### What this PR does / why we need it?
Fix the issue #6143 .
### Does this PR introduce _any_ user-facing change?
Allow to start the server with "--enable-lora && --fully-sharded-loras
&& --tensor_parallel_size 2".
### How was this patch tested?
pytest -sv tests/e2e/multicard/2-cards/test_llama32_lora_tp2.py
- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd
---------
Signed-off-by: paulyu12 <507435917@qq.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
Currently, we call lookup_client for looking up token hit in KV Pool,
however, when token length < block size, the key will be empty and there
is no point to lookup in KV Pool backend since there will never be a
hit.
Hence, add early return in `get_num_new_matched_tokens` when `token_len`
< `block_size`
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: Pz1116 <zpbzpb123123@gmail.com>
Co-authored-by: fems14 <1804143737@qq.com>
### What this PR does / why we need it?
Build Image for v0.16.0rc1
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
Signed-off-by: MengqingCao <cmq0113@163.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
The `test_mooncake_layerwise_connector.py` file in the `ut` test will be
skipped for now and fixed later.
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
Signed-off-by: MrZ20 <2609716663@qq.com>
### What this PR does / why we need it?
Fix the wrong usage of `model_type`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
By CI.
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
Signed-off-by: nwpu-zxr <zhouxuerong2@huawei.com>
### What this PR does / why we need it?
Initial version to support minimax-m2.5 on vllm-ascend.
This commit coverting original fp8 weight to a quantilized bf16 to
support Minimax-m2.5 on NPU.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
### Test Report
Self tested precision summary, where the official precision score of
AIME2025 is 86.3
<img width="426" height="84" alt="image"
src="https://github.com/user-attachments/assets/a3ce2452-92fa-4713-962e-862248e0b61a"
/>
---------
Signed-off-by: limuyuan <limuyuan3@huawei.com>
Signed-off-by: SparrowMu <52023119+SparrowMu@users.noreply.github.com>
Co-authored-by: limuyuan <limuyuan3@huawei.com>
### What this PR does / why we need it?
Mooncake Layerwise Connector supports hybrid attention manager with
multiple kvcache groups.
### Does this PR introduce _any_ user-facing change?
Yes.
### How was this patch tested?
By CI.
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
---------
Signed-off-by: nwpu-zxr <zhouxuerong2@huawei.com>
### What this PR does / why we need it?
The ops `torch_npu.npu_recurrent_gated_delta_rule` currently does not
support `ssm_state` inputs in float32 format,
we temporarily retain the _forward_core implementation with triton for
Qwen3_5
---------
Signed-off-by: pppeng <zepengliu912@qq.com>
Signed-off-by: pppeng <60355449+ppppeng@users.noreply.github.com>
### What this PR does / why we need it?
Since the PVC files for Guiyang and Hong Kong are not shared, we need to
trigger the download of both regions simultaneously when downloading the
model to ensure that the models in all regions are synchronized.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
This is a bug fix to resolve the issue where the MOE model fails to load
quantized weights in w4a8 format when EP is not enabled.The parameters
["weight_scale_second", "weight_offset_second", "scale_bias"] shall be
parsed in per-group mode, regardless of other conditions.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
Signed-off-by: 李少鹏 <lishaopeng21@huawei.com>
### What this PR does / why we need it?
Fix acceptance and high-concurrency bug in eagle3 and cp enabled
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
tests and ut
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: lilinsiman <lilinsiman@gmail.com>
### What this PR does / why we need it?
1. Increase nightly multi-node test max-parallel from 1 to 2, and fix
resource conflicts that arise when tests run concurrently.
2. Fix parse-trigger job: Add an if condition so it only runs on
schedule, workflow_dispatch, or PRs labeled nightly-test
3. Adjust nightly schedule: Shift trigger time from 24:00 to 23:45
(UTC+8)
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
### What this PR does / why we need it?
New Quantization Method: Introduced support for the W8A8SC static linear
quantization scheme specifically for 310P hardware, enabling more
efficient model compression.
Refactored the save_sharded_state_310.py to avoid multi-process issue.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
W8A8SC quant E2E test.
