### What this PR does / why we need it?
Align with upstream vLLM. This PR will help downstream vLLM-Omni reduce
the cost for maintaining the _prepare_inputs. Besides, it helps
vLLM-Ascend code more readable. In the future, we can follow closer to
vLLM. The `preprocess` logic is same as GPUModelRunner. We don't need to
maintain it anymore.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI.
- vLLM version: v0.14.0
- vLLM main:
d68209402d
---------
Signed-off-by: gcanlin <canlinguosdu@gmail.com>
### What this PR does / why we need it?
Currently, we pad the last dim of qkv to 128 before flash attention (in
`AscendMMEncoderAttention`) to get better performance on Ascend NPU.
However, the qkv padding is executed serially, which may lead to more
overhead when launching `aclnnConstantPadNd` (launch 3 times).
Since the three operations are mutually independent, we stack qkv first
and then pad them in one kernel launch. With this optimization, **TTFT**
has been reduced by **3.15%**, **peak throughput** has been increased by
**4.20%**.
---
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Launch the server:
```bash
vllm serve /root/.cache/modelscope/hub/models/Qwen/Qwen3-VL-8B-Instruct \
--dtype bfloat16 \
--limit-mm-per-prompt '{"image": 1}' \
--max-model-len 16384 \
--max-num-batched-tokens 16384
```
Run benchmark:
```bash
vllm bench serve \
--model /root/.cache/modelscope/hub/models/Qwen/Qwen3-VL-8B-Instruct \
--backend openai-chat \
--endpoint /v1/chat/completions \
--dataset-name hf \
--hf-split train \
--dataset-path lmarena-ai/vision-arena-bench-v0.1 \
--num-prompts 1000 \
--no-stream
```
Before this PR:
```
============ Serving Benchmark Result ============
Successful requests: 1000
Failed requests: 0
Benchmark duration (s): 122.33
Total input tokens: 66638
Total generated tokens: 122845
Request throughput (req/s): 8.17
Output token throughput (tok/s): 1004.18
Peak output token throughput (tok/s): 3073.00
Peak concurrent requests: 1000.00
Total token throughput (tok/s): 1548.90
---------------Time to First Token----------------
Mean TTFT (ms): 51757.16
Median TTFT (ms): 44853.42
P99 TTFT (ms): 110700.14
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 226.06
Median TPOT (ms): 206.85
P99 TPOT (ms): 935.31
---------------Inter-token Latency----------------
Mean ITL (ms): 208.82
Median ITL (ms): 96.37
P99 ITL (ms): 2183.13
==================================================
```
After this PR:
```
============ Serving Benchmark Result ============
Successful requests: 1000
Failed requests: 0
Benchmark duration (s): 121.47
Total input tokens: 66638
Total generated tokens: 122860
Request throughput (req/s): 8.23
Output token throughput (tok/s): 1011.47
Peak output token throughput (tok/s): 3202.00
Peak concurrent requests: 1000.00
Total token throughput (tok/s): 1560.08
---------------Time to First Token----------------
Mean TTFT (ms): 50125.08
Median TTFT (ms): 46270.85
P99 TTFT (ms): 108107.12
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 227.11
Median TPOT (ms): 205.13
P99 TPOT (ms): 816.08
---------------Inter-token Latency----------------
Mean ITL (ms): 204.60
Median ITL (ms): 92.66
P99 ITL (ms): 2219.02
==================================================
```
- vLLM version: v0.14.0
- vLLM main:
d68209402d
Signed-off-by: shen-shanshan <467638484@qq.com>
### What this PR does / why we need it?
Implement `apply_top_k_top_p` via ascendC to eliminate the constraint of
k [1,1024]. It enables high performance TopKTopP calculation and avoid
D2H synchronization introduced by k validation.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
E2E serving with `k=4096` and `p=0.95`
- vLLM version: v0.13.0
- vLLM main:
d68209402d
---------
Signed-off-by: linfeng-yuan <1102311262@qq.com>
Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
Co-authored-by: SlightwindSec <slightwindsec@gmail.com>
### What this PR does / why we need it?
This patch purpose to add the `update_max_model_len` interface.
