### What this PR does / why we need it?
This patch supports pipeline parallel in V1 Engine
### Does this PR introduce _any_ user-facing change?
Yes, users can run PP in V1
### How was this patch tested?
Manully test
- vLLM version: v0.9.2
- vLLM main:
31d5c1797f
Signed-off-by: weiguihua2 <weiguihua2@huawei.com>
### What this PR does / why we need it?
Follow vllm-project/vllm lint way:
https://github.com/vllm-project/vllm/blob/main/.pre-commit-config.yaml
Enable pre-commit to avoid some low level error AMAP.
This pr is one step of #1241, The purpose is make linting system more
clear and convenient, on this step, Mainly did the following things:
yapf, actionlint, ruff, typos, isort, mypy, png-lint, signoff-commit,
enforce-import-regex-instead-of-re.
TODO:
- clang-format(check for csrc with google style)
need clean code, disable for now
- pymarkdown
need clean code, disable for now
- shellcheck
need clean code, disable for now
### Does this PR introduce _any_ user-facing change?
Only developer UX change:
https://vllm-ascend--1256.org.readthedocs.build/en/1256/developer_guide/contributing.html#run-lint-locally
```
pip install -r requirements-lint.txt && pre-commit install
bash format.sh
```
### How was this patch tested?
CI passed with new added/existing test.
Co-authored-by: Yikun [yikunkero@gmail.com](mailto:yikunkero@gmail.com)
Co-authored-by: wangli
[wangli858794774@gmail.com](mailto:wangli858794774@gmail.com)
- vLLM version: v0.9.1
- vLLM main:
5358cce5ff
---------
Signed-off-by: wangli <wangli858794774@gmail.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?
1、Sometimes loading torchair cache will fail because of the floating of
npu memory, so this pr add a new cache to save the old kv cache bytes to
avoid the possible crash while loading the torchair graph cache.
2、When caching is enabled and does not exist, the first compilation
introduces the overhead of Dynamo Gurad. So in this case, we will
compile them directly twice to skip them (This will bring 3-4 ms of tpot
optimization)
### Does this PR introduce _any_ user-facing change?
Add a new env `VLLM_ASCEND_KV_CACHE_MEGABYTES_FLOATING_TOLERANCE` to
control kv cache floating tolerance
### How was this patch tested?
- vLLM version: v0.9.1
- vLLM main:
1fd471e957
Signed-off-by: boying <897013703@qq.com>
### What this PR does / why we need it?
Since running on Altlas 300I Duo was initial supported after #1333 ,
this PR will disable the JIT compiler for the 310P and changed the data
format to NZ for the weight in the vocabulary embedding and QKV
projection layers, which help improving performance.
See #1563
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Test manually:
https://github.com/vllm-project/vllm-ascend/pull/1591#issuecomment-3028352339
Signed-off-by: Vincent Yuan <farawayboat@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 supports w8a8 on 300I Duo platform. The main change is to use
`npu_quant_grouped_matmul_dequant` to replace `npu_grouped_matmul`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
offline inference on 310p runs 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?
1. drop some useless code for w8a8 fusedmoe
2. Add in8 kv cache check
3. Add more ut.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed with new added test.
---------
Signed-off-by: zhuyilin <809721801@qq.com>
Signed-off-by: tianyitang <tangtianyi4@huawei.com>
Co-authored-by: tianyitang <tangtianyi4@huawei.com>
### What this PR does / why we need it?
This PR (adapted from
2863befce3)
updates the CachedRequestData definition to use a single instance shared
across all requests in a batch, instead of creating a new instance per
request.
Found ci boken by the vllm's model_runner change: `ERROR 07-01 09:53:53
[core.py:521] TypeError: 'CachedRequestData' object is not iterable`,
Modify the model_runner to fix it.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
pass ci will verify this.
