## Summary
- Add automatic quantization format detection, eliminating the need to
manually specify `--quantization` when serving quantized models.
- The detection inspects only lightweight JSON files
(`quant_model_description.json` and `config.json`) at engine
initialization time, with no `.safetensors` reads.
- User-explicit `--quantization` flags are always respected;
auto-detection only applies when the flag is omitted.
## Details
**Detection priority:**
1. `quant_model_description.json` exists → `quantization="ascend"`
(ModelSlim)
2. `config.json` contains `"quant_method": "compressed-tensors"` →
`quantization="compressed-tensors"` (LLM-Compressor)
3. Neither → default float behavior
**Technical approach:**
Hooked into `NPUPlatform.check_and_update_config()` to run detection
after `VllmConfig.__post_init__`. Since `quant_config` is already `None`
at that point, we explicitly recreate it via
`VllmConfig._get_quantization_config()` to trigger the full quantization
initialization pipeline.
## Files Changed
| File | Description |
|------|-------------|
| `vllm_ascend/quantization/utils.py` | Added
`detect_quantization_method()` and `maybe_auto_detect_quantization()` |
| `vllm_ascend/platform.py` | Integrated auto-detection in
`check_and_update_config()` |
| `vllm_ascend/quantization/modelslim_config.py` | Improved error
handling for weight loading |
- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd
---------
Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
### What this PR does / why we need it?
This version has no divisibility constraint between tp and mtp+1.
However, cudagraph_capture_sizes must be a common multiple of tp and
mtp+1, with a maximum of tp * (mtp+1). Therefore, we fixed
cudagraph_capture_sizes.
We added a long-sequence test (64k input, 3k output) for the two-node
mixed deployment scenario. Due to the excessive time required for
performance benchmarking, we are only verifying functionality. The
single-node scenario is skipped because VRAM limitations prevent
launching the model with a max-model-len of 68,000.
and we also add aime2025 test for dual-node deepseek 3.2 nightly test.
### How was this patch tested?
test at nightly environment.
- vLLM version: v0.15.0
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.15.0
Signed-off-by: guozr <guozr1997@hotmail.com>
Co-authored-by: guozr <guozr1997@hotmail.com>
### What this PR does / why we need it?
New FIA operator requires queryT equal to the last element of
actualSequenceLengthQ.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Passed existing test (test_mtp_eagle_correctness.py).
- vLLM version: v0.15.0
- vLLM main:
9562912cea
---------
Signed-off-by: Wangbingjie <wangbj1207@126.com>
Signed-off-by: Wangbingjie <w30061490@china.huawei.com>
Co-authored-by: Wangbingjie <w30061490@china.huawei.com>
[Refactor] Modify the binding logic, added memory migration and
interrupt core binding functions.
### What this PR does / why we need it?
Controls the use of memory on a closer NUMA node to achieve a lower
memory access latency, while binding interrupts to different CPU cores
to prevent them form interrupting the inference process.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
b8eaaa073b
Signed-off-by: rowzwel_dx <1392851715@qq.com>
Signed-off-by: Rozwel-dx <1392851715@qq.com>
- vLLM version: v0.15.0
- vLLM main:
9562912cea
Signed-off-by: Rozwel-dx <1392851715@qq.com>
### What this PR does / why we need it?
Currently, the performance of multi-modal encoding (i.e.,
`AscendMMEncoderAttention` forward) is considerably bounded by the heavy
host pre-process operations.
We can see from the profiling results below, before the real computation
of Attention, there are long free time in the device, which will lead to
extremely low NPU utilization.
<img width="2264" height="1398" alt="iShot_2026-01-23_16 26 39"
src="https://github.com/user-attachments/assets/37f21d06-e526-4f28-82fe-005746cf13bd"
/>
---
**To opitimize this, this PR has proposed four changes:**
1. Use `seq_lens` CPU cache to avoid frequent d2h copy. Before this PR,
`AscendMMEncoderAttention` will copy the `cu_seqlens` from NPU to CPU in
every forward, since the op `_npu_flash_attention_unpad()` requires CPU
`cu_seqlens` (otherwise it will crash). Thus, we use
`seq_lens_cpu_cache` to cache this tensor, since it's shared between all
layers, but may change in different forward step. When the current
`layer_index` is `0`, we update the cache, otherwise we directly use the
cache to avoid frequent `diff` and `copy` operations, which are costful.
