### 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>
### What this PR does / why we need it?
Fix the bug in torch 2.5.1 that raising segment fault when enable
`pin_memory` while creating a tensor using `torch.tensor`.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
---------
Signed-off-by: shen-shanshan <467638484@qq.com>
### What this PR does / why we need it?
- Adds support for passing prompt_embeds to LLM.generate as
```bash
llm.generate({"prompt_embeds": input_embeds}, sampling_params)
```
or
```bash
llm.generate(
[{"prompt_embeds": input_embeds} for input_embeds in inputs_embeds], sampling_params
)
```
- Add `prompt_embeds` to examples
### How was this patch tested?
CI passed with new added/existing test.
and I have test with the example script in this pr, and the output seems
looks good:
```bash
[Single Inference Output]
------------------------------
The capital of France is Paris. Paris is the largest city in France and is
------------------------------
Adding requests: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 3966.87it/s]
Processed prompts: 100%|█████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 3.99it/s, est. speed input: 177.08 toks/s, output: 63.91 toks/s]
[Batch Inference Outputs]
------------------------------
Q1: Please tell me about the capital of France.
A1: The capital of France is Paris. It is located in the northern part of the
Q2: When is the day longest during the year?
A2: The day is longest during the year at the summer solstice. This typically occurs
Q3: Where is bigger, the moon or the sun?
A3: The sun is significantly bigger than the moon.
The sun has a diameter of
------------------------------
```
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
Add `with_prefill_across_dp` to AscendMetadata to fix dp
This pr fixes the bug introduced by #1012, which add an arg
`with_prefill_across_dp` when dp_size > 1.
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it remove cumsum operator in MOE to improve performance
### How was this patch tested?
it should be tested on a case with mc2 operator and graph mode enabled
Signed-off-by: zhky <hahazhky@163.com>
Co-authored-by: 洪炜杰 <hongweijie1@huawei.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>
### What this PR does / why we need it?
Support MOE inner Multi-stream for Deepseek.
This feature requires graph mode with mc2 enabled.
---------
Signed-off-by: David9857 <985700846@qq.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>
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?
When I run vllm-ascend, I get this error msg:
```bash
Traceback (most recent call last):
File "/home/sss/software/miniconda3/envs/vllm-v1/bin/vllm", line 8, in <module>
sys.exit(main())
File "/home/sss/github/vllm-project/vllm/vllm/entrypoints/cli/main.py", line 50, in main
cmd.subparser_init(subparsers).set_defaults(
File "/home/sss/github/vllm-project/vllm/vllm/entrypoints/cli/serve.py", line 101, in subparser_init
serve_parser = make_arg_parser(serve_parser)
File "/home/sss/github/vllm-project/vllm/vllm/entrypoints/openai/cli_args.py", line 254, in make_arg_parser
parser = AsyncEngineArgs.add_cli_args(parser)
File "/home/sss/github/vllm-project/vllm/vllm/engine/arg_utils.py", line 1582, in add_cli_args
current_platform.pre_register_and_update(parser)
File "/home/sss/github/vllm-project/vllm-ascend/vllm_ascend/platform.py", line 80, in pre_register_and_update
if ASCEND_QUATIZATION_METHOD not in quant_action.choices:
TypeError: argument of type 'NoneType' is not iterable
[ERROR] 2025-06-03-02:53:42 (PID:6005, Device:-1, RankID:-1) ERR99999 UNKNOWN applicaiton exception
```
This is because the `choices` attribute in `quant_action` can be `None`
and we don't check it.
```bash
# quant_action
_StoreAction(option_strings=['--quantization', '-q'], dest='quantization', nargs=None, const=None, default=None, type=<class 'str'>, choices=None, required=False, help='Method used to quantize the weights. If `None`, we first check the\n`quantization_config` attribute in the model config file. If that is\n`None`, we assume the model weights are not quantized and use `dtype` to\ndetermine the data type of the weights.', metavar=None)
```
Thus, I have added check for the `choices` to handle the scenario of
`choices=None`.
### Does this PR introduce _any_ user-facing change?
yes, vllm server with ascend quantization works now.
### How was this patch tested?
by `vllm server --quantization ascend` command.
Related: https://github.com/vllm-project/vllm/issues/19004
Signed-off-by: shen-shanshan <467638484@qq.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
Remove legacy input mapper/processor from V0.
Find more details at
https://github.com/vllm-project/vllm-ascend/issues/673 and
https://github.com/vllm-project/vllm/pull/15686.