- vLLM version: v0.14.0
- vLLM main:
d68209402d
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
In vLLM-Omni, there exists the empty `ModelConfig`. We need to add a
check before accessing the sub-field of model_config.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Will checked by CI.
- vLLM version: v0.14.0
- vLLM main:
d68209402d
Signed-off-by: gcanlin <canlinguosdu@gmail.com>
This reverts commit 8966a99710.
It breaks the test
`tests/e2e/singlecard/spec_decode/test_mtp_eagle_correctness.py::test_deepseek_mtp_correctness[True-FULL_DECODE_ONLY-2-wemaster/deepseek_mtp_main_random_bf16]`
- vLLM version: v0.14.0
- vLLM main:
d68209402d
### What this PR does / why we need it?
Since CI has integrated Triton, `fuse_qknorm_rope` is enabled by
default.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed with new added/existing test.
- vLLM version: v0.14.0
- vLLM main:
d68209402d
---------
Signed-off-by: wxsIcey <1790571317@qq.com>
### What this PR does / why we need it?
**Scope of Changes**:
| File Path |
| :--- |
| `vllm_ascend/distributed/kv_transfer/__init__.py` |
| `vllm_ascend/distributed/kv_transfer/kv_p2p/mooncake_connector.py` |
|
`vllm_ascend/distributed/kv_transfer/kv_p2p/mooncake_layerwise_connector.py`
|
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.14.0
- vLLM main:
d68209402d
Signed-off-by: MrZ20 <2609716663@qq.com>
### What this PR does / why we need it?
* Refactor the LayerNorm and activation operator classes to decouple the
310P device implementation from the main branch.
* Refactor `mm_encoder_attention` on 310P to use the
`torch_npu._npu_flash_attention_unpad` operator.
* Refactor the QKV inputs in the prefill stage of `attention_v1` on 310P
so they are no longer padded to 16× alignment.
* Refactor `model_runner` on 310P to align the KV-cache initialization
logic with the mainline implementation.
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
use the e2e tests.
- vLLM version: v0.13.0
- vLLM main:
d68209402d
---------
Signed-off-by: Tflowers-0129 <2906339855@qq.com>
### What this PR does / why we need it?
#5790 changes default addrmsnormBias operator if custom ops is enabled.
This PR modifies AddRmsNormQuant pass to align with addrmsnormBias.
---------
Signed-off-by: Angazenn <supperccell@163.com>
### What this PR does / why we need it?
**Refactor: Unify full-graph parameter update logic**
This PR consolidates the scattered full-graph parameter update logic
into a unified approach, improving code architecture and eliminating
duplication.
**Key improvements:**
1. **Unified interface**
- Create `update_full_graph_params` as the single entry point for all
full-graph updates
- Replace multiple scattered update calls with one unified function
- Remove ~50 lines of duplicated if-else logic across
`model_runner_v1.py` and `eagle_proposer.py`
2. **Better architecture**
- Move update logic to respective Backend classes
(`AscendAttentionBackend`, `AscendMLABackend`)
- Each Backend manages its own parameter update logic internally
- Simplify caller code to just dispatch to the appropriate Backend
3. **Cleaner parameter handling**
- Remove unnecessary `pcp_size` and `dcp_size` parameter passing
- Get parallel configuration directly from distributed groups
- Consistent with how other parts of the codebase obtain these values
**Why we need it:**
- **Maintainability**: Future changes only need to be made in one place
per Backend
- **Code quality**: Follows DRY principle and Single Responsibility
Principle
- **Readability**: Cleaner, more intuitive code structure
### Does this PR introduce _any_ user-facing change?
**No.** This is a pure refactoring with no functional changes - same
behavior, cleaner code.
