### What this PR does / why we need it?
cherry-pick #1675 to main
This PR adds validation checking to torchair_graph_config for better
reliability.
Co-authored-by: whx-sjtu <2952154980@qq.com>
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- vLLM version: v0.10.0
- vLLM main:
2836dd73f1
Signed-off-by: 22dimensions <waitingwind@foxmail.com>
### What this PR does / why we need it?
Cherry pick #1291 from v0.9.1-dev, This pr implement the synchronization
of whether `dbo` is enabled across all dp ranks. specifically, it
performed allreduce op across multiple DP ranks, only when all the dp
rank is `enable_dbo`, it is enabled
Co-authored-by: shikang-hangzhou <459956190@qq.com>
Co-authored-by: wangli <wangli858794774@gmail.com>
- vLLM version: v0.10.0
- vLLM main:
2836dd73f1
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
cherry-pick #1501 from 0.9.1-dev to main
Currently, Ray is not compatible with ACL Graph, so we need to fall back
to eager mode when using the Ray backend.
co-authored: Yizhou Liu <liu_yizhou@outlook.com>
- vLLM version: v0.10.0
- vLLM main:
2836dd73f1
Signed-off-by: 22dimensions <waitingwind@foxmail.com>
### What this PR does / why we need it?
Cherry pick #1705 from v0.9.1-dev
Compared qwen2_5_vl.py, qwen2_5_vl_without_padding.py missing some
funtions. The purpose of this PR is to supplement these.
add:
- rot_pos_emb(self, grid_thw: torch.Tensor)
- get_window_index(self, grid_thw)
- _process_image_input(self, image_input)
- _process_video_input(self, video_input)
Co-authored-by: zheliuyu
[15750543867@163.com](mailto:15750543867@163.com)
Co-authored-by: wangli
[wangli858794774@gmail.com](mailto:wangli858794774@gmail.com)
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.10.0
- vLLM main:
207b750e19
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
cherry pick #1749 from v0.9.1-dev
since the interface in vllm-ascend has changed so quickly, the
quantization function in mindie_turbo is no longer needed, so it needs
to be discarded.
Co-authored-by: zouyida [zouyida@huawei.com](mailto:zouyida@huawei.com)
Co-authored-by: wangli
[wangli858794774@gmail.com](mailto:wangli858794774@gmail.com)
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.10.0
- vLLM main:
207b750e19
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
Fixed 310p failure when using the sampler feature.
The root cause is: torch_npu.npu_top_k_top_p uses the operator
aclnnApplyTopKTopP, but aclnnApplyTopKTopP currently does not support
310P.
First PR that has the issue is #1308.
### Does this PR introduce _any_ user-facing change?
No
- vLLM version: v0.10.0
- vLLM main:
207b750e19
Signed-off-by: leo-pony <nengjunma@outlook.com>
This PR fixes the bug `local variable 'decode_hs_or_q_c' referenced
before assignment` when running chunked-prefill with torchair. We should
calculate `decode_hs_or_q_c` whether or not torchair graphics mode is
enabled.
backport of #1378
fix https://github.com/vllm-project/vllm-ascend/issues/1369
- vLLM version: v0.10.0
- vLLM main:
0e36abf993
---------
Signed-off-by: whx-sjtu <2952154980@qq.com>
Signed-off-by: MengqingCao <cmq0113@163.com>
Co-authored-by: whx-sjtu <2952154980@qq.com>
### What this PR does / why we need it?
Fix vLLM startup failure when inferring DeepSeek R1 model in DP
scenario.
When running vLLM inference for the DeepSeek R1 model in DP32+TP1
configuration, the vLLM service fails to start with the following error.
<img width="1786" height="918" alt="21b2011042d4f77f36f5243fa64d9c18"
src="https://github.com/user-attachments/assets/df1963fe-587e-43ca-822e-a9094d0034fb"
/>
The root cause is a missing else branch after [this line of
code](d629f0b2b5/vllm_ascend/ops/fused_moe.py (L1411)).
This PR fixes the issue.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed with new added/existing test.