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: pu-zhe <zpuaa@outlook.com>
### What this PR does / why we need it?
This PR adds comprehensive documentation for the CPU binding feature on
Ascend NPUs. It includes:
- A detailed developer guide
(`docs/source/developer_guide/feature_guide/cpu_binding.md`) covering
the design, internal logic, allocation examples, and troubleshooting for
the CPU binding mechanism.
- A concise user guide
(`docs/source/user_guide/feature_guide/cpu_binding.md`) explaining the
core concepts, usage, and common issues for end-users.
- An update to `additional_config.md` to use consistent terminology for
binding strategies (`global-slicing` and `topo-affinity`).
This documentation is needed to help both developers and users
understand, use, and debug the CPU binding feature, which is critical
for performance on ARM+Ascend platforms.
### Does this PR introduce _any_ user-facing change?
No. This is a documentation-only update.
### How was this patch tested?
The documentation has been reviewed for clarity and technical accuracy.
The examples and descriptions align with the implementation in
`vllm_ascend/cpu_binding.py`.
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: chenchuw886 <chenchuw@huawei.com>
Signed-off-by: c00818886 <chenchuwei@huawei.com>
Co-authored-by: chenchuw886 <chenchuw@huawei.com>
### What this PR does / why we need it?
Fix the LoRA e2e test accuracy issue that introduced by the upstream PR
https://github.com/vllm-project/vllm/pull/32005
### How was this patch tested?
pytest -sv tests/e2e/singlecard/test_llama32_lora.py
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
---------
Signed-off-by: paulyu12 <507435917@qq.com>
Signed-off-by: yupeng <507435917@qq.com>
Set proper 'text/event-stream; charset=utf-8' media type for streaming
requests instead of hardcoded 'application/json'
### What this PR does / why we need it?
This PR fixes an issue in the disaggregated prefill proxy server where
streaming requests (`"stream": true`) were always returned with a
hardcoded `Content-Type: application/json`, even when the backend vLLM
servers correctly returned Server-Sent Events (SSE) with `Content-Type:
text/event-stream; charset=utf-8`.
Specifically, the proxy used `StreamingResponse` with a fixed
`media_type` of `application/json`, which caused FastAPI to override the
response headers and break proper SSE semantics. As a result, clients
(e.g. `curl -i`, EventSource, or OpenAI-compatible SDKs) could not
reliably receive token-by-token streaming output.
In addition, this incorrect response type causes compatibility issues
with benchmarking and load-testing tools such as **EvalScope**. When
streaming is enabled, these tools expect SSE-formatted responses to
correctly parse token usage information. With the incorrect
`application/json` content type, EvalScope fails to parse the response
and reports errors similar to:`2025-12-15 09:27:56 - evalscope - ERROR:
Failed to parse usage from response: list index out of range. Response:
[]`
This PR updates the proxy to:
- Detect whether the incoming request is a streaming request
(`stream=true`)
- Use `text/event-stream; charset=utf-8` for streaming responses
- Preserve `application/json` for non-streaming responses
This aligns the proxy behavior with native vLLM prefill/decoder servers
and the OpenAI-compatible streaming API contract.
Fixes incorrect streaming response headers that prevented proper
real-time token delivery.
### Does this PR introduce _any_ user-facing change?
None
### How was this patch tested?
This change was tested manually using a disaggregated prefill + decode
setup
with the proxy server.
### Test Steps
1. Start prefiller and decoder vLLM servers:
```bash
vllm serve --host 0.0.0.0 --port 8001 ...
vllm serve --host 0.0.0.0 --port 8002 ...
```
2. Start the proxy server:
```bash
python load_balance_proxy_server_example.py \
--host 127.0.0.1 --port 8000 \
--prefiller-hosts 127.0.0.1 --prefiller-ports 8001 \
--decoder-hosts 127.0.0.1 --decoder-ports 8002
```
3. Send a streaming completion request through the proxy:
```bash
curl -i -X POST http://127.0.0.1:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "test",
"prompt": "hello",
"max_tokens": 3,
"stream": true
}'
```
4. Verify the following:
- The response header is Content-Type: text/event-stream; charset=utf-8
- Tokens are streamed incrementally as SSE data: events
- Non-streaming requests still return application/json
No automated tests were added because this change affects an example
proxy
server and is limited to HTTP response headers. The behavior is directly
verifiable using standard SSE-compatible clients.
