zzh_201018@outlook.com>
Co-authored-by: zzh <zzh_201018@outlook.com>
### What this PR does / why we need it?
Fix the LoRA accuracy issue that introduced by custom AscendC operator
"bgmv_shrink, sgmv_shrink, bgmv_expand, sgmv_epand".
The bug details are:
- In the kernel function, if you want to call GlobalTensor.GetSize
method, you have to pass the second parameter of bufferSize when you
call GlobalTensor.SetGlobalBuffer first.
- Or GlobalTensor.GetSize method will return a random value.
- You can refer to [this
doc](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/81RC1alpha002/apiref/ascendcopapi/atlasascendc_api_07_00024.html).
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
pytest -sv tests/e2e/singlecard/test_ilama_lora.py
pytest -sv tests/e2e/multicard/test_ilama_lora_tp2.py
- vLLM version: v0.10.1.1
- vLLM main:
a344a5aa0a
---------
Signed-off-by: paulyu12 <paulyu0307@gmail.com>
Signed-off-by: paulyu12 <507435917@qq.com>
Co-authored-by: paulyu12 <paulyu0307@gmail.com>
### What this PR does / why we need it?
The mergence of the upstream
https://github.com/vllm-project/vllm/pull/22592 caused a vllm-ascend
LoRA inference bug. The details are following:
According to
[torch_npu/npu/_stream_check.py](863b9071cb/torch_npu/npu/_stream_check.py (L74)),
NPU device type tensors have attributes is_cuda=True and is_npu=True.
This causes that vLLM's apply_repetition_penalties function will run
into the branch of "if logits.is_cuda and logits.is_contiguous()" and
call the custom op implemented in CUDA, which is not compatible with
NPU.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
pytest -sv tests/e2e/singlecard/test_ilama_lora.py
pytest -sv tests/e2e/multicard/test_ilama_lora_tp2.py
- vLLM version: v0.10.1.1
- vLLM main:
fe8d7b6f03
---------
Signed-off-by: paulyu12 <paulyu0307@gmail.com>
Signed-off-by: paulyu12 <507435917@qq.com>
Co-authored-by: paulyu12 <paulyu0307@gmail.com>
### What this PR does / why we need it?
1. update `CachedRequestState` as `NewRequestData` changed in
https://github.com/vllm-project/vllm/pull/22570
2. drop maintenance of vllm v0.10.0 in the branch main
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed with existing test.
- vLLM version: v0.10.0
- vLLM main:
92ff41abea
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
Remove redundant imported `envs`, using `envs_ascend` instead.
```python
import vllm.envs as envs_vllm
import vllm_ascend.envs as envs_ascend
```
- vLLM version: v0.10.0
- vLLM main:
71683ca6f6
---------
Signed-off-by: shen-shanshan <467638484@qq.com>
### What this PR does / why we need it?
This PR is to add e2e test for using npu_mm_all_reduce_base fusion
kernel.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
not involved
- vLLM version: v0.10.0
- vLLM main:
5d5d419ca6
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
Refactor Sampler implementation from patch way to inherit from vLLM
Sampler interface.
Next step: Make the op `TopKTopPSampler` in vLLM support custom ops
register mechanism
- vLLM version: v0.10.0
- vLLM main:
61a6905ab0
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
it'll execute allreduce and malmul seperately in vllm RowParallelLinear
forward funcion, this function use torch_npu.npu_mm_all_reduce_base to
execute allreduce and matmul in a fused kernel way. this will gain a 20%
performance
promotion in eager mode.
### Does this PR introduce _any_ user-facing change?
this PR introduce a new env `VLLM_ASCEND_ENABLE_MATMUL_ALLREDUCE` to
control whether enable the feature or not.
### How was this patch tested?
the patch is tested by adding a new test file `test_patch_linear.py` to
guard the ut
- vLLM version: v0.10.0
- vLLM main:
7728dd77bb
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
### What this PR does / why we need it?
- Upgrade to v0.10.0
- Drop v0.9.2 version compatibility
- Add patch for
`vllm_ascend/patch/worker/patch_common/patch_sampler_gather_logprobs.py`
as workaround of
f3a683b7c9
for v0.10.0 and also add e2e test `test_models_prompt_logprobs`
- Pin transformers<4.54.0 as workaround of
https://github.com/vllm-project/vllm-ascend/issues/2034
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Test locally:
`VLLM_USE_MODELSCOPE=true pytest -sv
tests/e2e/singlecard/test_offline_inference.py::test_models_prompt_logprobs`
- CI passed
- vLLM version: v0.9.2
- vLLM main:
7728dd77bb
---------
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
Remove ETP/EP maintained in branch main. We drop this as there is no
relevant scenarios to use ETP now, and we may subsequently advocate
implementing expert tensor parallelism in vLLM to support scenarios
where the expert is needed to be sliced
This is a part of #1422 backport.