- vLLM version: v0.10.0
- vLLM main:
5bbaf492a6
---------
Signed-off-by: zhanghaiwen <zhanghaiwen@cmss.chinamobile.com>
Co-authored-by: zhanghaiwen <zhanghaiwen@cmss.chinamobile.com>
bugfix cherry-pick from v0.9.1-dev
https://github.com/vllm-project/vllm-ascend/pull/2007
### What this PR does / why we need it?
Minimum reproducing code：
```python
# test.py
from vllm import LLM, SamplingParams
prompts = [
"Hello, my name is",
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="Qwen2.5-VL-7B-Instruct", max_model_len=26240)
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}")
```
```bash
export USE_OPTIMIZED_MODEL=0
python test.py
```
exception as follow:
```
[rank0]: File "/home/xxx/vllm_ascend/models/qwen2_5_vl_without_padding.py", line 84, in forward
[rank0]: q = torch_npu.npu_rotary_mul(q, cos, sin)
[rank0]: File "/home/anaconda3/envs/xxx/lib/python3.10/site-packages/torch/_ops.py", line 1116, in __call__
[rank0]: return self._op(*args, **(kwargs or {}))
[rank0]: RuntimeError: Expected all tensors to be on the same device, but found at least two devices, npu:0 and cpu! (when checking argument for argument r1 in method wrapper__npu_rotary_mul)
```
In `AscendQwen2_5_VisionAttention_Without_Padding`,
`torch_npu.npu_rotary_mul(q, cos, sin)`， `cos`/`sin` on cpu, but `q` on
npu, so there will be an error.
`qwen2_5_vl_without_padding.py` need this bugfix, because
`AscendQwen2_5_VisionTransformer_Without_Padding.rot_pos_emb` in
wen2_5_vl_without_padding.py is from vllm and `inv_freq` will create on
cpu.
40d86ee412/vllm/model_executor/models/qwen2_5_vl.py (L482)
```python
inv_freq = 1.0 / (theta**(torch.arange(0, dim, 2, dtype=torch.float, device='cpu') / dim))
```
`qwen2_5_vl.py` do not need, because
`AscendQwen2_5_VisionRotaryEmbedding` in qwen2_5_vl.py rewrite
`AscendQwen2_5_VisionRotaryEmbedding` and `inv_freq` will create on
device.
```python
inv_freq = 1.0 / (theta**(torch.arange(0, dim, 2, dtype=torch.float) / dim))
```
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
CI passed with new added/existing test.
- vLLM version: v0.10.0
- vLLM main:
18cc33dd60
Signed-off-by: pjgao <gaopengju3@huawei.com>
Co-authored-by: pjgao <gaopengju3@huawei.com>
### What this PR does / why we need it?
Fix#2033
Sync https://github.com/vllm-project/vllm/pull/14702 to solve
`grammar_bitmask` IndexError caused by outdated `apply_grammar_bitmask`
method
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Tested by upstream vllm
- vLLM version: v0.10.0
- vLLM main:
6e599eebe8
Signed-off-by: ApsarasX <apsarax@outlook.com>
### What this PR does / why we need it?
Adding `W4A8_DYNAMIC` quantization support for linear.
Dense models like Qwen3 can infer with `W4A8_DYNAMIC` quantization.
### Does this PR introduce _any_ user-facing change?
None
### How was this patch tested?
Adding ut case in `tests/ut/quantization/test_w4a8_dynamic.py`
Adding e2e case in
`tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_Qwen3_W4A8DYNAMIC`
to test qwen3 w4a8_dynamic quantized model
Note the w4a8_dynamic quantized model is quantized by `msit/msmodelslim`
of commit `d0abb0a47e1f1a473b866ad41b737fbc28fb1409`
1. Generate `W4A8_DYNAMIC` quantization weights using `msmodelslim`
```shell
git clone https://gitee.com/ascend/msit.git
cd msit/msmodelslim
git checkout d0abb0a47e1f1a473b866ad41b737fbc28fb1409
bash install.sh
```
2. Serve model using `vllm`
```shell
VLLM_USE_V1=1 python -m vllm.entrypoints.openai.api_server \
--model vllm-ascend/Qwen3-8B-W4A8 \
--port 8000 \
--quantization ascend \
--tensor_parallel_size 2 \
--enforce-eager
```
- vLLM version: v0.10.0
- vLLM main:
4cd7fe6cea
---------
Signed-off-by: ZhouXiang <zhouxiang100@huawei.com>
### What this PR does / why we need it?
This PR fixes a tensor shape mismatch in `add_lora_logits`.