### How was this patch tested?
- All existing unit tests pass with updated mocks
- No new tests needed (pure refactoring, no behavior changes)
- CI validates correctness
---
- vLLM version: v0.13.0
Signed-off-by: lico67373 <918688502@qq.com>
Co-authored-by: drslark <slarksblood@qq.com>
Co-authored-by: weijinqian0 <1184188277@qq.com>
### What this PR does / why we need it?
Remove restrictions on mooncake for IPv6
Dependencies: cann8.5、mooncake v0.3.8.post1
- vLLM version: v0.13.0
- vLLM main:
2c24bc6996
---------
Signed-off-by: liziyu <liziyu16@huawei.com>
### What this PR does / why we need it?
This PR：
1. Enhances the logic of `_skip_all_reduce_across_dp_group` to skip all
cpu dp allreduce for dense models. This is also for purpose 2.
2. Adds `_skip_all_reduce_across_dp_group` into eagle_proposer. Now
models like Qwen3-235b supports eagle3 spec decode. A typical setting
for these moe models on pd disaggregation often introduce `dp_size > 1`.
This requires `set_forward_context` to call a cpu dp allreduce to
retrieve `num_tokens_across_dp` on all cases. Skipping this allreduce
greatly improves performance.
- vLLM version: v0.14.0
- vLLM main:
d68209402d
---------
Signed-off-by: Angazenn <supperccell@163.com>
### What this PR does / why we need it?
ucm_connector add has `has_connector_metadata` interface to adapt to the
latest KV connector in vLLM.
### Does this PR introduce _any_ user-facing change?
this PR doesn't introduce _any_ user-facing change.
### How was this patch tested?
- vLLM version: v0.14.0
- vLLM main:
d68209402d
Signed-off-by: UnifiedCacheManager <unifiedcachem@163.com>
### What this PR does / why we need it?
If `sp` is enabled and `tp_size` >= 16,
`torch_npu.npu_mm_reduce_scatter_base` will raises a exception.
After consulting with the operator developer, we learned that the
operator does not work when `tp` = 16.
So, we disable the operator when `tp` = 16.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested
We started a server with `sp` enabled and `tp` = 16.
It started successfully.
```text
[0;36m(APIServer pid=1855938)[0;0m INFO: Started server process [1855938]
[0;36m(APIServer pid=1855938)[0;0m INFO: Waiting for application startup.
[0;36m(APIServer pid=1855938)[0;0m INFO: Application startup complete.
```
- vLLM version: v0.13.0
- vLLM main:
d68209402d
Signed-off-by: drslark <slarksblood@qq.com>
### What this PR does / why we need it?
This PR is to replace addRmsNorm and Add With addRmsNormBias. This way
can lead to a more effecient result.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Full Test Pass
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
Signed-off-by: Chen_HaoWen <chenhaowen12@huawei.com>
Co-authored-by: Chen_HaoWen <chenhaowen12@huawei.com>
### What this PR does / why we need it?
Rectify the problem that the pcp and pd separation and kv pooling
scenario.
In the pooling scenario, multi_nodes_meta_mapping is empty. As a result,
an error is reported when the remote_host information is obtained
through the get_remote_port_send_num method.
### Does this PR introduce _any_ user-facing change?
No
- vLLM version: v0.13.0
- vLLM main:
d68209402d
Signed-off-by: weiguihua2 <weiguihua2@huawei.com>
### What this PR does / why we need it?
Due to the long-term lack of synchronization with the upstream code, a
problem that led to a decrease in the acceptance rate of the
Qwen3-30B-A3B-EAGLE3 draft model was introduced when fixing the
bug（#5967）. Now, synchronize with the upstream and fix this bug
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
```python
from vllm import LLM, SamplingParams
def main():
prompts = [
"The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
# Create an LLM.
llm = LLM(
model="Qwen/Qwen3-30B-A3B",
tensor_parallel_size=4,
gpu_memory_utilization=0.9,
enforce_eager=True,
speculative_config={
"method": "eagle3",
"model": "AngelSlim/Qwen3-a3B_eagle3"
"num_speculative_tokens": 3,
},
)
# Generate texts from the prompts.
outputs = llm.generate(prompts, sampling_params)
print(f"Outputs: {outputs}")
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
- vLLM version: v0.13.0
- vLLM main:
d68209402d
Signed-off-by: zhaomingyu <zhaomingyu13@h-partners.com>
Co-authored-by: drslark <slarkblood@qq.com>
### What this PR does / why we need it?
1.Incorporate the warm up of the EPLB into the profile run.