---------
Signed-off-by: ganyi <pleaplusone.gy@gmail.com>
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
Co-authored-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
Change as little existing code as possible to add v1 pooling task's
support, notice that i move down the `vllm.v1.worker.gpu_input_batch` to
vllm-ascend, Considering the frequent changes in upstream interfaces, in
order to decouple, so i move it here
### How was this patch tested?
CI passed with new added/existing test, and I have a simple test was
first conducted locally which is adapted from
https://www.modelscope.cn/models/Qwen/Qwen3-Embedding-0.6B, just like
bellow：
```python
import os
import torch
from vllm import LLM
os.environ["VLLM_USE_MODELSCOPE"]="True"
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery:{query}'
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'What is the capital of China?'),
get_detailed_instruct(task, 'Explain gravity')
]
# No need to add instruction for retrieval documents
documents = [
"The capital of China is Beijing.",
"Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun."
]
input_texts = queries + documents
model = LLM(model="Qwen/Qwen3-Embedding-0.6B", task="embed")
outputs = model.embed(input_texts)
embeddings = torch.tensor([o.outputs.embedding for o in outputs])
scores = (embeddings[:2] @ embeddings[2:].T)
print(scores.tolist())
# [[0.7620252966880798, 0.14078938961029053], [0.1358368694782257, 0.6013815999031067]]
```
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
Signed-off-by: wangli <858794774@qq.com>
Co-authored-by: wangli <858794774@qq.com>
### What this PR does / why we need it?
support pangu moe w8a8c8
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed with new added test.
Signed-off-by: zhuyilin <809721801@qq.com>
### What this PR does / why we need it?
Support prompt logprobs in V1. This also enable lm_eval to test accuracy
on V1
### Does this PR introduce _any_ user-facing change?
support prompt logprobs output
### How was this patch tested?
CI passed with accuracy test.
Using lm_eval, which use prompt logprobs as output to test accuracy, to
test:
```python
VLLM_USE_V1=1 lm_eval \
--model vllm \
--model_args pretrained=Qwen/Qwen2.5-7B-Instruct,max_model_len=4096,block_size=4 \
--tasks ceval-valid_computer_network \
--batch_size 8
```
After this pr, the accuracy test results of `Qwen/Qwen2.5-7B-Instruct`
on V1 is:
```bash
| Tasks |Version|Filter|n-shot| Metric | |Value | |Stderr|
|----------------------------|------:|------|-----:|--------|---|-----:|---|-----:|
|ceval-valid_computer_network| 2|none | 0|acc |↑ |0.7368|± |0.1038|
| | |none | 0|acc_norm|↑ |0.7368|± |0.1038|
```
Closes: https://github.com/vllm-project/vllm-ascend/issues/1043
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
Reset all unused positions in `NPUModelRunner` to prevent out-of-bounds
asserts in the `GatherV3` operator.
Currently, in
[`get_splitfuse_attn_mask`](https://github.com/vllm-project/vllm-ascend/blob/main/vllm_ascend/attention/attention.py#L124),
the `position` tensor may contain values that exceed the dimensions of
the attention mask, triggering a `GatherV3` boundary check failure.
These invalid indices originate from stale “dirty” entries left over in
`position` due to padding logic in the ACL graph. Specifically, in
[`_process_reqs`](https://github.com/vllm-project/vllm-ascend/blob/main/vllm_ascend/worker/model_runner_v1.py#L989),
the variable `num_input_tokens` is always greater than or equal to
`total_num_scheduled_tokens`, so any positions not explicitly cleared
from a previous batch will persist and cause this sporadic error.
BTW, in the original vLLM implementation, masks are constructed
internally using other args, so these lingering values do not surface.
However, on the Ascend platform—where split-fuse attention requires
externally supplied masks—these residual indices become critical and
lead to this elusive, hard-to-reproduce failure.
The fix is to explicitly reset or zero out all unused entries in the
`position` tensor before passing it to `GatherV3`, ensuring that every
index lies within the valid range of the attention mask.
Closes: https://github.com/vllm-project/vllm-ascend/issues/1038
### Does this PR introduce _any_ user-facing change?
No
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
This PR aims to clean up the useless code for LLM setup. It helps to
make the code more clear.