Previously, `lora_a_stacked` was passed as shape `[num_loras, in_dim,
rank]`, which does not match the expected einsum pattern `"bi, boi ->
bo"` used in `bgmv_shrink`.
This causes runtime errors like:
RuntimeError: einsum(): subscript i has size 3 for operand 1 which does
not broadcast with previously seen size 4
This fix transposes `lora_a_stacked` and `lora_b_stacked` to match the
expected shapes:
- `lora_a`: `[num_loras, rank, in_dim]`
- `lora_b`: `[num_loras, out_dim, rank]`
All unit tests pass after this fix.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
```
import torch
import pytest
from unittest.mock import patch, PropertyMock, ANY
from vllm_ascend.lora.punica_wrapper.punica_npu import PunicaWrapperNPU
@pytest.fixture
def wrapper_cpu():
cfg = {"max_num_batched_tokens": 10, "max_batches": 2, "device": "cpu"}
w = PunicaWrapperNPU(**cfg)
w.is_prefill = True
w.no_lora = False
return w
def test_add_lora_logits(wrapper_cpu):
batch_size = 2
hidden_size = 4
lora_rank = 3
vocab_size = 5
y = torch.zeros(batch_size, vocab_size)
x = torch.randn(batch_size, hidden_size)
num_loras = 1
lora_a = torch.randn(num_loras, hidden_size, lora_rank)
lora_b = torch.randn(num_loras, lora_rank, vocab_size)
with patch.object(wrapper_cpu.__class__, "sampler_indices",
new_callable=PropertyMock) as mock_idx:
mock_idx.return_value = torch.zeros(batch_size, dtype=torch.long)
wrapper_cpu.add_lora_logits(y, x, lora_a, lora_b, scale=1.0)
assert y.shape == (batch_size, vocab_size)
assert not torch.allclose(y, torch.zeros_like(y))
Signed-off-by: hongfugui <hongfugui_yewu@cmss.chinamobile.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>
This PR designs the shared expert multi-stream parallelism of
w8a8-dynamic-quantized MoE stage in more detail to achieve better
performance.
- vLLM version: v0.10.0
- vLLM main:
2cc571199b
Signed-off-by: whx-sjtu <2952154980@qq.com>
### What this PR does / why we need it?
Add two custom kernels(bgmv_shrink and bgmv expand) to solve the
performance of LoRA
### Does this PR introduce _any_ user-facing change?
no user-facing change
### How was this patch tested?
we add Unit Test file to test the custom ascendc kernel. See
vllm-ascend/tests/e2e/singlecard/ops/test_bgmv_expand.py and
vllm-ascend/tests/e2e/singlecard/ops/test_bgmv_expand.py
Based on the actual test of the QWen2.5 7B model using vllm-ascend
version v0.9.2.rc1, the TTFT, TPOT and throughput have increased by
about 70%.
- vLLM version: v0.9.2
- vLLM main:
40d86ee412
---------
Signed-off-by: taoxudonghaha <justsheldon@163.com>
Support the inference of the Deepseekr1-w8a8-mtp model with
statically-quantized shared_head in MTP layers.
- vLLM version: v0.9.2
- vLLM main:
6eca337ce0
Signed-off-by: curryliu <120010041@link.cuhk.edu.cn>
This PR removes the restriction that TP cannot be greater than 4 in
torchair scenario, because current newest version of CANN has fixed this
bug.
- vLLM version: v0.10.0
- vLLM main:
04ff4be310
Signed-off-by: whx-sjtu <2952154980@qq.com>
Clean up useless import from vllm to make code more clear.
- vLLM version: v0.10.0
- vLLM main:
18cc33dd60
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?
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?
