Currently in the Fused MoE module, functions of classes like
MoECommMethod and MoETokenDispatcher output data in dictionary or tuple
format, which hampers code maintainability, readability, and
extensibility. This PR introduces dataclasses for these key output types
to address these issues.
- vLLM version: v0.13.0
- vLLM main:
5326c89803
---------
Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>
By converting the KV cache from ND to NZ format when the decode node
receives it, this PR ensures that the KV NZ feature works correctly
during the decoding phase in disagg-prefill scenario.
- vLLM version: v0.11.0
- vLLM main:
83f478bb19
---------
Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>
Co-authored-by: ghphotoframe <854746559@qq.com>
Co-authored-by: alex101-ops <alex1015718386@gmail.com>
### What this PR does / why we need it?
This patch is a series of refactoring actions, including clarifying the
directory structure of nightly tests, refactoring the config retrieval
logic, and optimizing the workflow, etc. This is the first step:
refactoring the directory structure of nightly to make it more readable
and logical.
- vLLM version: v0.13.0
- vLLM main:
5326c89803
Signed-off-by: wangli <wangli858794774@gmail.com>
1. What this PR does / why we need it?
This PR supports the moe_gating_top_k operator, which enables
post-positioned renormalization (renorm) on the basis of softmax.
2. Does this PR introduce any user-facing change?
No user-facing changes are required.
3. How was this patch tested?
This patch was tested with the test_npu_moe_gating_top_k test case.
vLLM version: release/v0.13.0
vLLM main:
ad32e3e19c
---------
Signed-off-by: ZCG12345 <2097562023@qq.com>
Signed-off-by: zzzzwwjj <34335947+zzzzwwjj@users.noreply.github.com>
Co-authored-by: zzzzwwjj <34335947+zzzzwwjj@users.noreply.github.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?
<!--
- 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 #
-->
This pull request introduces a new custom operator
`aclnnMoeInitRoutingCustom` for Mixture-of-Experts models.
It can be replaced by `aclnnMoeInitRoutingV3` once CANN 8.5 becomes
available.
### 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.
-->
No.
### 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.
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why it was difficult to add.
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---------
Signed-off-by: jiazhengyi <jiazhengyi@huawei.com>
Signed-off-by: Chenxi Qian <chenxi.qian.cq@outlook.com>
Co-authored-by: jiazhengyi <jiazhengyi@huawei.com>
Co-authored-by: Chenxi Qian <chenxi.qian.cq@outlook.com>
### What this PR does / why we need it?
This pacth mainly do the following things:
1. Bugfix for multi_node_tests log, log names must be unique when
uploading logs.
2. Optimize `get_cluster_ips` logic, increase the max retry times for
robustness
3. Abandoned the existing gh-proxy temporarily until it is stable
enough.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: release/v0.13.0
- vLLM main:
81786c8774
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
Qwen3-235B-A22B belongs to the TopN model, but there is currently a lack
of care for the test cases of the wen3-235B-A22B model on Atlas A2, and
most of the machines currently owned by users in the community are A2.
When users encounter problems, we currently have no way of knowing
whether the model runs normally on the corresponding version of the
code, so we added it. In addition, we currently see TopN models such as:
qwen-dense, qwen3-30b-a3b, Qwen3-Next, Qwen2.5-Omni, but Qwen3-235B-A22B
is missing.
