There is lot of torchair code in model runner leading the code hard for
maintenance. We'll create new torchair_model_runner to split torchair
related logic. Following the workflow #2203, this is the first PR.
What this PR does:
create the new torchair model runner, more function will be added later
- vLLM version: v0.10.0
- vLLM main:
586f286789
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
we recently added disaggregated_prefill and ascend_forward_context
feature by
ba3dfbd59e
and
df0ec55162.
This PR fix some nit introduced by them to make the code clear.
1. drop `current_platform` usage. It'll lead unknown circular import
error in some case
2. update `set_ascend_forward_context` function to make the logic clear.
for example, remove V0 support in this function.
3. Remove useless `self.local_rank_across_dp` in worker
4. Remove `soc_info.py` to use `get_ascend_soc_version` instead.
- vLLM version: v0.10.0
- vLLM main:
02f82fe438
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
This pr fix broken CI:
1. Fix the
ee2eb6ecd8
changes, in this commit, they fused the gate and up projections in the
vision MLP, This can improve performance by reducing one matrix
multiplication. so, this pr do the following things:
- Specify that the two linear layers are fused as `mlp.gate_up_proj`
when loading the weights.
- Use a SiluAndMul activation function.
2. Fix
aefeea0fde,
Update ModelRunnerOutput parameters to adapt to its changes
3. Fix
[vllm-commit](https://github.com/vllm-project/vllm/pull/20815/files#diff-3ffb829a39ab2b3e4706aa28f5e476815f36c3a87b98d6a66514ebedc8f3ffb4R354-R356),
fix qwen moe
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.10.0
- vLLM main:
fed5849d3f
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
We notice that vllm's main branch merged the PR
https://github.com/vllm-project/vllm/pull/21072 and
https://github.com/vllm-project/vllm/pull/21473 to support ray backend
and fix some rebase bug from previous change. Those changes makes the
disaggregate pd in vllm ascend breaks in some scenario.
In this PR, we adopt those changes to make sure the
`llmdatddist_c_mgr_connector` works fine on the newest vllm main branch.
### Does this PR introduce _any_ user-facing change?
No user face change.
### How was this patch tested?
relevant ut will be added to make sure the functionality of those
changes.
- vLLM version: v0.10.0
- vLLM main:
ad57f23f6a
---------
Signed-off-by: ganyi <pleaplusone.gy@gmail.com>
### What this PR does / why we need it?
A refactoring of forward_context and model_runner_v1, add some context
which is necessary in model inference into forward_context, and refactor
dummy_run logic, make it more reasonable.
Some details for this PR:
Add `ascend_forward_context`;
Update mc2_v2 op, and support `active_mask` param;
Update scripts in examples dir;
refactor `dummy_run` logic;
Add soc_version for A2 and A3;
### Does this PR introduce _any_ user-facing change?
No change at user-facing.
### How was this patch tested?
- vLLM version: v0.10.0
- vLLM main:
57c22e57f9
Signed-off-by: zzzzwwjj <1183291235@qq.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>
There is a lot torchair specified logic in common code. It results hard
code maintenance. We will create a new torchair module to launch
torchair related logic there. I plan to add 4 PR.
1. Refactor worker (this PR)
- create torchair module and move torchair related code in worker to the
new module
3. Refactor utils
4. Refactor model_runner
5. Refactor attention
- vLLM version: v0.9.2
- vLLM main:
8188196a1c
Signed-off-by: wangxiyuan <wangxiyuan1007@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>
1. vLLM commit
45badd05d0
changed the pooling check logic which broken vLLM Ascend.
2. vLLM commit
3e04107d97
requires higher version of transformers. The transformers version bug
has been fixed by
e936e401de.
We can safe to remove the version limit now.
3. vLLM commit
217937221b
added a new input `enable_eplb` for FusedMoe Ops
This PR fix the broken CI.
- vLLM version: v0.9.2
- vLLM main:
6a971ed692
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### 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?
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>
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>
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>
<!-- 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`.
<!--
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section is to outline the changes and how this PR fixes the issue.
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- Please clarify why the changes are needed. For instance, the use case
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- Fixes #
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### 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.
<!--
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as API, interface or other behavior changes.
Documentation-only updates are not considered user-facing changes.
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### 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>
### 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?
When profiling, it is often necessary to disable the call stack to
reduce profiling overhead, and adjust the profiler_level to level1 to
obtain more detailed operator and communication information.
Therefore, it is recommended to modify the default profiling
configuration.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
No
Signed-off-by: ApsarasX <apsarax@outlook.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?
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>
### What this PR does / why we need it?
Fix update_aclgraph_sizes when running MoE models.