2.Reusing the same gather buffer
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
qwen3-235b aime baseline
| dataset | version | metric | mode | vllm-api-general-chat |
|----- | ----- | ----- | ----- | -----|
| aime2024 | 604a78 | accuracy | gen | 86.67 |
eplb The OOM issue does not occur.
| dataset | version | metric | mode | vllm-api-general-chat |
|----- | ----- | ----- | ----- | -----|
| aime2024 | 604a78 | accuracy | gen | 86.67 |
- vLLM version: v0.13.0
- vLLM main:
2c24bc6996
Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
### What this PR does / why we need it?
Align max_num_batched_tokens with tp*pcp when using FLASHCOMM1 to avoid
assert error in `NPUModelRunner._dummy_run`.
- vLLM version: v0.13.0
- vLLM main:
2c24bc6996
---------
Signed-off-by: QiuChunshuo <qiuchunshuo@huawei.com>
### What this PR does / why we need it?
`vllm_ascend` already supports several speculative decoding strategies
such as MTP, EAGLE, N-gram, and suffix decoding. However, Medusa is not
yet supported. Medusa is an efficient speculative decoding framework
that leverages a lightweight draft model to propose multiple tokens in a
single step, which can significantly improve decoding throughput and
reduce latency.
To enable Medusa-based speculative decoding on Ascend hardware and
provide more decoding options for users, this PR adds Medusa support
into the `vllm_ascend` speculative decoding pipeline.
### Does this PR introduce _any_ user-facing change?
This PR introduces Medusa speculative decoding as an additional
speculative decoding method:
✔ Adds `MedusaProposer` and integrates it into the speculative decoding
registry
✔ Extends `SpecDcodeType` with a `MEDUSA` enum entry
✔ Updates `NPUModelRunner` to recognize and invoke Medusa during
decoding
✔ Adds Medusa-specific handling in the draft token generation logic
✔ Ensures backward compatibility — Medusa is only used when explicitly
enabled
Key code changes include:
* New file: `vllm_ascend/spec_decode/medusa_proposer.py`
* Register Medusa in `get_spec_decode_method`
* Extend proposer type hints to include `MedusaProposer`
* Add a Medusa-specific branch in `generate_draft_token_ids`
* Pass `sample_hidden_states` required by Medusa
### How was this patch tested?
Medusa is implemented as a new proposer class (`MedusaProposer`)
following the existing speculative decoding interface. The integration
works as follows:
1. Users enable Medusa via the speculative decoding configuration.
2. `get_spec_decode_method()` returns a `MedusaProposer` instance when
`method="medusa"`.
3. During decoding, `NPUModelRunner` detects that the active drafter is
a `MedusaProposer`.
4. Instead of the generic speculative decoding path, the Medusa-specific
`generate_token_ids()` method is invoked, which consumes:
* `valid_sampled_token_ids`
* `sampling_metadata`
* `spec_decode_metadata`
* `sample_hidden_states`
5. The proposed tokens are validated by the target model as usual.
When Medusa is not enabled, the decoding pipeline behaves exactly as
before, ensuring full backward compatibility.
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
Signed-off-by: simplzyu <191163281@qq.com>
Signed-off-by: simplzyu <zhenyuguo@cmbchina.com>
### What this PR does / why we need it?
PCP/DCP splits the kv-cache onto different cards. After introducing the
parameter cp-kv-cache-interleave-size, the first size tokens will be
cached at Card 0, and so on.
However, if there are too few tokens, some cards will not store the
key-value pairs, resulting in values of 0, corrupted values, and
precision issues. Currently, additional operations are introduced to
avoid this precision problem.