### Does this PR introduce _any_ user-facing change?
NA
### How was this patch tested?
Test with multi-node, result as following:
1. Accuracy test (Time for executing this test case: 25 minutes)
test running successfully, accuracy as following:
```
dataset version metric mode vllm-api-general-chat
--------- --------- -------- ------ -----------------------
gsm8k 7cd45e accuracy gen 95.68
```
2. Perf test (Time for executing this test case: 1h15 minutes)
test running successfully, throughput as following(This is the atlas A3,
for A2 the result about A3/1.3):
```
╒══════════════════════════╤═════════╤════════════════╤════════════════╤════════════════╤════════════════╤════════════════╤════════════════╤════════════════╤══════╕
│ Performance Parameters │ Stage │ Average │ Min │ Max │ Median │ P75 │ P90 │ P99 │ N │
╞══════════════════════════╪═════════╪════════════════╪════════════════╪════════════════╪════════════════╪════════════════╪════════════════╪════════════════╪══════╡
│ E2EL │ total │ 384086.3958 ms │ 214767.0486 ms │ 528014.771 ms │ 387621.5746 ms │ 388776.7492 ms │ 390164.3559 ms │ 488105.8512 ms │ 2800 │
├──────────────────────────┼─────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼──────┤
│ TTFT │ total │ 159409.9868 ms │ 1849.4588 ms │ 302439.6965 ms │ 162183.7007 ms │ 162965.477 ms │ 164274.1936 ms │ 262578.6041 ms │ 2800 │
├──────────────────────────┼─────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼──────┤
│ TPOT │ total │ 149.8842 ms │ 130.2175 ms │ 151.2625 ms │ 150.473 ms │ 150.6978 ms │ 150.9102 ms │ 151.2131 ms │ 2800 │
├──────────────────────────┼─────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼──────┤
│ ITL │ total │ 149.6789 ms │ 0.0099 ms │ 283.0242 ms │ 150.3276 ms │ 156.8649 ms │ 168.1372 ms │ 199.378 ms │ 2800 │
├──────────────────────────┼─────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼──────┤
│ InputTokens │ total │ 3654.3079 │ 3108.0 │ 4280.0 │ 3629.0 │ 3728.0 │ 3842.1 │ 4079.0 │ 2800 │
├──────────────────────────┼─────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼──────┤
│ OutputTokens │ total │ 1500.0 │ 1500.0 │ 1500.0 │ 1500.0 │ 1500.0 │ 1500.0 │ 1500.0 │ 2800 │
├──────────────────────────┼─────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼──────┤
│ OutputTokenThroughput │ total │ 3.935 token/s │ 2.8408 token/s │ 6.9843 token/s │ 3.8698 token/s │ 3.8799 token/s │ 3.9916 token/s │ 6.2137 token/s │ 2800 │
╘══════════════════════════╧═════════╧════════════════╧════════════════╧════════════════╧════════════════╧════════════════╧════════════════╧════════════════╧══════╛
╒══════════════════════════╤═════════╤═══════════════════╕
│ Common Metric │ Stage │ Value │
╞══════════════════════════╪═════════╪═══════════════════╡
│ Benchmark Duration │ total │ 4391524.3389 ms │
├──────────────────────────┼─────────┼───────────────────┤
│ Total Requests │ total │ 2800 │
├──────────────────────────┼─────────┼───────────────────┤
│ Failed Requests │ total │ 0 │
├──────────────────────────┼─────────┼───────────────────┤
│ Success Requests │ total │ 2800 │
├──────────────────────────┼─────────┼───────────────────┤
│ Concurrency │ total │ 244.8903 │
├──────────────────────────┼─────────┼───────────────────┤
│ Max Concurrency │ total │ 256 │
├──────────────────────────┼─────────┼───────────────────┤
│ Request Throughput │ total │ 0.6376 req/s │
├──────────────────────────┼─────────┼───────────────────┤
│ Total Input Tokens │ total │ 10232062 │
├──────────────────────────┼─────────┼───────────────────┤
│ Prefill Token Throughput │ total │ 22.924 token/s │
├──────────────────────────┼─────────┼───────────────────┤
│ Total generated tokens │ total │ 4200000 │
├──────────────────────────┼─────────┼───────────────────┤
│ Input Token Throughput │ total │ 2329.9568 token/s │
├──────────────────────────┼─────────┼───────────────────┤
│ Output Token Throughput │ total │ 956.3877 token/s │
├──────────────────────────┼─────────┼───────────────────┤
│ Total Token Throughput │ total │ 3286.3445 token/s │
╘══════════════════════════╧═════════╧═══════════════════╛
```
- vLLM version: release/v0.13.0
- vLLM main:
254f6b9867
---------
Signed-off-by: leo-pony <nengjunma@outlook.com>
### What this PR does / why we need it?
This PR adds the method for sending chat and non-chat request, we need
it to test much folloing cases.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
by running the test
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: jiangyunfan1 <jiangyunfan1@h-partners.com>
### What this PR does / why we need it?
#4443 introduces a precision issue in scenarios where MTP >= 3 + deepseek v3.1, and this pr reverts it
- vLLM version: release/v0.13.0
- vLLM main:
bc0a5a0c08
Signed-off-by: GDzhu01 <809721801@qq.com>
1. refresh additional config doc
2. move kv config logic to platform.