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
Signed-off-by: zrj026 <zhangrunjiang026@gmail.com>
Co-authored-by: zrj026 <zhangrunjiang026@gmail.com>
## What this PR does / why we need it?
Fixes the broken URL for chunked-prefill in the supported features
documentation page.
The chunked prefill documentation URL was moved from
`performance/optimization.html` to `configuration/optimization.html` in
upstream vLLM docs. This PR updates the link to point to the correct
location.
**Before**:
https://docs.vllm.ai/en/stable/performance/optimization.html#chunked-prefill
(404)
**After**:
https://docs.vllm.ai/en/stable/configuration/optimization.html#chunked-prefill
(working)
## Does this PR introduce _any_ user-facing change?
Yes - fixes a broken documentation link that users encounter when
clicking 'Chunked Prefill' in the supported features page.
## How was this patch tested?
- Verified the new URL resolves correctly
- Documentation change only
Closes#4217
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
Signed-off-by: NJX-njx <3771829673@qq.com>
## What this PR does / why we need it?
This PR addresses issue #5027 where users find that `output.metrics`
returns `None` when using the vLLM offline inference API.
**Root Cause**: vLLM disables log stats by default
(`disable_log_stats=True`), which causes `output.metrics` to be `None`.
**Changes**:
1. Added a NOTE comment in `examples/offline_inference_npu.py`
explaining how to enable metrics
2. Created a new example `examples/offline_inference_metrics.py`
demonstrating how to access request-level metrics (`first_token_time`,
`finished_time`, etc.) by setting `disable_log_stats=False`
## Does this PR introduce _any_ user-facing change?
Yes - adds documentation and example code to help users understand how
to access output metrics.
## How was this patch tested?
- Documentation/example change only
- Verified example code follows the same patterns as existing examples
Closes#5027
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
Signed-off-by: NJX-njx <3771829673@qq.com>
### What this PR does / why we need it?
- move llms.txt under docs/source and publish it at /llms.txt via
html_extra_path
- rewrite llms.txt to an LLM-friendly link index
- use _sources markdown links and include missing entry points such as
FAQs
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
This PR updates the GLM4.x documentation by adding multi-node like 2 ×
Atlas 800 A2 (64G × 8) deployment tutorial.
- **What changed**: Added instructions for deploying GLM-4.X models
across multiple nodes, including environment variables and example
commands.
- **Why needed**: Although the previous tutorial stated that multi-node
deployment on Atlas 800 A2 (64GB × 8) is **not recommended**, but we
still face some situation that must deploy GLM-4.7 on 2 × Atlas 800 A2
(64G × 8). And we successfully run GLM-4.7 on 2 nodes and it works fine,
so we think it might be the time to update this part.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
- Verified that the new documentation renders correctly in Markdown
format.
- Tested the multi-node deployment steps on 2 × Atlas 800 A2 (64G × 8)
to ensure the commands work as described.
- Confirmed that existing GLM4.x documentation links and structure
remain intact.
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
---------
Signed-off-by: ZKSU <zksu@outlook.com>
### What this PR does / why we need it?
fix skiped test_aclgraph_capture_replay.py when upgrade vllm version
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.15.0
- vLLM main:
13397841ab
Signed-off-by: Meihan-chen <jcccx.cmh@gmail.com>
### What this PR does / why we need it?
This pull request introduces support for the Qwen3.5 MoE model on Ascend
devices. The key changes are:
* **Quantization Configuration for Qwen3.5 MoE**: Adds necessary prefix
mappings and packed module definitions for `qwen3_5_moe` in
`vllm_ascend/quantization/modelslim_config.py` to enable ModelSlim
quantization.
* **Triton Kernel Fix**: Corrects a bug in the `fused_gdn_gating` Triton
kernel. The calculation for `BLK_BATCHES` had an operator precedence
issue which is now resolved. The calculation has also been made more
robust with added clamping to prevent potential out-of-bounds memory
access in the unified buffer.