2. Pre-compute the scale value to avoid calculating it in every forward.
3. Move the judgment of `enable_pad` from forward to the `__init__`
method.
4. Revert https://github.com/vllm-project/vllm-ascend/pull/6204.
**Performance after these optimizations:**
- **TTFT** has been reduced by **7.43%** ⬇️.
- **Throughput** has been increased by **1.23%** ⬆️.
---
> [!NOTE]
> This PR requires https://github.com/vllm-project/vllm/pull/33674 be
merged.
---
### 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 \
--no-async-scheduling
```
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 500 \
--request-rate 10 \
--burstiness 5 \
--no-stream
```
Before this PR:
```
============ Serving Benchmark Result ============
Successful requests: 500
Failed requests: 0
Request rate configured (RPS): 10.00
Benchmark duration (s): 82.23
Total input tokens: 33418
Total generated tokens: 61543
Request throughput (req/s): 6.08
Output token throughput (tok/s): 748.45
Peak output token throughput (tok/s): 3203.00
Peak concurrent requests: 402.00
Total token throughput (tok/s): 1154.86
---------------Time to First Token----------------
Mean TTFT (ms): 10275.37
Median TTFT (ms): 6297.88
P99 TTFT (ms): 22918.26
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 263.02
Median TPOT (ms): 277.61
P99 TPOT (ms): 483.56
---------------Inter-token Latency----------------
Mean ITL (ms): 257.31
Median ITL (ms): 94.83
P99 ITL (ms): 1773.90
==================================================
```
After this PR:
```
============ Serving Benchmark Result ============
Successful requests: 500
Failed requests: 0
Request rate configured (RPS): 10.00
Benchmark duration (s): 81.20
Total input tokens: 33418
Total generated tokens: 61509
Request throughput (req/s): 6.16
Output token throughput (tok/s): 757.54
Peak output token throughput (tok/s): 2562.00
Peak concurrent requests: 395.00
Total token throughput (tok/s): 1169.11
---------------Time to First Token----------------
Mean TTFT (ms): 9511.91
Median TTFT (ms): 5479.78
P99 TTFT (ms): 21427.21
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 261.12
Median TPOT (ms): 276.03
P99 TPOT (ms): 446.99
---------------Inter-token Latency----------------
Mean ITL (ms): 254.04
Median ITL (ms): 97.71
P99 ITL (ms): 1516.67
==================================================
```
- vLLM version: v0.15.0
- vLLM main:
dc917cceb8
Signed-off-by: shen-shanshan <467638484@qq.com>
### What this PR does / why we need it
This PR introduces the **first AI-assisted model-adaptation skill
package** for `vllm-ascend`.
The goal is to make model adaptation work (especially for recurring
feature-request issues) **repeatable, auditable, and easier to hand
off**.
### Scope in this PR
This PR adds only skill/workflow assets under:
- `.agents/skills/vllm-ascend-model-adapter/SKILL.md`
-
`.agents/skills/vllm-ascend-model-adapter/references/workflow-checklist.md`
-
`.agents/skills/vllm-ascend-model-adapter/references/troubleshooting.md`
-
`.agents/skills/vllm-ascend-model-adapter/references/multimodal-ep-aclgraph-lessons.md`
-
`.agents/skills/vllm-ascend-model-adapter/references/fp8-on-npu-lessons.md`
- `.agents/skills/vllm-ascend-model-adapter/references/deliverables.md`
### Workflow improvements
The skill standardizes:
1. **Environment assumptions** used in our Docker setup
- implementation roots: `/vllm-workspace/vllm` and
`/vllm-workspace/vllm-ascend`
- serving root: `/workspace`
- model path convention: `/models/<model-name>`
2. **Validation strategy**
- Stage A: fast `--load-format dummy` gate
- Stage B: mandatory real-weight gate before sign-off
- avoid false-ready by requiring request-level checks (not startup log
only)
3. **Feature-first verification checklist**
- ACLGraph / EP / flashcomm1 / MTP / multimodal
- explicit `supported / unsupported / not-applicable /
checkpoint-missing` outcomes
4. **Delivery contract**
- minimal scoped code changes
- required artifacts (Chinese report + runbook, e2e config YAML,
tutorial doc)
- one signed commit in delivery repo
### What this PR does NOT do
- No runtime/kernel/model patch is included in this PR.
- No direct model support claim is made by this PR alone.