1. remove useless `self.xxx` property
2. change `set_random_seed` to `seed_everything`
3. remove `set_custom_all_reduce`, it's only used for cuda
This is just a code clean. no change for any code logic.
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
Sync MRotaryEmbedding interface change to recover main CI
(https://github.com/vllm-project/vllm/pull/19939)
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed
---------
Signed-off-by: wangli <wangli858794774@gmail.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>
### What this PR does / why we need it?
This PR implements the Eagle Pososer feature for vLLM v1, which enables
more efficient speculative decoding by using a draft model to predict
potential future tokens.
- The implementation includes the core Eagle algorithm integration with
vLLM's existing architecture, allowing for faster inference while
maintaining output quality.
- This is needed to significantly improve the generation speed of large
language models without compromising on the quality of generated text.
### Does this PR introduce any user-facing change?
Yes, this PR introduces a new speculative decoding mode that can be
enabled via configuration.
- Users can now choose to use Eagle Pososer by setting appropriate flags
in the inference configuration.
- The API remains backward compatible, with the new functionality being
opt-in.
### How was this patch tested?
CI passed with new unit tests added for the Eagle Pososer functionality.
- Benchmark tests were conducted comparing generation speed and quality
with and without Eagle Pososer.
- Integration tests were performed with various model architectures to
ensure compatibility.
- Manual testing was done using different prompt scenarios to verify
output quality remains consistent.
- we test accept rate on one Ascend 910B npu, The acceptance rate
results are basically consistent with those shown here:
https://github.com/vllm-project/vllm/pull/16937
- Currently, we support scenarios where num_spec_tokens <= 2. When
num_spec_tokens > 2, issues such as insufficient GPU memory and operator
computation errors may occur. We will address this in subsequent
updates.
- We will add support for Eagle v1 in future updates.
### Acceptance Test Script
```bash
SCRIPT="/offline/eagle.py"
DATASET="ShareGpt"
MODEL=Meta-Llama-3.1-8B-Instruct
DRAFT=EAGLE3-LLaMA3.1-Instruct-8B
CUDA_VISIBLE_DEVICES="0" VLLM_USE_V1=1 $PYTHON $SCRIPT \
--dataset $DATASET \
--num_spec_tokens 2 \
--max_num_seqs 1 \
--model_dir $MODEL \
--eagle_dir $DRAFT \
--tp 1 \
--num_prompts 80
```
### Acceptance Test Results
```bash
██████████████████████████████████████████████████████████████████████████████████████████████████████████| 80/80 [21:22<00:00, 16.03s/it, est. speed input: 4.72 toks/s, output: 13.56 toks/s]
-------------------------------------------------------------------------------------
mean acceptance length: 1.63
-------------------------------------------------------------------------------------
total_counts: 8062
acceptance at token 0: 1.00 (8062 times)
acceptance at token 1: 0.70 (5612 times)
acceptance at token 2: 0.47 (3765 times)
```
Closes: https://github.com/vllm-project/vllm-ascend/issues/1004
---------
Signed-off-by: yuancaoyaoHW <a2749322671@gmail.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?
This PR is used for resolved [issue
1147](https://github.com/vllm-project/vllm-ascend/issues/1147)
1. Move fused_moe code into one file `fused_moe.py`.
2. Integrate branch conditions into function `get_fused_moe_state`.
<!--
- 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 #
-->
### Does this PR introduce _any_ user-facing change?
1. This PR has removed the env `VLLM_ENABLE_MC2`, because I think this
env is useless, we can make judgments based on the current scenario
without this env, it will only increase complexity.
2. This PR has removed the env `USING_LCCL_COM`, because this env has
already expired.
3. `additional_config.expert_tensor_parallel_size` has already expired,
and now we also use parameter `enable_expert_parallel`, consistent with
the vLLM.
<!--
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.
-->
### 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.
-->
Signed-off-by: zzzzwwjj <1183291235@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>
### What this PR does / why we need it?
The former PR https://github.com/vllm-project/vllm-ascend/pull/736
select the valid token inside the `input_ids` and `position_ids` breaks
the necessary padding required by torchair. In this PR, we pending the
pad logic after the multimodal part.