These changes enable the correct and efficient execution of Qwen3.5 MoE
models on Ascend hardware.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
CI should be used to verify the correctness of these changes. It is
recommended to run tests with the Qwen3.5 MoE model to ensure the new
configurations and the kernel fix work as expected.
Signed-off-by: xmpp777 <yangming2@huawei.com>
### What this PR does / why we need it?
Previously implemention of triton rope_siso missing the storage of
second half of rope results, which will result in:
1. accuracy problem in neox-style scenario
2. ub overflow in non neox-style scenario
This PR fixes it and supplement nightly test case for it.
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
Signed-off-by: whx-sjtu <2952154980@qq.com>
Derive MLA dimension constants (q_lora_rank, qk_nope_head_dim, etc.)
from tensor shapes at runtime instead of hardcoding DeepSeek V3 values.
This enables the mla_preprocess fused op to work with both DeepSeek V3
and GLM5 models without Python API changes.
- Add 9 dimension fields to MlaTilingData with DeepSeek V3 defaults
- Add OpParam fields and dynamize all host-side tiling functions
- Derive dimensions from wuk, gamma1, kv_cache_rope tensor shapes
- Replace 310+ hardcoded constants across 4 kernel .hpp files
- Remove unused MMSIZE1/MMSIZE2 constants
### What this PR does / why we need it?
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
---------
Signed-off-by: liuchenbing <chenliumail@163.com>
Co-authored-by: liuchenbing <chenliumail@163.com>
### What this PR does / why we need it?
Support chunked prefill for Qwen3Next with PCP&DCP
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
---------
Signed-off-by: QiuChunshuo <qiuchunshuo@huawei.com>
### What this PR does / why we need it?
If expert_map is on the device, there may be occasional repeated answers
in long output scenarios.
dsv3.2-exp-w8a8
No garbled characters are displayed in the output.
| dataset | version | metric | mode | vllm-api-stream-chat |
|----- | ----- | ----- | ----- | -----|
| aime2025 | ef2f4f | accuracy | gen | 60.00 |
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
### What this PR does / why we need it?
This PR updates the documentation for running vLLM on Atlas 300I series
(310p) hardware. It adds a warning to explicitly set `--max-model-len`
to prevent potential Out-of-Memory (OOM) errors that can occur with the
default configuration.
The example commands and Python scripts for online and offline inference
have been updated to:
- Include `--max-model-len 4096` (or `max_model_len=4096`).
- Remove the `compilation-config` parameter, which is no longer
necessary for 310p devices.
These changes ensure users have a clearer and more stable experience
when using vLLM on Atlas 300I hardware.
### Does this PR introduce _any_ user-facing change?
No, this is a documentation-only update.
### How was this patch tested?
The changes are to documentation and do not require testing.
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: Tflowers-0129 <2906339855@qq.com>
## Summary
- Fix incorrect layer count calculation for MTP (Multi-Token Prediction)
models in `update_aclgraph_sizes()` function
- For MTP models, the draft model's layer count is stored in
`num_nextn_predict_layers` or `mtp_num_hidden_layers` (for Qwen3.5), not
in the standard `num_hidden_layers` field
- Directly accessing `draft.hf_config.num_hidden_layers` returns the
main model's layer count instead of the MTP draft model's layer count
## Bug Description
In `vllm_ascend/utils.py`, the `update_aclgraph_sizes()` function
calculates `resources_per_graph` for speculative decoding scenarios.
When calculating the resources needed for the draft model, the original
code directly accessed:
```python
resources_per_graph += draft.hf_config.num_hidden_layers + 1
```
This works correctly for standard draft models, but **fails for MTP
models** (like DeepSeek-V3's MTP or Qwen3.5's MTP) because:
1. MTP models store their layer count in model-specific fields:
- `num_nextn_predict_layers` (DeepSeek-V3 MTP)
- `mtp_num_hidden_layers` (Qwen3.5 MTP)
2. The `num_hidden_layers` field in these models contains the **main
model's** layer count, not the MTP layer count
3. This leads to **grossly overestimating** the `resources_per_graph`,
which in turn causes the calculated `max_batch_sizes` to be
unnecessarily small
## Fix
Use `draft.get_total_num_hidden_layers()` instead of directly accessing
`draft.hf_config.num_hidden_layers`. This method correctly handles
different model types through the `model_arch_config_convertor`
infrastructure, returning the appropriate layer count for:
- Standard draft models → `num_hidden_layers`
- DeepSeek-V3 MTP → `num_nextn_predict_layers`
- Qwen3.5 MTP → `mtp_num_hidden_layers`
🤖 Generated with [Claude Code](https://claude.com/claude-code)
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
Signed-off-by: wanghuanjun2113 <wanghuanjun2113@gmail.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