After we integrate FIA operator in mla_cp._forward_decode and CANN
updates to 8.5.0, we now can remove these additional operations.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
passed all CI by CANN 8.5.0
- vLLM version: v0.13.0
- vLLM main:
2c24bc6996
Signed-off-by: dsxsteven <dsxsteven@sina.com>
Signed-off-by: dsxsteven <36877507+dsxsteven@users.noreply.github.com>
### What this PR does / why we need it?
Now `seq_lens` was not being reset correctly after each step due to
missing code that clears the sequence lengths. As a result, when
processing a smaller batch after a larger batch, the `seq_lens` from the
larger batch was still carried over. This caused the attention operator
to compute using an unnecessarily larger sequence length, leading to an
increased computation load and performance degradation.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
d68209402d
Signed-off-by: ZYang6263 <zy626375@gmail.com>
### What this PR does / why we need it?
When the P node accesses the proxy meteserver, add the SSL certificate
and the CA certificate path to improve security.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By ci
- vLLM version: v0.13.0
- vLLM main:
bde38c11df
---------
Signed-off-by: wangxiaoteng <wangxiaoteng@huawei.com>
### What this PR does / why we need it?
Drop vLLM 0.13.0 support, upgrade to 0.14.0
- vLLM version: v0.13.0
- vLLM main:
d68209402d
---------
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
### What this PR does / why we need it?
This PR merge all steps of draft model in fullgraph mode, to avoid the
synchronize between each graph, reduce the bubble time.
#### Key ideas:
- The "model forward" of the step 0 (first step) and remaining steps are
captured together as a "Callable", rather than capturing each model
individually.
- "update_attn_params" is moved outside the entire graph, meaning that
all "attn_metadata" required by all steps are constructed before
"replay", and the "attn_params" of all steps are updated at once.
- Remove synchronization between the main model graph and draft model
graph.
#### Key params/functions:
- params: draft_attn_metadatas, attn_metadata_multi_steps,
slot_mapping_group
- functions: _run_merged_draft, attn_update_stack_num_spec_norm,
update_attn_params, _propose, dummy_run
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
11b6af5280
Signed-off-by: anon189Ty <Stari_Falcon@outlook.com>
### What this PR does / why we need it?
This PR aims to remove `use_aclgraph` and use `use_cuda_graph` just the
same as eagle_proposer in mtp_proposer. The reason of these changes are
described below.
There is a scenario that `use_aclgraph=True` while
`use_cuda_graph=False`, e.g. enabling `async_scheduling=True`. When
using deepseek v3.2, `common_attn_metadata.num_input_tokens` is
important and it should be the same as `num_input_tokens` entering into
model. In the above scenario, `use_aclgraph` accidentally pad
`num_tokens` to `num_input_tokens`, coinciding with
`common_attn_metadata.num_input_tokens`. But later eager mode is
triggered and actually we don't need padding. That means that the code
logic is incorrect but the running output looks fine.
However, `common_attn_metadata.num_input_tokens` should mean
`num_input_tokens` entering into model. So we should update
`common_attn_metadata.num_input_tokens = num_input_tokens` after
padding. Therefore, we can safely use normal `use_cuda_graph` instead of
problematic `use_acl_graph`.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
by ci
- vLLM version: v0.13.0
- vLLM main:
2c24bc6996
Signed-off-by: Zetong Li <slippersss@126.com>
### What this PR does / why we need it?
Fix Qwen3VL dense quant model load weights Error.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
The Qwen3VL quantized model service initialized successfully. Inference
requests are processed correctly, and valid responses are returned.
- vLLM version: v0.13.0
- vLLM main:
d68209402d
Signed-off-by: 李少鹏 <lishaopeng21@huawei.com>
### What this PR does / why we need it?
Replace the npu_multi_head_latent_attention with FIA operator in
mla_cp.py _forward_decode.
Adjust mla_attn_dpc_pcp in acl_graph.py
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
---------
Signed-off-by: 白永斌 <baiyongbin3@h-partners.com>
Signed-off-by: Bai Yongbin <845473182@qq.com>
Signed-off-by: tongyuzhou <t00886357@china.huawei.com>
Co-authored-by: 白永斌 <baiyongbin3@h-partners.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: tongyuzhou <t00886357@china.huawei.com>
### What this PR does / why we need it?
This PR builds upon PR
https://github.com/vllm-project/vllm-ascend/pull/5011 and aims to
further enhance the npu_graph_ex_passes module. Based on prior work, we
have added graph optimization support for the add_rms_quant fused
operator in scenarios where a bias term is present—ensuring the fusion
pattern is correctly registered and matched into the computation graph.