Signed-off-by: ganyi <pleaplusone.gy@gmail.com>
1. Add `__init__.py` for vllm_ascend/compilation to make sure it's a
python module
2. Fix model runner bug to keep the same with vllm
3. Add release note for 0.9.0rc2
---------
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
The current vllm-ascend is not support the multimodal model in
vllm-ascend v1 yet. So I change the `model_runner_v1.py` file with using
MRoPE feature and so on to support this feature. It currently still not
perfect since the Ascend operator is not support the `window/full attn`
to reduce Memcpy operations, so it would out of memory if the input
embedding is too large, so We can't use `self._profile_multimodal()` for
profile since it use a big dummy input (i.e. images) as the multimodal
input.
Fixes: https://github.com/vllm-project/vllm-ascend/issues/514
### Does this PR introduce _any_ user-facing change?
No, this feature not need change the user-facing
### How was this patch tested?
I test this offline using my machine 910B3 and my own fork, and it works
well.
---------
Signed-off-by: cty <ctynb@qq.com>
### What this PR does / why we need it?
View optimization in torchair (defaulted to on for Transpose with any of
its axis being 1) prevents the weight Transpose to be fused with later
GroupedMatmul, which decrease the performance of MoE layer when expert
parallelism equals the total number of experts (e.g. EP256 for DSKv3).
Add an option to solve this problem by disabling the optimization.
### Does this PR introduce _any_ user-facing change?
Controlled by
`additional_config.torchair_graph_config.enable_view_optimize`,
defaulted to `True`.
### How was this patch tested?
Tested on 1x16 910 node, with tailored 2 layer DSKv2.
Signed-off-by: sdmyzlp <lrwei2@petalmail.com>
Fix the ascend config check logic:
1. refactor check_ascend_config to make it clear:
1. torchair graph should not work with enforce_eager=True
2. aclgraph should not work with torchair graph
3. add refresh config for rlhf case
4. fix a typo in model runner
5. change expert_tensor_parallel_size default to 0 to keep the same as
before
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
KV cache manger has been changed by
f8a1a2d108
This PR adapt the change into vllm-ascend to make ci happy
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
We need to **observe the time consumed in each stage of inference
(including pre-processing, model forward, etc.), without any performance
loss**.
Therefore, we use the event timestamp mechanism of the NPU to mark any
stage during the execution of the NPU device (this marking operation is
executed asynchronously, with no performance loss).
Additionally, we provide a blocking synchronization API
`pop_captured_sync` to be called at an appropriate time, to print the
time consumed in all observed stages.