### What this PR does / why we need it?
Resolve compilation errors that occur when building versions subsequent
to b020：
Root Cause
During operator compilation, we previously modified the names of structs
HcclOpResParam and HcclRankRelationResV2 in the moe_distribute_base.h
file. After version b020, moe_distribute_base.h was updated with
additional code that references these two structs. This resulted in
compilation errors, as renaming the structs alone broke the newly added
references to them.
Solution
we have added the moe_distribute_base.h file to the operator
implementation. This avoids compilation errors caused by updates to this
file in the CANN framework.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
Signed-off-by: guanguan0308 <1546542263@qq.com>
### What this PR does / why we need it?
We need to support quant config in glm46v
.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
We used the 'Ascend/msit' quantization method to test the w8a8 weights.
Successfully ran on NPU using vllm-ascend by the w8a8 weights.
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
Signed-off-by: g00887675/loganJane <g00887675/loganJane73@hotmail.com>
Co-authored-by: g00887675/loganJane <g00887675/loganJane73@hotmail.com>
### What this PR does / why we need it?
This PR fixes a bug in the `_merge_multimodal_embeddings` function where
the parameter order was incorrect. The `multimodal_embeddings` and
`is_multimodal` parameters were swapped, which would lead to runtime
errors when the function is called with positional arguments.
This change corrects the function signature to align with its expected
usage, ensuring that multimodal embeddings are correctly merged.
### Does this PR introduce _any_ user-facing change?
No. This is a bug fix for an internal utility function and has no
user-facing impact.
### How was this patch tested?
The correctness of this fix is validated by existing tests for
multimodal functionality. With the incorrect function signature, these
tests would fail due to argument type mismatches. CI passing confirms
the fix is effective.
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
Signed-off-by: tanhaoan333 <tanhaoan@huawei.com>
## Summary
- Move `_update_states_after_model_execute` call from after main model
sampling to after draft model execution
- This reordering reduces pipeline bubbles between main model and draft
model execution
- No accuracy impact - the state update operation is independent of
draft token proposal
## Performance Impact
Reduces idle time between main model and draft model execution stages,
improving overall MTP (Multi-Token Prediction) performance.
- vLLM version: v0.15.0
- vLLM main:
83b47f67b1
---------
Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
Co-authored-by: wanghuanjun2113 <wanghuanjun2113@gmail.com>
### What this PR does / why we need it?
Supports contiguous tensor hybrid-attn kv-cache on fullattn-mamba hybrid
model, such as Qwen3Next and Qwen3.5.
Due to the restrictions of Ascend operators, all KV tensors, conv
tensors, and SSM tensors must be contiguous. Therefore, this PR uses the
following solution to generate the KV cache:
tensor1: [(kv_padding), conv , ...]
tensor2: [k , ssm , ...]
tensor3: [v , (mamba_padding), ...]
Under this scheme, although some waste may occur, the tensors of all
caches are guaranteed to be contiguous.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
By CI.
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
---------
Signed-off-by: nwpu-zxr <zhouxuerong2@huawei.com>
### What this PR does / why we need it?
This PR refactors the `tools/collect_user_first_contribution.sh` script
to improve how we track and update our contributors list.
Key changes include:
- **Incremental Updates**: The script can now perform incremental
updates by storing and reading the last processed commit hash from
`docs/source/community/contributors.md`. This is much more efficient
than re-processing all commits every time.
- **Full Refresh Option**: A `--full` flag is added to allow forcing a
full recalculation of all contributors, useful for correcting errors or
initial setup.