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.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>
<!-- 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?
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This PR supports the access of vllm-acend to the piecewise_graph feature
provided by the v1 engine.
1. register unifiled_ascend_attention_with_output for piecewise_graph to
split graph.
2. support NPUGraph to accelerate kernel launch.
### 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.
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support npugraph to default， Users can disenable the npugraph feature by
configuring enforce_eager.
This has corresponding requirements for the versions of torch_npu and
CANN, and they need to support graph capture.
### 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
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the future.
If tests were not added, please describe why they were not added and/or
why it was difficult to add.
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it turn to default
---------
Signed-off-by: Bug Hunter Yan <yanpq@zju.edu.cn>
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
Co-authored-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
This PR adds sleep mode feature for vllm-ascend, when sleeps, we do
mainly two things:
- offload model weights
- discard kv cache
RLHF tools(such as https://github.com/volcengine/verl and
https://github.com/OpenRLHF/OpenRLHF) have a strong need of sleep mode
to accelerate the training process.
This PR may solve #375 and #320 .
### Does this PR introduce _any_ user-facing change?
No existing user interfaces changed.
Users will have two new methods(`sleep()` and `wake_up()`) to use.
### How was this patch tested?
This PR is tested with Qwen/Qwen2.5-0.5B-Instruct.
At first, we have free NPU memory M1.
After `llm = LLM("Qwen/Qwen2.5-0.5B-Instruct", enable_sleep_mode=True)`
executed, we have free NPU memory M2. M2 < M1.
Then we call `llm.sleep(level=1)`, we have free NPU memory M3.
We have M3 > M2, M3 is very close to M1.
Plus, we have the same output tokens before sleep and after wake up,
with the config of `SamplingParams(temperature=0, max_tokens=10)` and
with the same input tokens of course.
This PR is utilizing the CMake procedure of #371 , thanks a lot.
Signed-off-by: Shuqiao Li <celestialli@outlook.com>
1. Add `vllm_version_is` function to check vllm version.
2. `ensure_kv_transfer_initialized` and `get_kv_transfer_group ` have
been moved to other place in vllm main branch via
3408e47159
, this patch fix the import error.
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
This PR adds AscendScheduler to vllm v1 engine.
This scheduler currently supports v0-style prefill-first scheduling
strategy.
In the future more schedule methods will be supported by this scheduler.
---------
Signed-off-by: hw_whx <wanghexiang7@huawei.com>
Co-authored-by: hw_whx <wanghexiang7@huawei.com>
This PR added patch module for vllm
1. platform patch: the patch will be registered when load the platform
2. worker patch: the patch will be registered when worker is started.
The detail is:
1. patch_common: patch for main and 0.8.4 version
4. patch_main: patch for main verison
5. patch_0_8_4: patch for 0.8.4 version
### What this PR does / why we need it?
Adapt Disaggregated Prefill feature onto Ascend device
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
The test usage has been provided alongwith the PR, in
examples/offline_disaggregated_prefill_npu.py
To run it, do this
```
export PROMPT_DEVICE_ID=0,1
export DECODE_DEVICE_ID=2,3
python examples/offline_disaggregated_prefill_npu.py
```
---------
Signed-off-by: ZihuiQian <qianzihui@huawei.com>
Co-authored-by: ZihuiQian <qianzihui@huawei.com>
1. Doc: Fix error link
2. Doc: make Chinese version the same with english
3. remove useless file `test.py`
4. update `collect_env.py`
5. Fix v1 import error
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
Add support for V1 Engine.
Please note that this is just the initial version, and there may be some
places need to be fixed or optimized in the future, feel free to leave
some comments to us.
### Does this PR introduce _any_ user-facing change?
To use V1 Engine on NPU device, you need to set the env variable shown
below:
```bash
export VLLM_USE_V1=1
export VLLM_WORKER_MULTIPROC_METHOD=spawn
```
If you are using vllm for offline inferencing, you must add a `__main__`
guard like:
```bash
if __name__ == '__main__':
llm = vllm.LLM(...)
```
Find more details
[here](https://docs.vllm.ai/en/latest/getting_started/troubleshooting.html#python-multiprocessing).
### How was this patch tested?
I have tested the online serving with `Qwen2.5-7B-Instruct` using this
command:
```bash
vllm serve Qwen/Qwen2.5-7B-Instruct --max_model_len 26240
```
Query the model with input prompts:
```bash
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen/Qwen2.5-7B-Instruct",
"prompt": "The future of AI is",
"max_tokens": 7,
"temperature": 0
}'
```
---------
Signed-off-by: shen-shanshan <467638484@qq.com>
Co-authored-by: didongli182 <didongli@huawei.com>