For validation, we switched to the Qwen3-235B-A22B-W8A8 model for
QKVNormRopeWithBias and Qwen3-32B model for QKVNormRope . Benchmark
results show that, compared to the unfused baseline, enabling this
fusion pass significantly improves inference throughput for W8A8
quantized models.
For more details can refer to the
RFC:https://github.com/vllm-project/vllm-ascend/issues/4715
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
```
llm = LLM(
model=model,
tensor_parallel_size=GPUs_per_dp_rank,
enforce_eager=False,
enable_expert_parallel=enable_expert_parallel,
trust_remote_code=trust_remote_code,
gpu_memory_utilization=0.98,
max_num_batched_tokens=512,
# load_format="dummy",
max_model_len=2048,
max_num_seqs=16,
quantization="ascend",
additional_config={
"refresh": True,
"enable_npugraph_ex": True
},
compilation_config={
"cudagraph_capture_sizes": [8, 16],
"cudagraph_mode": "FULL_DECODE_ONLY",
},
)
if profile_dir:
llm.start_profile()
outputs = llm.generate(prompts, sampling_params)
if profile_dir:
llm.stop_profile()
for i, output in enumerate(outputs):
if i >= 5:
break
prompt = output.prompt
generated_text = output.outputs[0].text
print(
f"DP rank {global_dp_rank}, Prompt: {prompt!r}, "
f"Generated text: {generated_text!r}"
)
```
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
---------
Signed-off-by: cjian <2318164299@qq.com>
### What this PR does / why we need it?
The issue of the D node mistakenly sending the pull-end signal twice,
leading to the P node printing logger errors abnormally, has been
resolved.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By ci
- vLLM version: v0.13.0
- vLLM main:
d68209402d
Signed-off-by: wangxiaoteng <wangxiaoteng@huawei.com>
### What this PR does / why we need it?
This helps to fix a bug in for pa get_workspace. In earlier
implementation, we use `_npu_paged_attention_get_workspace` in
`_update_pa_attn_params`. However, this might cause some potential
memory problems as it dynamically allocate new memory for workspace when
calling this api. Therefor, we move this back to capturing, and use a
fixed `SEQ_LEN_WITH_MAX_PA_WORKSPACE` to get max workspace.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
Signed-off-by: Angazenn <supperccell@163.com>
### What this PR does / why we need it?
This patch purpose to optimize the lint check term. The main idea is to
reduce unnecessary installation time.
1. The installation of vllm is not must, only append the path of vllm
src to the `PATHONPATH` is effective
2. This installation of `requirements-dev.txt` is not must, we have a
pre-built image `quay.io/ascend-ci/vllm-ascend:lint` with all the
requirements installed in advance.
**NOTE**: the conditions for triggering image builds are: 1).Daily
scheduled build; 2) Build when requirements are modified; 3) Manual
build. This ensures that the dependencies in our image are up-to-date to
the greatest extent possible.
3. The `mypy` was separated from the `pre-commit` hook for performance
reasons; we found that integrating `mypy` into the `pre-commit` hook
resulted in poor performance.
4. Reduce the CPU core consumption from 16 -> 8
### Does this PR introduce _any_ user-facing change?
The end-to-end lint time was optimized from 20min/per PR to 8min/per PR
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
2c24bc6996
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
When running the Qwen2.5-Omni-7B model on Ascend NPU, the engine fails
during the profiling/warmup stage with the following error:
`AclNN_Runtime_Error(EZ9903): rtKernelLaunchWithHandleV2 failed: 507035.
The vector core execution is abnormal.`
error log:
https://github.com/vllm-project/vllm-ascend/actions/runs/21144534911/job/60806765393#step:17:6412
This error is specifically triggered by the `triton_mrope` kernel when
handling the unique `mrope_section` configurations of the Omni model.