### Does this PR introduce _any_ user-facing change?
no.
### How was this patch tested?
Launch online service:
```bash
vllm serve Qwen/Qwen2.5-VL-7B-Instruct \
--dtype bfloat16 \
--max_model_len 32768 \
--max-num-batched-tokens 32768
```
Query the server:
```bash
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen/Qwen2.5-VL-7B-Instruct",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": [
{"type": "image_url", "image_url": {"url": "https://modelscope.oss-cn-beijing.aliyuncs.com/resource/qwen.png"}},
{"type": "text", "text": "What is the text in the illustrate?"}
]}
]
}'
```
Result:
```bash
{"id":"chatcmpl-619e70733ed148b3be3a0b6524ee0ef3","object":"chat.completion","created":1748226332,"model":"/home/sss/.cache/modelscope/hub/models/Qwen/Qwen2___5-VL-7B-Instruct","choices":[{"index":0,"message":{"role":"assistant","reasoning_content":null,"content":"The text in the illustration reads \"TONGYI Qwen.\"","tool_calls":[]},"logprobs":null,"finish_reason":"stop","stop_reason":null}],"usage":{"pro
```
Signed-off-by: shen-shanshan <467638484@qq.com>
Co-authored-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?
the interface of qwen2.5vl changes from column linear to qkv linear,
this makes our weight pad func become abnormal, thus we optimize
split_qkv func to fix this bug.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
with CI
Signed-off-by: zouyida2052 <zouyida2002@gmail.com>
### What this PR does / why we need it?
1. Implentment `NPUPiecewiseBackend` to enable aclgraph
2. Eable aclgraph by default in V1, but raise error when running
deepseek and raise warning when running models except for qwen
### How was this patch tested?
CI pass with the new ut
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
Fix deepseek with v1, this error is introdeced by
https://github.com/vllm-project/vllm-ascend/pull/945. and this pr fix
the block table of mla
### How was this patch tested?
CI passed with new addedtest.
Signed-off-by: Mengqing Cao <cmq0113@163.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>
<!-- 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 fix accuracy issues incurred by codes that adapt to
`FusedMoEParallelConfig` in vLLM 0.9.0 version. The `tp_size` used to
split weights are wrongly passed. The root cause is that vLLM community
and vLLM-Ascend are using different methods to decide whether to use
Expert Parallel.
vLLM:
vLLM use a flag `enable_expert_parallel` to indicate whether to use EP
and use the following codes to decide `ep_size`:
```
use_ep = (dp_size_ * tp_size_ > 1
and vllm_parallel_config.enable_expert_parallel)
dp_size = dp_size_
dp_rank = get_dp_group().rank_in_group if dp_size > 1 else 0
tp_size, tp_rank = flatten_tp_across_dp(dp_rank)
if not use_ep:
return FusedMoEParallelConfig(tp_size=tp_size,
tp_rank=tp_rank,
dp_size=dp_size,
dp_rank=dp_rank,
ep_size=1,
ep_rank=0,
use_ep=False)
# DP + EP / TP + EP / DP + TP + EP
assert use_ep
# In EP, each device owns a set of experts fully. There is no tensor
# parallel update tp_size, tp_rank, ep_size and ep_rank to reflect that.
ep_size = tp_size
ep_rank = tp_rank
return FusedMoEParallelConfig(tp_size=1,
tp_rank=0,
dp_size=dp_size,
dp_rank=dp_rank,
ep_size=ep_size,
ep_rank=ep_rank,
use_ep=True)
```
vLLM-Ascend:
vLLM-Ascend uses `etp` to specify Tensor Parallel in MoE.
```
self.ep_size = get_ep_group().world_size
self.tp_size = get_etp_group().world_size
self.dp_size = (dp_size if dp_size is not None else
get_dp_group().world_size)
```
So there will be conflicts if we simply combine these codes together.
### 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.
-->
### 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: angazenn <zengyanjia@huawei.com>
Co-authored-by: angazenn <zengyanjia@huawei.com>
### What this PR does / why we need it?
This PR fixes two accuracy bugs incurred by PR #819 when running
deepseekv3 series models:
1. #819 adds `all_to_all` communication in quantized cases, but
`all_gather` && `reduce_scatter` are removed in both of quantized and
unquantized cases. When running unquantized deepseekv3 models with
`ep_size == world_size`, the moe modules fail to communicate. Therefore,
this PR adds `all_to_all` communication on unquantized situation to
solve this accuracy issue.