- **Improved Usage**: Replaced positional arguments with command-line
flags (`--repo`, `--file`, `--full`) for better usability and clarity.
- **Robust Contributor-ID detection**: Improved logic to find a
contributor's GitHub login, including a fallback to parse it from
`noreply` email addresses.
- **In-place File Updates**: The script now directly updates the
`contributors.md` file with new contributors and correct numbering,
automating the entire process.
These changes make the process of maintaining the contributors list more
automated, reliable, and efficient.
### Does this PR introduce _any_ user-facing change?
No, this only changes a developer tool and does not affect the vLLM
library's public API or behavior.
### How was this patch tested?
The script can be tested locally by running it against the repository.
For an incremental update:
`GITHUB_TOKEN=<your_token> ./tools/collect_user_first_contribution.sh`
For a full refresh:
`GITHUB_TOKEN=<your_token> ./tools/collect_user_first_contribution.sh
--full`
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
The index-select operation `mrope_positions.gpu[:,
:total_num_scheduled_tokens].copy_(...)` triggers a CPU-NPU
synchronization, which blocks subsequent operator dispatch and causes
bubbles visible in Profiling.
This PR changes to full tensor copy
(`mrope_positions.gpu.copy_(mrope_positions.cpu)`) to eliminate the sync
point. The trade-off is a negligible increase in memory usage since
`mrope_positions.cpu` is a small tensor.
**Result:** ~2-3% TPOT improvement with the profiling bubbles
eliminated.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Verified via Profiling that the CPU sync bubble is eliminated and TPOT
is reduced by 2-3%.
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
Co-authored-by: wanghuanjun2113 <wanghuanjun2113@gmail.com>
### What this PR does / why we need it?
Change recurrent_gated_delta_rule ops from triton to ascend C version
for better performance.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- vLLM version: v0.15.0
- vLLM main:
9562912cea
---------
Signed-off-by: SunnyLee219 <3294305115@qq.com>
### What this PR does / why we need it?
This pull request addresses a bug related to the fused mc2 functionality
within the EPLB (Expert Parallelism Load Balancing) system, specifically
impacting quantization and MoE communication.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.15.0
- vLLM main:
83b47f67b1
Signed-off-by: Spicy-Stick <873805887@qq.com>
Signed-off-by: root <root@localhost.localdomain>
### What this PR does / why we need it?
This PR aims to fix incorrect slot mapping in qwen35 due to mismatched
block size. In qwen35, we should use `kernel_block_size` so that we can
compute it in a correct way, and it is obtained in `load_model` when we
have a chance to grab `draft_attn_layers`.
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
Signed-off-by: Zetong Li <slippersss@126.com>
### What this PR does / why we need it?
The community has added a cleaning mechanism for the metadata after the
main model finishes running. The MTP layer should not clean the
metadata, and a new condition has been added to avoid cleaning it.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By ci
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: wangxiaoteng <wangxiaoteng@huawei.com>
### What this PR does / why we need it?
This PR creates and registers `ascend_multi_connector`, which allows the
`mooncake_layerwise_connector` to use the kv_pooling feature.
We unregister the original vllm's `MultiConnector` and replace it with
`AscendMultiConnector` when registering the connectors.
### Does this PR introduce _any_ user-facing change?
No. User can use `MultiConnector` to initialize `AscendMultiConnector`.
### How was this patch tested?
By CI.
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: nwpu-zxr <zhouxuerong2@huawei.com>
### What this PR does / why we need it?
If some `eagle3` model without embed_tokens works with `quarot` target
model, the acceptence rate will drop.
We solve it in this PR.
The relative vllm pr is https://github.com/vllm-project/vllm/pull/36225.
- vLLM main:
4034c3d32e
Signed-off-by: drslark <slarksblood@qq.com>
### What this PR does / why we need it?
GMM custom operator optimization in small batch scenarios
### How was this patch tested?
Submit the GMM custom operator for subsequent integration into the MOE
process.
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
---------
Signed-off-by: chenxi-hh <chen464822955@163.com>
Signed-off-by: chenxi-hh <32731611+chenxi-hh@users.noreply.github.com>