3. improve `dump_config` init logic and rename it to `dump_config_path`
this change is user impacted. dump_config is changed from dict to
string.
4. correct `enable_async_exponential` type
5. remove useless `chunked_prefill_for_mla`
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
- Fix vllm break in the pr:
1.[Drop v0.14 deprecations
]https://github.com/vllm-project/vllm/pull/31285
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- vLLM version: release/v0.13.0
- vLLM main:
bc0a5a0c08
---------
Signed-off-by: ZT-AIA <1028681969@qq.com>
### What this PR does / why we need it?
Currently, our multi-node logs only show the master node's logs (via the
Kubernetes API), which is insufficient for effective problem
localization if other nodes experience issues. Therefore, this pull
request adds the ability to upload logs for other nodes.
Next plan: Output structured directory logs, including logs from each
node and the polog.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: release/v0.13.0
- vLLM main:
bc0a5a0c08
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
Description:
This PR updates the implementation of the Triton operator for deployment
on NPU devices, focusing on optimizing grid size and memory handling
based on NPU limitations.
Design Plan:
Grid Calculation: The grid size is now dynamically calculated by batch
and dim to ensure that the number of programs executed does not exceed
the NPU's vector core capacity. This ensures optimal parallelism without
overloading the hardware.
Data Block Handling: Due to the limited on-chip memory (UB) on Ascend
NPUs, this implementation splits large data into smaller chunks of 32k
or less per block. The kernel performs a for-loop to process the data in
these smaller chunks, minimizing memory usage and avoiding potential
overflows.
Changes Compared to GPU Implementation:
Grid and Block Sizing:
For GPU, the grid and block size were determined based on available
thread counts and memory size. In contrast, the NPU version dynamically
adjusts these parameters using B_TILE and BLOCK_N to optimize for NPU’s
architecture.
Memory Chunking:
The original GPU implementation did not require chunking due to the
higher available memory and processing capacity. For the NPU, data is
divided into smaller chunks (32k or smaller) to comply with memory
constraints on the device. The kernel has been modified to handle this
chunking mechanism inside a loop.
Optimized Thread Usage:
The NPU implementation takes into account the hardware-specific thread
limit (24 threads per vector core), ensuring that the number of active
programs is aligned with the NPU's vector core count, avoiding
over-subscription that would lead to serial processing.
This PR ensures that the operator functions efficiently on Ascend NPU,
considering hardware limitations while maintaining the same
functionality and input parameters as the GPU implementation.
- vLLM version: release/v0.13.0
- vLLM main:
5fbfa8d9ef
Signed-off-by: maoxx241 <maomaoyu870@gmail.com>
### What this PR does / why we need it?
Fixed the error in the CI process for
vllm-ascend/tests/e2e/nightly/ops/triton/test_rejection_sampler.py
Error: test_rejection_sampler_block_verify_triton_kernel: duplicate
parametrization of 'vocab_size'.
- vLLM version: release/v0.13.0
- vLLM main:
bc0a5a0c08
Signed-off-by: chenaoxuan <cax1165@163.com>
`VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE` is only used together with
`VLLM_ASCEND_ENABLE_PREFETCH_MLP` which is useless totally. This PR
remove it.
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
1. MagicMTP (paper: "Block Verification Accelerates Speculative
Decoding") was introduced to consider the influence among multiple draft
tokens, improving the acceptance rate without compromising accuracy.
2. The rejection sampling logic in rejection_sampler.py was restructured
using Triton-Ascend, enabling it to operate under high concurrency, thus
resolving CPU and NPU operator bottlenecks and enhancing throughput.
### Does this PR introduce _any_ user-facing change?
MagicMTP will automatically take effect when the parameter
"num_speculative_tokens" >= 3.
- vLLM version: v0.11.2
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.2
Signed-off-by: chenaoxuan <cax1165@163.com>
### What this PR does / why we need it?
This pull request introduces an L2 normalization kernel implemented in
Triton, specifically optimized for Ascend NPUs.
### Does this PR introduce _any_ user-facing change?
No, this PR does not introduce any user-facing changes.