**model_runner_v1.py file only changed 5 lines, all of which were
`ProfileExecuteDuration()` calls, and nothing else was changed， while
more changes were showed due to the alignment issue.**
### Does this PR introduce _any_ user-facing change?
Use env `VLLM_MODEL_EXECUTE_TIME_OBSERVE `to enable this feature
### How was this patch tested?
Tested in deepseek model，Print like this:
```
5691:(IntegratedWorker pid=1502285) Profile execute duration [Decode]: [post process]:14.17ms [prepare input and forward]:9.57ms [forward]:4.14ms
5695:(IntegratedWorker pid=1502285) Profile execute duration [Decode]: [post process]:14.29ms [prepare input and forward]:10.19ms [forward]:4.14ms
5697:(IntegratedWorker pid=1502343) Profile execute duration [Decode]: [post process]:14.81ms [prepare input and forward]:10.29ms [forward]:3.99ms
5701:(IntegratedWorker pid=1502343) Profile execute duration [Decode]: [post process]:14.10ms [prepare input and forward]:10.62ms [forward]:4.33ms
5705:(IntegratedWorker pid=1502343) Profile execute duration [Decode]: [post process]:14.65ms [prepare input and forward]:9.58ms [forward]:4.20ms
5709:(IntegratedWorker pid=1502343) Profile execute duration [Decode]: [post process]:14.43ms [prepare input and forward]:9.88ms [forward]:4.20ms
5711:(IntegratedWorker pid=1502401) Profile execute duration [Decode]: [post process]:14.89ms [prepare input and forward]:10.49ms [forward]:4.19ms
5715:(IntegratedWorker pid=1502401) Profile execute duration [Decode]: [post process]:14.14ms [prepare input and forward]:11.21ms [forward]:4.18ms
5719:(IntegratedWorker pid=1502401) Profile execute duration [Decode]: [post process]:14.71ms [prepare input and forward]:10.15ms [forward]:4.42ms
5723:(IntegratedWorker pid=1502401) Profile execute duration [Decode]: [post process]:14.62ms [prepare input and forward]:10.31ms [forward]:4.25ms
5725:(IntegratedWorker pid=1502462) Profile execute duration [Decode]: [post process]:14.12ms [prepare input and forward]:10.33ms [forward]:4.24ms
5729:(IntegratedWorker pid=1502462) Profile execute duration [Decode]: [post process]:14.58ms [prepare input and forward]:10.85ms [forward]:4.32ms
5733:(IntegratedWorker pid=1502462) Profile execute duration [Decode]: [post process]:14.32ms [prepare input and forward]:9.79ms [forward]:4.28ms
5737:(IntegratedWorker pid=1502462) Profile execute duration [Decode]: [post process]:15.06ms [prepare input and forward]:9.89ms [forward]:4.32ms
5739:(IntegratedWorker pid=1502524) Profile execute duration [Decode]: [post process]:14.62ms [prepare input and forward]:10.48ms [forward]:4.27ms
5743:(IntegratedWorker pid=1502524) Profile execute duration [Decode]: [post process]:14.60ms [prepare input and forward]:10.71ms [forward]:4.61ms
5747:(IntegratedWorker pid=1502524) Profile execute duration [Decode]: [post process]:14.21ms [prepare input and forward]:10.10ms [forward]:4.52ms
5751:(IntegratedWorker pid=1502524) Profile execute duration [Decode]: [post process]:15.03ms [prepare input and forward]:10.00ms [forward]:4.42ms
```
---------
Signed-off-by: depeng1994 <depengzhang@foxmail.com>
More and more config options are added to additional_config. This PR
provide a new AscendConfig to manage these config options by an easier
way to make code cleaner and readable.
This PR also added the `additional_config` doc for users.
Added the test_ascend_config.py to make sure the new AscendConfig works
as expect.
TODO: Add e2e test with torchair and deepseek once the CI resource is
available.
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
1. In previous PRs https://github.com/vllm-project/vllm-ascend/pull/580https://github.com/vllm-project/vllm-ascend/pull/784, I saved GPU memory
by promptly deleting unnecessary tensors. For tensors passed from
upper-layer functions, I used a list container to transfer the parameter
and then popped the tensor from the list within the inner function to
achieve deletion. Recently, I discovered a better implementation in
sglang—the `dispose_tensor` function and I recommend adopting this
approach.
2. Dispose `hidden_states` and `residual` from the previous layer once
they're no longer used.
3. Avoid to generate `self.inputs_embeds` in `ModelRunnerV1` in
non-multimodal scenarios.
With the aforementioned optimizations, using the DeepSeek-R1-W8A8 model
under the conditions of `TP=16` and `max-model-len=32768`, we can save
1.3GB of npu memory.
**Reference**: https://github.com/sgl-project/sglang/pull/6147
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
---------
Signed-off-by: ApsarasX <apsarax@outlook.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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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.
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Not change user facing.
### How was this patch tested?
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CI passed with new added/existing test.
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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>
### What this PR does / why we need it?
Add V1Engine LoRA support.
Add LoRA e2e test on single card and multiple cards.
### Does this PR introduce _any_ user-facing change?
support lora for V1
### How was this patch tested?
CI passed with new added test
---------
Signed-off-by: jesse <szxfml@gmail.com>
Signed-off-by: paulyu <paulyu0307@gmail.com>
Signed-off-by: paulyu12 <507435917@qq.com>
Co-authored-by: jesse <szxfml@gmail.com>
Co-authored-by: paulyu <paulyu0307@gmail.com>