- Model-specific adaptation/fix work should be submitted in follow-up
PRs using this skill as the workflow baseline.
### Why this matters for maintainers
This gives the repo a shared, explicit AI-assistance protocol, so future
model-adaptation PRs are easier to review, compare, and reproduce.
---------
Signed-off-by: QwertyJack <7554089+QwertyJack@users.noreply.github.com>
Co-authored-by: QwertyJack <7554089+QwertyJack@users.noreply.github.com>
### What this PR does / why we need it?
This PR adds a new document, `AGENTS.md`, which provides detailed
development guidelines for contributors to the vLLM Ascend project.
These guidelines cover code style, testing, NPU-specific considerations,
and the contribution process to ensure code quality and consistency.
### Does this PR introduce _any_ user-facing change?
No, this is a documentation-only update for developers.
### How was this patch tested?
This is a documentation change and does not require testing.
- vLLM version: v0.15.0
- vLLM main:
83b47f67b1
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
In order to adapt to the GLM model, logits were passed in the sample,
which can cause accuracy issues in version 0.15.0.
- vLLM version: v0.15.0
- vLLM main:
83b47f67b1
Signed-off-by: GDzhu01 <809721801@qq.com>
### What this PR does / why we need it?
fix(mtp): resolve MTP core bugs and enhance eager mode test cases
1. Resolved critical issues in eager mode MTP core execution logic;
2. Fixed functional bugs in the _update_states_after_model_execute
function;
3. Updated and released test_mtp_qwen3_next.py to validate eager mode
acceptance rate.
### Does this PR introduce _any_ user-facing change?
None
### How was this patch tested?
- vLLM version: v0.15.0
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.15.0
Signed-off-by: Bowen-Leee <caoshankuangren@gmail.com>
### What this PR does / why we need it?
This PR supports the Kimi-K2.5 models on the NPU of bf16 and w4a8
weights.
The corresponding PR in the vllm community has been merged:
https://github.com/vllm-project/vllm/pull/34501
### Does this PR introduce _any_ user-facing change?
- No.
### How was this patch tested?
We test the Kimi-K2.5 weights. The weights path:
https://modelscope.cn/models/Eco-Tech/Kimi-K2.5-W4A8
Successfully ran on 910B NPU using vllm-ascend by the w4a8 weights.
- vLLM version: v0.15.0
- vLLM main:
9562912cea
---------
Signed-off-by: LoganJane <LoganJane73@hotmail.com>
### What this PR does / why we need it?
change num_commmon_tokens to num_common_tokens in
vllm_ascend/_310p/model_runner_310p.py，which caused CI test failure
- vLLM version: v0.15.0
- vLLM main:
9562912cea
Signed-off-by: 01267596 <xiongkai123@cmbchina.com>
Co-authored-by: 01267596 <xiongkai123@cmbchina.com>
### What this PR does / why we need it?
- New section: "Request Forwarding" documentation in
docs/source/tutorials/models/DeepSeek-V3.2.md
- Environment fix: Changed VLLM_ASCEND_ENABLE_FLASHCOMM1 from 0 to 1 in
the DeepSeek-V3 configuration examples
### Does this PR introduce _any_ user-facing change?
Documentation update only - provides new configuration guidance for
request forwarding setups
### How was this patch tested?
- vLLM version: v0.15.0
- vLLM main:
9562912cea
---------
Signed-off-by: guozr <guozr1997@hotmail.com>
Co-authored-by: guozr <guozr1997@hotmail.com>
### What this PR does / why we need it?
This PR fixes a bug in `vllm_ascend/worker/model_runner_v1.py` where the
`@torch.inference_mode` decorator was used without parentheses. Using
the decorator without instantiation is deprecated and may not correctly
disable gradient calculations, leading to performance degradation and
increased memory usage during inference. This change adds the required
parentheses to ensure `torch.inference_mode` is applied correctly.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
The change is a minor syntax correction. Existing CI tests should cover
this.
- vLLM version: v0.15.0
- vLLM main:
9562912cea
Signed-off-by: gcanlin <canlinguosdu@gmail.com>
### What this PR does / why we need it?
1. Following https://github.com/vllm-project/vllm/pull/32322, use the
`memory_profiling` context manager from vllm for profiling.
2. Fix wrong non-torch memory value recorded during profiling, which is
not its peak during inference.