Fixes https://github.com/vllm-project/vllm-ascend/issues/1396https://github.com/vllm-project/vllm-ascend/issues/1154
### Does this PR introduce _any_ user-facing change?
We'll not maintain etp/ep in vllm-ascend anymore, and use the tp/ep in
vllm instead.
### How was this patch tested?
CI passed with new added and existing test.
- vLLM version: v0.9.2
- vLLM main:
fe8a2c544a
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
Performance optimization for apply_top_k_top_p
### Does this PR introduce _any_ user-facing change?
Use VLLM_ASCEND_ENABLE_TOPK_TOPP_OPTIMIZATION to enable this feature
### How was this patch tested?
e2e & ut
- vLLM version: v0.9.2
- vLLM main:
6a9e6b2abf
Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
vllm has released 0.9.2. This PR drop 0.9.1 support.
- vLLM version: v0.9.1
- vLLM main:
b942c094e3
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
This PR supports torchair graph mode with non-mla backend on both 800IA2
and 300I Duo platforms. The main change is to add
`attention_v1_torchair.py` to support specific attention related
operations that are required by torchair.
### Does this PR introduce _any_ user-facing change?
Before this PR, vLLM-Ascend only allows deepseek to use torchair. Now we
can also use it with pangu. Besides, we add a support model list to
control which type of models that can use torchair.
### How was this patch tested?
We have test it with PanguProMoE on both 800IA2 and 300I Duo platforms,
and model generates answer normally.
---------
Signed-off-by: angazenn <zengyanjia@huawei.com>
Signed-off-by: tianyitang <tangtianyi4@huawei.com>
Co-authored-by: angazenn <zengyanjia@huawei.com>
Co-authored-by: tianyitang <tangtianyi4@huawei.com>
### What this PR does / why we need it?
This PR fixes a bug that use broadcast with cpu_group when running dp.
The `broadcast310p` patch will take effects for both cpu_group and
device group, but we only need it for device group. Hence a wrapper is
added to allow cpu_group use native torch broadcast and it solves the
bug.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
With this PR, DP on 310p runs normally and generates reasonable answers.
Signed-off-by: angazenn <zengyanjia@huawei.com>
Co-authored-by: angazenn <zengyanjia@huawei.com>
Add static build_info py file to show soc and sleep mode info. It helps
to make the code clean and the error info will be more friendly for
users
This PR also added the unit test for vllm_ascend/utils.py
This PR also added the base test class for all ut in tests/ut/base.py
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
Use fused ops torch_npu.npu_top_k_top_p(logits, p, k) when p and k are
not None, otherwise fallback to the original one. The replacement will
take place automatically when `VLLM_ASCEND_ENABLE_TOPK_OPTIMIZE=1` .
This patch are using `npu_top_k_top_p` which required
torch_npu>=2.5.1.post1.dev20250619
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Tested by DeepSeek R1 and UT passed
Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
### What this PR does / why we need it?
This PR aims to address a long-standing **CI bug** and remove unused
code. The specific changes include:
1. **Fixing CI Bug**: Resolves the root cause of CI test failures or
instability. This often stems from incorrect environment configurations,
dependency version conflicts, or flawed test script logic. This fix
ensures the reliability and consistency of the CI pipeline.
2. **Removing `patch_eagle.py`**: Deletes the `patch_eagle.py` file,
which is no longer utilized by the project. This file was likely legacy
code, experimental code, or its functionality has since been replaced by
other modules. Its removal helps reduce codebase complexity, improves
maintainability, and prevents potential confusion.
### Does this PR introduce _any_ user-facing change?
No, this PR primarily focuses on internal CI stability maintenance and
code cleanup. It does not introduce any user-visible changes to APIs,
interfaces, or other behaviors.
### How was this patch tested?
CI passed. Specifically:
1. **Existing CI Pipelines Passed**: After fixing the CI bug, all
existing CI tests and pipelines were verified to run correctly and pass
successfully.
2. **Code Cleanup Verified**: Following the removal of `patch_eagle.py`,
it was ensured that any related functional modules (if applicable)
continue to work as expected, without introducing new regressions. This
was typically verified by running the project's main test suite.
