### What this PR does / why we need it?
Unify Model Usage via ModelScope
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed
Signed-off-by: hfadzxy <starmoon_zhang@163.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?
Add test for chunked prefill and prefix cache on v1/AscendScheduler
Covered scenarios:
- `Qwen/Qwen3-0.6B-Base` and `deepseek-ai/DeepSeek-V2-Lite-Chat` ---
multicard CI time increased by 19 min
- `V1 + default scheduler` vs `V1 + default scheduler + enable prefix
cache`
- `V1 + Ascend scheduler` vs `V1 + Ascend scheduler + enable prefix
cache` vs `V1 + Ascend scheduler + enable prefix cache + enable chunked
prefill`
- `Qwen/Qwen3-0.6B-Base` --- singlecard CI time increased by 8 min
- `V1 + Ascend scheduler` vs `V1 + Ascend scheduler + enable chunked
prefill`
should rebase after #1498 and #1446
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed with new added test.
Signed-off-by: MengqingCao <cmq0113@163.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>
Previous, the DeepSeek V3 Pruning weight is not correct, the moe layer
is not tested. We update a new Pruning model to enable moe layer
compute.
This PR fix the CI to address the new weight.
---------
Signed-off-by: wangxiyuan <wangxiyuan1007@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?
After #1094, decode might be executed with non-compiled mode, despite of
`torchair_graph_config.enabled`, causing multistream mla to fail, which
assumes torchair compiled mode for decode when
`torchair_graph_config.enabled == True`.
Augment that assumption to fix this.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Tested both offline, and by graph mode mla e2e testcase.
---------
Signed-off-by: sdmyzlp <lrwei2@petalmail.com>
### What this PR does / why we need it?
Add `max_num_tokens_across_dp` to AscendMetadata to fix dp
This pr fixes the bug introduced by
https://github.com/vllm-project/vllm-ascend/pull/1229, which add an arg
`max_num_tokens_across_dp` when dp_size > 1.
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
Use eager mode to run disaggregated prefill ci
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed with new existing test.
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
- Fix
[doctest](https://github.com/vllm-project/vllm-ascend/actions/workflows/vllm_ascend_doctest.yaml?query=event%3Aschedule)
- add system package installation
- Add doc for run doctests
- Cleanup all extra steps in .github/workflows/vllm_ascend_doctest.yaml
- Change schedule job from 4 ---> 12 hours
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- doctest CI passed
- Local test with
`/vllm-workspace/vllm-ascend/tests/e2e/run_doctests.sh`.
Signed-off-by: Yikun Jiang <yikunkero@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?
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>
This PR added the unit test framework to enable ut for vLLM Ascend. Unit
test runs on CPU machines. It'll be ran once lint check is passed the
same as e2e test.
For unit test, this PR created a new folder called `ut` under `tests`
module. All the test file in `ut` should keep the same with the code in
`vllm-ascend`. The file name should be start with `test_` prefix. For
example, in this PR. the `test_ascend_config.py` is added for
`ascend_config.py` test.
A new fille `worker/test_worker_v1.py` is also added as the placeholder.
This file should be the unit test for `vllm-ascend/worker/worker_v1.py`.
Additional, a new `fake_weight` folder is added, it contains the
config.json from `facebook/opt-125m`, so that the test will not always
visit huggingface.
TODO:
We should add all the unit test file one by one in the future.
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
Add quickstart doctest CI
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
- CI passed
- Run `/vllm-ascend/tests/e2e/run_doctests.sh`
Related: https://github.com/vllm-project/vllm-ascend/issues/725
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