---
**More details about point 2:**
After profling, the non-torch memory value we recorded is lower than
that in real inference. This is mainly because of the different memory
management behaviour between `torch.cuda.empty_cache()` and
`torch.npu.empty_cache()`.
With regard to `torch.cuda.empty_cache()`, it only recycle the unused
memory in pytorch memory pool (i.e., memory managed by pytorch caching
allocator), **with no affect to non-torch memory**. However, as for
`torch.npu.empty_cache()`, it has a totally different memory management
mechanism, i.e., it may call `aclrtSynchronize` and **enable Ascend
runtime to free up non-torch memory**.
Thus, the non-torch memory value we recorded after
`torch.npu.empty_cache()` is much lower than its peak during profling.
Resolution:
We record the peak non-torch memory value
(`non_torch_memory_before_empty_cache`) after profiling, but before
`torch.npu.empty_cache()`. Then, we add the diff
(`non_torch_memory_cleared_by_empty_cache =
non_torch_memory_before_empty_cache - self.non_torch_memory`) to
non-torch memory when calculating available KV cache memory, which will
lead to less KV cache memory (i.e., it's safer to avoid OOM issues).
---
> [!NOTE]
> This PR needs to wait for main2main aligning to latest vllm commit
before merging.
### Does this PR introduce _any_ user-facing change?
no.
### How was this patch tested?
Before this PR, the non-torch memory we used to calculate available KV
cache memory is **0.90 G**, whereas its peak during real inference is
**1.08 G**, diff: **182.00 M**.
After this PR, we add this diff to non-torch memory after profiling and
thus make the profiling results more accurate.
- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd
---------
Signed-off-by: shen-shanshan <467638484@qq.com>
### What this PR does / why we need it?
- Keeps enable_cpu_binding default on, but skips binding on non‑ARM CPUs
inside bind_cpus, with a clear log.
- Uses a table-driven binding policy: A3 uses NUMA‑balanced binding;
other device types use NUMA‑affinity binding.
- Updates docs to reflect the exact behavior and adds/updates unit tests
for the new logic.
### Does this PR introduce _any_ user-facing change?
- Yes. CPU binding is now enabled by default via additional_config, and
documented in the user guide.
- CPU binding behavior differs by device type (A3 vs. others).
### How was this patch tested?
Added/updated unit tests:
test_cpu_binding.py
1. test_binding_mode_table covers A2 vs A3 binding mode mapping.
2. test_build_cpu_pools_fallback_to_numa_balanced covers fallback when
affinity info is missing.
3. TestBindingSwitch.test_is_arm_cpu covers ARM/x86/unknown arch
detection.
4. test_bind_cpus_skip_non_arm covers non‑ARM skip path in bind_cpus.
test_worker_v1.py
1. Updated mocks for enable_cpu_binding default True to align with new
config default.
- vLLM version: v0.14.1
- vLLM main: d7de043
---------
Signed-off-by: chenchuw886 <chenchuw@huawei.com>
Co-authored-by: chenchuw886 <chenchuw@huawei.com>
### What this PR does / why we need it?
The ds3.2 model adaptation supports the PCP feature.
The solution is as follows: When saving the KV cache, first perform an
allgather operation on the KVs, and then each node saves its own copy.
When the attention or indexer performs calculations, they all gather the
KV cache and then perform the calculations.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
02/12 23:05:10 - AISBench - INFO - Running 1-th replica of evaluation
02/12 23:05:10 - AISBench - INFO - Task [vllm-api-general-chat/gsm8k]:
{'accuracy': 96.35416666666667, 'type': 'GEN'}
02/12 23:05:10 - AISBench - INFO - time elapsed: 2.87s
02/12 23:05:12 - AISBench - INFO - Evaluation tasks completed.
02/12 23:05:12 - AISBench - INFO - Summarizing evaluation results...
dataset version metric mode vllm-api-general-chat
gsm8kdataset - accuracy gen 96.35
- vLLM version: v0.15.0
- vLLM main:
9562912cea
---------
Signed-off-by: weiguihua2 <weiguihua2@huawei.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
Update release note & support matrix to add experimental tag for
features and models.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.15.0
- vLLM main:
9562912cea
0.13.0 branch: https://github.com/vllm-project/vllm-ascend/pull/6751
Signed-off-by: zzzzwwjj <1183291235@qq.com>
### What this PR does / why we need it?
This PR extends the Ascend 310P attention backend to support the
`PrefillCacheHit` state. Previously, only `PrefillNoCache`,
`DecodeOnly`, and `ChunkedPrefill` were supported.