Other multimodal models with standard sections (e.g., [16, 24, 24]) or
standard LLMs work correctly with Triton.
Modified vllm_ascend/ops/rotary_embedding.py to add a conditional check
before calling forward_triton.
1. For standard LLMs (mrope_interleaved = True ), it continues to use
Triton for acceleration.
2. For complex configurations (like Qwen2.5-Omni mrope_interleaved =
False ), it now falls back to the native super().forward_oot() path,
which uses the stable torch_npu or PyTorch implementation.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
d68209402d
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
### What this PR does / why we need it?
When using the full_decode_only mode, the vllm framework will still use
the torch.fx.passes.split_module.split_module API to process the
corresponding GraphModule of the model.
However, the output of this API may cause the output of the fx graph to
no longer be a tuple, and torch.compile enforces strict checks on this.
Previously, we manually modified the fx graph, which introduced an
abnormality in the model output type.
In this PR, we switched to using PyTorch's native API to modify the FX
graph, and removed the code that was previously added to handle output
type anomalies.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
2c24bc6996
---------
Signed-off-by: chencangtao <chencangtao@huawei.com>
Co-authored-by: chencangtao <chencangtao@huawei.com>
According to the official documentation, the parameter
"draft_tensor_parallel_size": 1 is supposed to be applied to the Eagle3
model. However, based on actual debugging, it was found that the number
of tensor parallelisms (tp) of the Eagle model is consistent with that
of the target model. The setting of tp for the draft model did not take
effect as expected.
**Note:** This feature has not been superimposed and tested with `sp`
and `dp`. It will be adapted later
No
```python
from vllm import LLM, SamplingParams
def main():
prompts = [
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(
model="meta-llama/Llama-3.1-8B-Instruct",
tensor_parallel_size=4,
gpu_memory_utilization=0.9,
enforce_eager=True,
speculative_config={
"method": "eagle3",
"model": "yuhuili/EAGLE3-LLaMA3.1-Instruct-8B"
"draft_tensor_parallel_size": 1,
"num_speculative_tokens": 3,
},
)
outputs = llm.generate(prompts, sampling_params)
print(f"Outputs: {outputs}")
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
Fixesvllm-project/vllm#31345
### What this PR does / why we need it?
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
d68209402d
Signed-off-by: zhaomingyu <zhaomingyu13@h-partners.com>
Co-authored-by: drslark <slarksblood@qq.com>
### What this PR does / why we need it?
Operator `DispatchGmmCombineDecode` does not support non-W8A8 scenarios
and cannot share the same communication domain with Operator
`Dispatch`/`Combine`.
> for instance, when the draft model uses a non-W8A8 MOE architecture
while the main model employs a W8A8 MOE architecture.
Therefore days ago, I implemented an interception that unconditionally
disables Operator `DispatchGmmCombineDecode` whenever the speculative
mode is `EAGLE` or `EAGLE-3`. [PR:
5293](https://github.com/vllm-project/vllm-ascend/pull/5293)
However, this approach was not precise enough.
This PR further refines the logic by specifically identifying the draft
model's configuration: Operator `DispatchGmmCombineDecode` will now be
disabled only when the draft model uses an MOE architecture and is
non-W8A8.