Fix num_hidden_layers when Qwen2-Audio 7B and #1760 ：
```
INFO 07-15 04:38:53 [platform.py:174] PIECEWISE compilation enabled on NPU. use_inductor not supported - using only ACL Graph mode
Traceback (most recent call last):
File "/workspace/test1.py", line 58, in <module>
main(audio_count)
File "/workspace/test1.py", line 38, in main
llm = LLM(model="Qwen/Qwen2-Audio-7B-Instruct",
File "/vllm-workspace/vllm/vllm/entrypoints/llm.py", line 271, in __init__
self.llm_engine = LLMEngine.from_engine_args(
File "/vllm-workspace/vllm/vllm/engine/llm_engine.py", line 494, in from_engine_args
vllm_config = engine_args.create_engine_config(usage_context)
File "/vllm-workspace/vllm/vllm/engine/arg_utils.py", line 1286, in create_engine_config
config = VllmConfig(
File "/usr/local/python3.10.17/lib/python3.10/site-packages/pydantic/_internal/_dataclasses.py", line 123, in __init__
s.__pydantic_validator__.validate_python(ArgsKwargs(args, kwargs), self_instance=s)
File "/vllm-workspace/vllm/vllm/config.py", line 4624, in __post_init__
current_platform.check_and_update_config(self)
File "/vllm-workspace/vllm-ascend/vllm_ascend/platform.py", line 180, in check_and_update_config
update_aclgraph_sizes(vllm_config)
File "/vllm-workspace/vllm-ascend/vllm_ascend/utils.py", line 307, in update_aclgraph_sizes
num_hidden_layers = vllm_config.model_config.hf_config.num_hidden_layers
File "/usr/local/python3.10.17/lib/python3.10/site-packages/transformers/configuration_utils.py", line 211, in __getattribute__
return super().__getattribute__(key)
AttributeError: 'Qwen2AudioConfig' object has no attribute 'num_hidden_layers'
```
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
Closes: https://github.com/vllm-project/vllm-ascend/issues/1780https://github.com/vllm-project/vllm-ascend/issues/1760https://github.com/vllm-project/vllm-ascend/issues/1276https://github.com/vllm-project/vllm-ascend/issues/359
- vLLM version: v0.10.0
- vLLM main:
7728dd77bb
Signed-off-by: hfadzxy <starmoon_zhang@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>
Signed-off-by: wuzhongjian <wuzhongjian_yewu@cmss.chinamobile.com>
### What this PR does / why we need it?
Fix duplicate 'torch.' prefix in qwen2-vl, qwen2.5-vl
- vLLM version: v0.9.2
- vLLM main:
dde295a934
### What this PR does / why we need it?
Refactor the comments of `AscendMetaData` to make it clearer.
- vLLM version: v0.9.2
- vLLM main:
f3137cdd81
---------
Signed-off-by: shen-shanshan <467638484@qq.com>
Signed-off-by: wuzhongjian <wuzhongjian_yewu@cmss.chinamobile.com>
### What this PR does / why we need it?
Add prefix parameter to parent class initialization to avoid parameter
naming conflicts
### Does this PR introduce _any_ user-facing change?
No
- vLLM version: v0.9.2
- vLLM main:
32142b3c62
Before do attention module refactor, we can do some code cleanup to make
the next step easier.
What this PR does:
1. remove uesless `common_prefix_len` for attention builder
2. remove uesless `is_only_prefill` and `num_input_tokens` in attention
metadata.
3. remove `CommonAttentionMetadata` and ues `query_start_loc` instead,
`CommonAttentionMetadata` is over designed and uesless
4. update the attention backend input parameters to keep the same as
vLLM.
5. Rename attention name to the same style with `ASCEND` prefix
- vLLM version: v0.9.2
- vLLM main:
107111a859
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
Support pipeline parallel with ray backend in V1Engine.
Fixes#1751
### Does this PR introduce _any_ user-facing change?
Users could specify ray as distributed backend when inferencing with pp
### How was this patch tested?
CI passed with new added test.
- vLLM version: v0.9.2
- vLLM main:
32142b3c62
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
Optimizes the performance of the Qwen3 quantization model by registering
a custom model and adding the AddRmsNormQuant operation. Subsequent PRs
will focus on performance optimizations based on this custom model.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
CI passed with existing test.
- vLLM version: v0.9.2
- vLLM main:
8d0a01a5f2
Signed-off-by: rjg-lyh <1318825571@qq.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
2. Refactor utils (this PR)
- simple change that move all torchair related util function to torchair
module
3. Refactor model_runner
4. Refactor attention
- vLLM version: v0.9.2
- vLLM main:
8188196a1c
Signed-off-by: wangxiyuan <wangxiyuan1007@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>
vLLM commit
752c6ade2e
removed `blocksparse_params` for attention backend. This PR does the
same change to make CI happy.