2. Use `ep_size` rather than `dp_size` to decide whether to use
`all_to_all` in moe.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
CI passed with new added/existing test.
---------
Signed-off-by: angazenn <zengyanjia@huawei.com>
Co-authored-by: angazenn <zengyanjia@huawei.com>
Tweak packed_modules_mapping to support W8A8 weights.
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### What this PR does / why we need it?
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### Does this PR introduce _any_ user-facing change?
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### How was this patch tested?
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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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Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
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### What this PR does / why we need it?
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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?
<!--
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as API, interface or other behavior changes.
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-->
Not change user facing.
### How was this patch tested?
<!--
CI passed with new added/existing test.
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clarify how you tested step by step, ideally copy and paste-able, so
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the future.
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why it was difficult to add.
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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>
<!-- Thanks for sending a pull request!
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### What this PR does / why we need it?
Currently, the implementation for MLA V1 pads q, k, v to `head_dim` 256
to conform to early MLA kernel. But the new MLA kernel supports
`head_dim` that can't be devided by 128. Therefore we can remove those
unnecessary paddings to boost the performance
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
<!--
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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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Signed-off-by: angazenn <zengyanjia@huawei.com>
Co-authored-by: angazenn <zengyanjia@huawei.com>
### What this PR does / why we need it?
Update attention nz and mla nz modules to improve TPOP 6ms performance
Convert W_UV and W_UK_T to NPU format in mla_v1.py
Convert layer.weight to NPU format in w8a8.py
Signed-off-by: ttanzhiqiang <389825161@qq.com>
Implement save kv cache logic for v1 disaggregated prefill in ascend
scheduler
This PR adds support for saving kv cache in the ascend scheduler, which
is part of the v1 disaggregated prefill design. The load functionality
is not yet implemented.
Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>
### What this PR does / why we need it?
Revert the default value of enable_chunked_prefill to 'False' in
additional_scheduler_config. In engine v1, enable_chunked_prefill is
forcibly set to True in VllmConfig, which causes it to be perceived as
True in check_and_update_config(). As a result, when the v0 scheduler is
enabled, the chunked prefill feature remains active, leading to the
failure of the v0 scheduler and causing it to fall back to the native v1
scheduling logic.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
CI passed with new added/existing test.
Signed-off-by: rjg-lyh <1318825571@qq.com>
### What this PR does / why we need it?
Fix the bug of #703, where vllm wrong raised the ERROR : Failed to
import vllm_ascend_C:No module named 'vllm_ascend.vllm_ascend_C'. The
format for reporting import vllm_ascend_C failure is unified by warning
("Failed to import vllm_ascend_C:%s", e).
### Does this PR introduce _any_ user-facing change?
No
---------
Signed-off-by: yangpuPKU <604425840@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>
### What this PR does / why we need it?
Fix the bugs when run deepseek model in engine v1.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
CI passed with new added/existing test.
---------
Signed-off-by: rjg-lyh <1318825571@qq.com>
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### What this PR does / why we need it?
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Set div_mode to False to use the ACLNN kernel, which is crucial when
using ACL Graph.
### Does this PR introduce _any_ user-facing change?
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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?
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Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
- According to https://github.com/vllm-project/vllm-ascend/issues/807,
we pull request for customer ascendc kernel of multi-step.
- also a bug we found in multi_step_runner.py is fixed when we use
multi-step on V0 Engine.
### Does this PR introduce _any_ user-facing change?
no user-facing change
### How was this patch tested?
we add Unit Test file and offline inference file to test the custom
ascendc kernel. See test/ops/test_multi_step.py and
examples/offline_multi_step.py
---------
Signed-off-by: wan_danfeng <wonderful199082@126.com>
For online serving, "ascend" quantization method is not a choice
natively, so we need to add "ascend" quantization method to quantization
methods list and the user can enable quantization using "vllm serve
--quantization ascend" command.
---------
Signed-off-by: 22dimensions <waitingwind@foxmail.com>
The [vllm
commit](67da5720d4)
changed the input and rotary position embedding for qwen 2.5 vl which
break CI. This PR fix the CI failure for qwen2.5 vl in quick
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
this PR fix CI failure broken by vllm.
1. add moe_config for fused_moe
2. adjust the change for kv cache group from vllm. currently vllm-ascend
doesn't support this feature. this is just a quick fix for backward
compatibility
fix: #872
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
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### 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>
make sure pytorch infer_schema check is patched before some case which
using fused moe ops:
1. model register
2. quantization loading
3. fused moe ut
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.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>