Signed-off-by: yuancaoyaoHW <a2749322671@gmail.com>
### What this PR does / why we need it?
`stateless_init_dp_group` in vllm works with non-cuda platform now.
Remove this useless patch.
Which was introduced in vllm-ascend by
e74331a1ed
(v0.8.4rc2)
vLLM upstream merged:
3e472d882a
(v0.8.0)
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
1. [PR913](https://github.com/vllm-project/vllm-ascend/pull/913)
introduced an error that caused V0's spec decode function to fail.
[PR1109](https://github.com/vllm-project/vllm-ascend/pull/1109) wanted
to fix this problem. Unfortunately, the fix broke the ngram function. I
fixed the ngram function in this PR. **PS**: Q: Why is there a problem
when ngram is not found when pr1109 is merged? A: The newly introduced
problem will only appear when tp>1, and the use cases on CI are all tp=1
2. In versions after 0.7.3, vllm-ascend deleted some spec decode UTs to
avoid CI taking too long, including eagle speculative UTs, which made CI
unable to take care of the eagle function. I added
it(`test_eagle_correctness.py`) back in this PR
3. Because of the reason mentioned in 2, the current version of Eagle
has a problem. I located and fixed this problem. It was because vllm's
`draft_model_runner.py` was changed and vllm-ascend was not synchronized
in time.
4. Currently, the UTs of v0 and v1 are mixed in the spec_decode
directory. I split them into two directories: spec_decode_v0 and
spec_decode_v1.
5. i found
`vllm.spec_decode.multi_step_worker.MultiStepWorker.set_include_gpu_probs_tensor`
and
`vllm.spec_decode.multi_step_worker.MultiStepWorker.set_should_modify_greedy_probs_inplace`
have changed in vllm, so i remove it in this pr.
### Does this PR introduce _any_ user-facing change?
This PR fixes the functions of ngram and eagle spec decode in the v0
engine
### How was this patch tested?
tested by CI
Signed-off-by: mengwei805 <mengwei25@huawei.com>
### What this PR does / why we need it?
Add initial experimental support for Ascend 310P, this patch squash
below PR into one to help validation:
- https://github.com/vllm-project/vllm-ascend/pull/914
- https://github.com/vllm-project/vllm-ascend/pull/1318
- https://github.com/vllm-project/vllm-ascend/pull/1327
### Does this PR introduce _any_ user-facing change?
User can run vLLM on Altlas 300I DUO series
### How was this patch tested?
CI passed with:
- E2E image build for 310P
- CI test on A2 with e2e test and longterm test
- Unit test missing because need a real 310P image to have the test,
will add in a separate PR later.
- Manually e2e test:
- Qwen2.5-7b-instruct, Qwen2.5-0.5b, Qwen3-0.6B, Qwen3-4B, Qwen3-8B:
https://github.com/vllm-project/vllm-ascend/pull/914#issuecomment-2942989322
- Pangu MGoE 72B
The patch has been tested locally on Ascend 310P hardware to ensure that
the changes do not break existing functionality and that the new
features work as intended.
#### ENV information
CANN, NNAL version: 8.1.RC1
> [!IMPORTANT]
> PTA 2.5.1 version >= torch_npu-2.5.1.post1.dev20250528 to support NZ
format and calling NNAL operators on 310P
#### Code example
##### Build vllm-ascend from source code
```shell
# download source code as vllm-ascend
cd vllm-ascend
export SOC_VERSION=Ascend310P3
pip install -v -e .
cd ..
```
##### Run offline inference
```python
from vllm import LLM, SamplingParams
prompts = ["水的沸点是100摄氏度吗？请回答是或者否。", "若腋下体温为38摄氏度，请问这人是否发烧？请回答是或者否。",
"水的沸点是100摄氏度吗？请回答是或者否。", "若腋下体温为38摄氏度，请问这人是否发烧？请回答是或者否。"]
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.0, top_p=0.95, max_tokens=10)
# Create an LLM.
llm = LLM(
model="Qwen/Qwen2.5-7B-Instruct",
max_model_len=4096,
max_num_seqs=4,
dtype="float16", # IMPORTANT cause some ATB ops cannot support bf16 on 310P
disable_custom_all_reduce=True,
trust_remote_code=True,
tensor_parallel_size=2,
compilation_config={"custom_ops":['none', "+rms_norm", "+rotary_embedding"]},
)
# Generate texts from the prompts.
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
---------
Signed-off-by: Vincent Yuan <farawayboat@gmail.com>
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
Signed-off-by: angazenn <zengyanjia@huawei.com>
Co-authored-by: Vincent Yuan <farawayboat@gmail.com>
Co-authored-by: angazenn <zengyanjia@huawei.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
Co-authored-by: leo-pony <nengjunma@outlook.com>
Co-authored-by: shen-shanshan <467638484@qq.com>
1. upgrade vllm to 0.9.1. 0.9.0 is not supported for main branch now.
keep doc to 0.9.0 until we release the first 0.9.1 release.