More info about this operator, please refer to RFC: issue
https://github.com/vllm-project/vllm-ascend/issues/5476
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Acc
test qwen3-235b eplb on a single A3 node(ep16),
with dispatch_gmm_combine_decode
```shell
nic_name="xxxx"
local_ip="xxx.xxx.xxx.xxx"
export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
export VLLM_ASCEND_ENABLE_FUSED_MC2=2
echo "VLLM_ASCEND_ENABLE_FUSED_MC2=${VLLM_ASCEND_ENABLE_FUSED_MC2}"
export HCCL_OP_EXPANSION_MODE="AIV"
export HCCL_BUFFSIZE=512
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
vllm serve /dataset/Qwen3-235B-A22B-Instruct-2507-w8a8-QuaRot/ \
--served-model-name "qwen" \
--host 0.0.0.0 \
--port 8004 \
--async-scheduling \
--tensor-parallel-size 4 \
--data-parallel-size 4 \
--max-num-seqs 64 \
--max-model-len 40960 \
--max-num-batched-tokens 16384 \
--gpu-memory-utilization 0.9 \
--enable-expert-parallel \
--no-enable-prefix-caching \
--quantization "ascend" \
--trust-remote-code \
--speculative_config \
'{
"method": "eagle3",
"model": "/dataset/Qwen3-235B-A22B-Instruct-2507-speculator-eagle3/",
"num_speculative_tokens": 2
}' \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
2>&1 | tee qwen3_235b_eagle3.log
```
| dataset | version | metric | mode | vllm-api-stream-chat |
|----- | ----- | ----- | ----- | -----|
| aime2024 | 604a78 | accuracy | gen | 80.00 |
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
Signed-off-by: wangqiankun <wangqiankun13@huawei.com>
This PR addresses a request ID mismatch issue in the PD
(Prefill-Decoding) separation deployment scenario for vllm-ascend.
Upstream vLLM recently mitigated request ID collisions by appending a
random suffix to each request_id (e.g., req-123 → req-123-abc), refer to
[PR-27987](https://github.com/vllm-project/vllm/pull/27987 ) &
[PR-29665](https://github.com/vllm-project/vllm/pull/29665). While this
works in single-node deployments, it breaks compatibility in
PD-separated setups: the Producer (Prefill node) and Consumer (Decoding
node) end up with different request_id values, preventing the Consumer
from correctly retrieving the KV cache generated by the Producer.
To resolve this, this PR introduces a new field remote_request_id in the
metadata passed via mooncake_connector. The Producer preserves and
forwards the original (unmodified) request_id as remote_request_id. The
Consumer then uses this remote_request_id—instead of its locally
generated suffixed ID—to fetch the correct KV cache from the Prefill
node.
This ensures consistent request identification across PD nodes while
maintaining compatibility with upstream vLLM’s request ID deduplication
mechanism.
<img width="1279" height="781" alt="image"
src="https://github.com/user-attachments/assets/274238c1-dab6-4d3a-9ee4-6e578679b762"
/>
- vLLM version: v0.13.0
- vLLM main:
d68209402d
Signed-off-by: ghphotoframe <854746559@qq.com>
Co-authored-by: jiangweixiang <jwx02384838@antgroup.com>
Signed-off-by: zzhx1 <zzh_201018@outlook.com>
### What this PR does / why we need it?
> Extracted from PR #5513
Based on the Sharded-CP feature PR:#4702;
RFC:https://github.com/vllm-project/vllm/issues/30055
### Support FULL_DECODE_ONLY Mode under PD-Mixed Scenario:
Extends DSA-CP to handle the FULL_DECODE_ONLY execution mode when
running in a prefill-decode mixed (PD-mixed) serving environment,
improving throughput and resource utilization for decode-intensive
workloads.
**In pure prefill nodes:**
- Both q_proj and o_proj are sharded across world ranks, using
**broadcast** for weights distribution.
**In PD-mixed nodes (supporting both prefill and decode):**
- q_proj is fully replicated (not sharded) to avoid communication
overhead during decoding.
- o_proj Using the original TP `RowParallelLinear` method to store
weights
**During prefill execution:**
- o_proj forwards through all_gather to collect weights, reconstructing
the complete o_proj weights on each card.
**During decode (graph replay phase):**
- Additional all_to_all (before o_proj) and reduce_scatter (after
o_proj) are introduced to enable sequence-parallel output aggregation
while maintaining correctness under SFA CP.
### benchmark:
- TTFT increased by **527%**
- TPOT increased by **180%**
<img width="1550" height="938" alt="image"
src="https://github.com/user-attachments/assets/9b7a03d8-a3db-4a99-8923-6e5bfcfecf72"
/>
### Does this PR introduce _any_ user-facing change?
None
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
---------
Signed-off-by: zzhx1 <zzh_201018@outlook.com>
Signed-off-by: zzhxx <zhangzihang23@mails.ucas.ac.cn>
Co-authored-by: clrs97 <524936896@qq.com>