- vLLM version: v0.9.2
- vLLM main:
9499e26e2a
---------
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
Co-authored-by: Yikun Jiang <yikunkero@gmail.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?
Before patch, we can see
`vllm_ascend.models.deepseek_v2:CustomDeepseekV3ForCausalLM`, it seems
not friendly format.
```
WARNING 07-14 23:57:34 [registry.py:413] Model architecture DeepseekV2ForCausalLM is already registered, and will be overwritten by the new model class vllm_ascend.models.deepseek_v2:CustomDeepseekV2ForCausalLM.
WARNING 07-14 23:57:34 [registry.py:413] Model architecture DeepseekV3ForCausalLM is already registered, and will be overwritten by the new model class vllm_ascend.models.deepseek_v2:CustomDeepseekV3ForCausalLM.
WARNING 07-14 23:57:34 [registry.py:413] Model architecture Qwen3MoeForCausalLM is already registered, and will be overwritten by the new model class vllm_ascend.models.qwen3_moe:CustomQwen3MoeForCausalLM.
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Local Test.
- vLLM version: v0.9.2
- vLLM main:
bcdfb2a330
Signed-off-by: xleoken <xleoken@163.com>
### What this PR does / why we need it?
maybe fixes
[#1728](https://github.com/vllm-project/vllm-ascend/issues/1728#issuecomment-3065083433)
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Test Qwen3-32B tp=4 with:
```bash
vllm serve --port 1234 Qwen/Qwen3-32B \
--served-model-name Qwen3-32B \
--tensor-parallel-size 4 \
--swap-space 16 \
--max-model-len 6000 \
--load-format dummy \
--disable-log-stats \
--disable-log-requests \
```
Request batch_size=128 input/output token=1024
**In 0.9.2rc1**
```text
=====================================================
Total TPS with prefill(tokens/s) : 785.1395
Total TPS without prefill : 846.6809
Mean TPS with prefill : 6.1339
Mean TPS without prefill : 6.6147
=====================================================
Mean TTFT(ms) : 10307.8123
Max TTFT(ms) : 21423.0733
Min TTFT(ms) : 362.3602
=====================================================
Mean TPOT(ms) : 151.3051
Max TPOT(ms) : 159.4649
Min TPOT(ms) : 140.899
=====================================================
Total Time(s) : 175.6032
Request Throughput(requests/s) : 0.7289
=====================================================
```
**Apply this PR**
```text
=====================================================
Total TPS with prefill(tokens/s) : 811.0014
Total TPS without prefill : 876.4423
Mean TPS with prefill : 6.3359
Mean TPS without prefill : 6.8472
=====================================================
Mean TTFT(ms) : 10263.8382
Max TTFT(ms) : 21151.2547
Min TTFT(ms) : 375.9136
=====================================================
Mean TPOT(ms) : 146.1686
Max TPOT(ms) : 154.0957
Min TPOT(ms) : 136.8879
=====================================================
Total Time(s) : 169.8579
Request Throughput(requests/s) : 0.7536
=====================================================
```
The TPOT performance gap between these two sets of data is about 3%.
- vLLM version: v0.9.2
- vLLM main:
8dfb45ca33
Signed-off-by: lianyibo <lianyibo1@kunlunit.com>
### What this PR does / why we need it?
We'll refator `CustomOp` in vllm-ascend from this pr on.
Use function `CustomOp.register_oot` to achieve the customop registery,
taking `AscendQuickGELU` as an example:
```python
from vllm_ascend.ops.activation import AscendQuickGELU
CustomOp.register_oot(_decorated_op_cls=AscendQuickGELU, name="QuickGELU")
```
This is a quick adapt for `CustomOp.register_oot` mechanism from vllm
0.9.2. For further step, we can remove inherit from `QuickGELU` can
write our own `QuickGELU` at all.
Part of https://github.com/vllm-project/vllm-ascend/pull/1647
- vLLM version: v0.9.2
- vLLM main:
8dfb45ca33
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