2. disable V0 test for PR
3. move actionlint check to lint job
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Make sure the lint test passed before start the e2e test to save compute
resource.
Updated the patch doc to make sure the CI works as expect.
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
Remove `spec_decode.metrics` patch as this has been resolved in
https://github.com/vllm-project/vllm/pull/16983 (include in vllm
`v0.9.0`).
Returns a CUDA event recording when the copy is complete **--after
modified-->** Returns a device event (NPU Event for vllm-ascend)
recording when the copy is complete.
Signed-off-by: shen-shanshan <467638484@qq.com>
### What this PR does / why we need it?
With this PR, we can migrate to the native `data_parallel.py` in vllm
examples and remove the version in vllm-ascend.
At present, `ASCEND_RT_VISIBLE_DEVICES` introduces considerable
difficulties; therefore, we must employ a temporary workaround and
manually specify the device.
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
Fix typo of VLLM_ASCEND_ENABLE_TOPK_OPTIMIZE
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed
Signed-off-by: linfeng-yuan <1102311262@qq.com>
### What this PR does / why we need it?
- Set default values to fix spec decode
- To avoid oom, we need to run the test in a single process
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- CI passed, espcecially multicards CI
- For spec decode test, long term CI passed
Closes: https://github.com/vllm-project/vllm-ascend/pull/1105
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
Co-authored-by: Yizhou Liu <liu_yizhou@outlook.com>
Co-authored-by: mengwei805 <mengwei25@huawei.com>
### What this PR does / why we need it?
Optimize the performance of calculation logic in sampler and deepseekv2.
### Does this PR introduce _any_ user-facing change?
Added VLLM_ENABLE_TOPK_OPTIMZE config in sampler
### How was this patch tested?
pytest test_sampler.py
Signed-off-by: wangxiaoxin (A) <wangxiaoxin7@huawei.com>
Co-authored-by: wangxiaoxin (A) <wangxiaoxin7@huawei.com>
Co-authored-by: ZhengWG <zwg0606@gmail.com>
### What this PR does / why we need it?
Solve the bug that the graph mode is the same as p and d, and some other
bugs.
### Does this PR introduce _any_ user-facing change?
Wouldn't be
### How was this patch tested?
Follow the end-to-end test
Signed-off-by: ningbenzhe1 <ningbenzhe@huawei.com>
<!-- Thanks for sending a pull request!
BEFORE SUBMITTING, PLEASE READ
https://docs.vllm.ai/en/latest/contributing/overview.html
-->
### What this PR does / why we need it?
<!--
- Please clarify what changes you are proposing. The purpose of this
section is to outline the changes and how this PR fixes the issue.
If possible, please consider writing useful notes for better and faster
reviews in your PR.
- Please clarify why the changes are needed. For instance, the use case
and bug description.
- Fixes #
-->
Make spec decode support for V1 Engine
- Currently, Ascend does not support the triton kernel. PyTorch is used
to rewrite the `rejection_sampler.py` triton kernel. However, PyTorch is
not as good as Triton. Therefore, ascend c is used to implement the
function in the future.
- Currently, spec decode supports only the ngram algorithm. The eagle
algorithm needs to be further adapted.
### Does this PR introduce _any_ user-facing change?
<!--
Note that it means *any* user-facing change including all aspects such
as API, interface or other behavior changes.
Documentation-only updates are not considered user-facing changes.
-->
Not change user facing.
### How was this patch tested?
<!--
CI passed with new added/existing test.
If it was tested in a way different from regular unit tests, please
clarify how you tested step by step, ideally copy and paste-able, so
that other reviewers can test and check, and descendants can verify in
the future.
If tests were not added, please describe why they were not added and/or
why it was difficult to add.
-->
test by `tests/singlecard/spec_decode/e2e/test_v1_spec_decode.py` and
`tests/sample/test_rejection_sampler.py`, test base function of
rejection sampler and e2e function of spec decode.
Signed-off-by: ponix-j <657511300@qq.com>
<!-- Thanks for sending a pull request!