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
bc0a5a0c08
---------
Signed-off-by: Ascendyh <hw7osiris@outlook.com>
Co-authored-by: Mengqing Cao <cmq0113@163.com>
### What this PR does / why we need it?
Remove unnecessary attributes from set_ascend_forward_context
1.prefetch_stream
2.weight_prefetch_method
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: Wang Kunpeng <1289706727@qq.com>
### What this PR does / why we need it?
This PR updates the mm param --mm-processor-cache-gb, we need it to run
the case
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
by running the test
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
Signed-off-by: jiangyunfan1 <jiangyunfan1@h-partners.com>
### What this PR does / why we need it?
In this PR, DispatchGmmCombineDecode add an optional input
x_active_mask, with which
only token masked True will be dispatched and handle.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: wangqiankun <wangqiankun13@huawei.com>
### What this PR does / why we need it?
vLLM community has integrated their MooncakeConnector. The original
scripts will now find this MooncakeConnector instead of the one from
vLLM-Ascend. All scripts that involve using the MooncakeConnector need
to be modified to another name.
### Does this PR introduce _any_ user-facing change?
Yes, users need to use a new name to load vLLM-Ascend MooncakeConnector.
### How was this patch tested?
By CI.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: nwpu-zxr <zhouxuerong2@huawei.com>
### What this PR does / why we need it?
Since the `llmdatadist` has sunset, the logic gen_ranktable should also
be removed
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
Support triton causal_conv1d_fn ops.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
CI passed with new added/existing test.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: QilaiZhang <245706640@qq.com>
### What this PR does / why we need it?
This PR adds mlapo operation support for bf16 no_quant mode.
### Does this PR introduce _any_ user-facing change?
This PR makes quant related parameters optional.
### How was this patch tested?
CI passed with new added/existing test.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: chenjunyi <isjunyi.chen@gmail.com>
### What this PR does / why we need it?
This patch do some tiny optimization for nightly ci:
1. Polling the frequency with which the service prints logs when it
starts up in order to obtain useful information more quickly.
2. Shorten the timeout for waiting server
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
aclgraph is stable and fast now. Let's drop torchair graph mode now.
TODO: some logic to adapt torchair should be cleaned up as well. We'll
do it in the following PR.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Co-authored-by: Mengqing Cao <cmq0113@163.com>
### What this PR does / why we need it?
As there is not accuracy test for qwen3-235B-A22B model
Test result:
dataset version metric mode vllm-api-general-chat
--------- --------- -------- ------ -----------------------
gsm8k 7cd45e accuracy gen 96.29
Times long for test case running: 30mintues
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: leo-pony <nengjunma@outlook.com>
Co-authored-by: Mengqing Cao <cmq0113@163.com>
### What this PR does / why we need it?
As support for the mooncake connector is now available, the llmdatadist
connector is no longer being maintained, so the llmdatadist-related
files need to be retired.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By ci
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: wangxiaoteng <wangxiaoteng@huawei.com>
Signed-off-by: liziyu <liziyu16@huawei.com>
Co-authored-by: liziyu <liziyu16@huawei.com>
### What this PR does / why we need it?
This PR adapt DispatchGmmCombineDecode operator to parameters of small
operators.
1. This operator no longer requires permuting the weights and scales of
GMM1.
2. This operator no longer requires transposing the weights of GMM2.
Therefore, this operator and the small operator can use the same
parameters (weights and scales), which is beneficial for model
adaptation.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: wangqiankun <wangqiankun13@huawei.com>
### What this PR does / why we need it?
This PR adds mlapo operation support qdown of output.
### Does this PR introduce _any_ user-facing change?
mlapo operation add enable_inner_out of input
### How was this patch tested?
CI passed with new added/existing test.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: h1074112368 <h1074112368@gmail.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
This PR introduces the Ascend implementation of the
`dispatch_ffn_combine` kernel and wires it into the vLLM-Ascend runtime,
together with follow‑up fixes to ensure the kernel builds and runs
correctly in CI.
- Add full host and device implementation of the `dispatch_ffn_combine`
kernel under `csrc/dispatch_ffn_combine`, including tiling logic, MOE
routing helpers, and kernel utilities for quantized FFN dispatch.