BEFORE SUBMITTING, PLEASE READ
https://docs.vllm.ai/en/latest/contributing/overview.html
-->
### What this PR does / why we need it?
1. This PR introduces native `all_to_all` communication operator to fix
`allgather` bugs when dp_size > 1. Besides, it adds a naive
implementation of force-load-balance when doing profile runs.
2. The operator `npu_dequant_swiglu_quant` only supports input
hidden_states with dtype `torch.int32`. This tensor occupies space of
`global_bs * seq_len * topk * hidden_size`, which might be very large as
`ep_size` grows. Therefore we need to disable this operator and use
original `swiglu` && `quantize`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
By performing offline inference:
---------
Signed-off-by: angazenn <zengyanjia@huawei.com>
Co-authored-by: angazenn <zengyanjia@huawei.com>
Patch torch.library.infer_schema for torch 2.5 backward compatibility
- Introduced a new module `patch_utils` under
`vllm_ascend/patch/worker/patch_common/`.
- Added a function `ascend_direct_register_custom_op` to handle custom
operator registration with backward compatibility for PyTorch < 2.7
(such as torch 2.5.1).
- Implemented type conversion logic for annotations to ensure
compatibility across different PyTorch versions.
- Registered the function `ascend_direct_register_custom_op` to
`utils.direct_register_custom_op`.
- Updated `__init__.py` to include `patch_utils` as the first import.
- Ensured `patch_utils` is available for use in other patch files and
skipped isort checks for `patch_utils` import.
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
- Revert "Re-patch TritonPlaceholder on main to make CI happy (#753)"
because upstream main CI already merged:
https://github.com/vllm-project/vllm/pull/17446
- Keep 0.8.5.post1 compatible
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed
---------
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
Platform should only contain the function that based from vllm. This PR
move the unrelated function to the right place to make platform more
clear.
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
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### What this PR does / why we need it?
As custom deepseek modeling do some changes to support graph mode in
https://github.com/vllm-project/vllm-ascend/pull/585, so i follow it to
change custom deepseek_mtp modeling.
And some modifications for k>1 were not carried over by the
https://github.com/vllm-project/vllm-ascend/pull/429, now i add it.
In order to better take care of the MTP feature in the vllm-ascend
repository, I added cases related to graph mode(torchair), but i skip it
since torchair can not correctly clean up memory in vllmrunner.
Also i add some case for MTP quantization weights, but test weight is
not ready, so i skip it and i will open it when test quant weights is
ready.
https://github.com/vllm-project/vllm-ascend/pull/648 did not completely
fix the sample
change(https://github.com/vllm-project/vllm-ascend/issues/660) issue, I
added the relevant changes.
### Does this PR introduce _any_ user-facing change?
now, u can use following method to use mtp in deepseek v3/r1 float or
quant weights with eager mode.
```python
llm = LLM(
model="wemaster/deepseek_mtp_main_random_bf16",
tensor_parallel_size=2,
speculative_config={
"num_speculative_tokens": 1,
},
enforce_eager=True,
trust_remote_code=True,
disable_log_stats=False,
gpu_memory_utilization=0.8,
max_model_len=64,
)
```
or use mtp in deepseek v3/r1 float or quant weights with graph
mode（torchair）
```python
llm = LLM(
model="wemaster/deepseek_mtp_main_random_bf16",
tensor_parallel_size=2,
speculative_config={
"num_speculative_tokens": 1,
},
trust_remote_code=True,
additional_config={
'enable_graph_mode': True,
},
disable_log_stats=False,
gpu_memory_utilization=0.8,
max_model_len=64,
)
```
add notes:
1. now, we support k>1, so u can set num_speculative_tokens > 1 if there
is sufficient redundant computing power;
2. MTP is not supported in V1, we will support it when vLLM does it in
https://github.com/vllm-project/vllm/issues/13500.
3. if u run MTP failed by `segmentation fault`, u can follow v0.7.3
patch https://github.com/vllm-project/vllm-ascend/pull/236 file
`vllm_ascend/patch/patch_metrics.py` method
`__npu_async_metrics_collector_init__`
### How was this patch tested?
local tested passed and test by CI
Signed-off-by: mengwei805 <mengwei25@huawei.com>
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### What this PR does / why we need it?
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and bug description.
- Fixes #
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Add dp stateless process group initialization path with hccl backend as
vllm-ascend patch.
### Does this PR introduce _any_ user-facing change?
<!--
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as API, interface or other behavior changes.
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### How was this patch tested?
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---------
Signed-off-by: ganyi <pleaplusone.gy@gmail.com>