- Integrate the new kernel with the PyTorch binding
(csrc/torch_binding.cpp, csrc/torch_binding_meta.cpp) and the Ascend
runtime (vllm_ascend/ascend_forward_context.py,
vllm_ascend/worker/model_runner_v1.py).
- Extend fused MoE communication and token dispatch support in
`vllm_ascend/ops/fused_moe`, adding methods/utilities needed by the new
dispatch path.
- Update quantization logic in vllm_ascend/quantization/w8a8_dynamic.py
to support the new FFN dispatch flow.
- Fix kernel build issues by adjusting `csrc/build_aclnn.sh`, CMake
configuration, and include/namespace usage in the new kernel files.
- Add an end‑to‑end nightly test
`tests/e2e/nightly/ops/test_dispatch_ffn_combine.py` and helper
utilities in `vllm_ascend/utils.py` to validate the new kernel.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.12.0
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.12.0
---------
Signed-off-by: mojave2 <chenchen145@huawei.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
1. Optimize multi-node waiting logic
2. Remove the `tee` pipeline for logs, which will lead to hang issue
### How was this patch tested?
- vLLM version: v0.12.0
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.12.0
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
This pr https://github.com/vllm-project/vllm-ascend/pull/2958 is
supporting gatingtopk operator generalization, but caused nightly ci
error.
Now we add check logits for ops gatingtopk, and fix nightly ci.
- vLLM version: v0.12.0
Signed-off-by: 1092626063 <1092626063@qq.com>
### What this PR does / why we need it?
This PR adds a triton rope kernel witch supports scenarios of `rope_dim
!= head_dim`. This can save the split op before rope and the concat op
after rope. Profiling shows improvement.
### Does this PR introduce _any_ user-facing change?
None
### How was this patch tested?
I will add related ut after ci integrated with triton.
- vLLM version: v0.11.2
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.2
---------
Signed-off-by: whx-sjtu <2952154980@qq.com>
### What this PR does / why we need it?
Add a new fusion ops to custom_op, which can cobime the torch.bmm() and
transpsose to achieve better peformance. This ops is used in mla_v1 to
replace the bmm and transpose
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- vLLM version: v0.11.2
---------
Signed-off-by: hust17yixuan <303660421@qq.com>
Ascend scheduler was added for non chunk prefill case before, since that
the npu ops didn't work well with chunked prefill.
Now the ops with chunked prefill work better, it's time to remove the
ascend scheduler to use vLLM default scheduler.
- vLLM version: v0.11.2
---------
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
This PR introduces support for adding custom CANN `aclnn` ops to
`vllm-ascend`, allowing users to define and use their own custom
operators.
Key changes include:
- Building and installing custom ops into the `vllm-ascend`-specified
directory
- Binding the `aclnn` op interface to the `torch.ops._C_ascend` module
- Enabling invocation of these ops within `vllm-ascend`
This PR includes a sample custom op:
`aclnnGroupedMatmulSwigluQuantWeightNzTensorList`, which is adapted from
the CANN operator
[`aclnnGroupedMatmulSwigluQuantWeightNZ`](https://www.hiascend.com/document/detail/zh/canncommercial/83RC1/API/aolapi/context/aclnnGroupedMatmulSwigluQuantWeightNZ.md).
Its input parameters `weight` and `weight_scale` now accept
`list[torch.Tensor]` (i.e., `at::TensorList`).
### Does this PR introduce _any_ user-facing change?
No.
- vLLM version: v0.11.2
---------
Signed-off-by: QianChenxi <chenxi.qian.cq@outlook.com>
This PR introduces the `EXEC_NPU_CMD` macro, serving as an adapter layer
to simplify the invocation of `aclnn` operators on Ascend NPUs.
**Key Changes:**
* **Adapter Layer:** Added `EXEC_NPU_CMD` macro and related dependencies
to standardize `aclnn` calls.
* **Operator Support:** Integrated `grouped_matmul_swiglu_quant` as a
reference implementation to demonstrate the usage of the new macro.
---
- vLLM version: v0.11.2
---------
Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
### What this PR does / why we need it?
This patch mainly doing the following things:
1. Make k8s/lws optional for multi-node testing, allowing developers to
run multi-node tests locally by actively passing in the IP addresses of
all nodes.
2. Allows passing a custom proxy script path in the config file to load
the proxy.
- vLLM version: v0.11.2
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