This PR handles this state by routing it to the existing
`forward_chunked_prefill_310` implementation, which is suitable for this
scenario.
The changes also include refactoring the main `forward_impl` dispatch
method for better clarity and updating unit tests to cover the new state
and ensure correctness.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Accuracy test when chunked prefill is disabled.
- vLLM version: v0.15.0
- vLLM main:
9562912cea
---------
Signed-off-by: pu-zhe <zpuaa@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?
<!--
- 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 #
-->
Fix wrong computed_tokens when meet exception. This pull request
addresses a bug in the KV transfer mechanism where an exception during
token lookup operations could lead to an incorrect count of
computed_tokens. By modifying the exception handling in both the lookup
and lookup_scheduler functions to return 0 instead of the start index,
the system now correctly indicates that no tokens were successfully
processed when a remote connection failure occurs. This enhancement
improves the robustness and accuracy of token management within the
vllm_ascend distributed KV pool.
### 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.
If tests were not added, please describe why they were not added and/or
why it was difficult to add.
-->
Signed-off-by: xleoken <xleoken@163.com>
### What this PR does / why we need it?
#6043 deleted the forward_before phase of the dynamic eplb. Currently,
the end-to-end precision is monitored in the UT, and the log is not
printed in the key place. As a result, the eplb does not take effect and
is not intercepted.
1. The forward_before function is added back.
2. Delete unnecessary logs and add key logs.
3. Warm-up of algorithm 3 is added.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
#### The conversation is normal.
Okay, the user is asking, \"What is deep learning?\" I need to explain
this in a clear and concise way. Let me start by recalling what I know
about deep learning. It's a subset of machine learning, right? So first,
I should mention that it's part of machine learning, which itself is a
branch of AI. Then, the key aspect of deep learning is the use of neural
networks with multiple layers. These are called deep neural
networks.\n\nWait, I should define neural networks first. Maybe start
with the basics. A neural network is inspired by the human brain, with
layers of nodes (neurons) that process data. But deep learning
specifically refers to networks with many layers—hence \"deep.\" So the
term \"deep\" comes from the number of layers. \n\nI should explain how
deep learning works. It involves training these networks on large
datasets, allowing them to automatically learn features from the data.
Unlike traditional machine learning, where you might have to manually
extract features, deep learning models can do this automatically. That's
a key point. For example, in image recognition, a deep learning model
can learn to detect edges, shapes, and then more complex patterns
without human intervention.\n\nApplications are important too. The user
might want to know where deep learning is used. Common examples include
image and speech recognition, natural language processing, autonomous
vehicles, and recommendation systems. Maybe mention specific
technologies like self-driving cars using computer vision or virtual
assistants like Siri or Alexa
- vLLM version: v0.15.0
- vLLM main:
13397841ab
Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
### What this PR does / why we need it?
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.15.0
- vLLM main:
9562912cea
Signed-off-by: leo-pony <nengjunma@outlook.com>
### What this PR does / why we need it?
1. add description of another version of glm5-w4a8 weight
2. update the introduction of installation
3. introduce a script to enable bf16 MTP
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
N/A
- vLLM version: v0.15.0
- vLLM main:
9562912cea
---------
Signed-off-by: yydyzr <liuyuncong1@huawei.com>
### What this PR does / why we need it?
The incorrect regular expression syntax `.*[UE4M3|ue4m3].*` actually
ignores all words containing any of the following characters: `u, e, 4,
m, 3, |`
```yaml
extend-ignore-identifiers-re = [".*Unc.*", ".*_thw",
".*UE8M0.*", ".*[UE4M3|ue4m3].*", ".*eles.*", ".*fo.*", ".*ba.*",
".*ot.*", ".*[Tt]h[rR].*"]
```
===fix===>
```yaml
extend-ignore-identifiers-re = [".*Unc.*", ".*_thw",
".*UE8M0.*", ".*(UE4M3|ue4m3]).*", ".*eles.*", ".*fo.*", ".*ba.*",
".*ot.*", ".*[Tt]h[rR].*"]
```
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.15.0
- vLLM main:
9562912cea
Signed-off-by: MrZ20 <2609716663@qq.com>
### What this PR does / why we need it?
The basic configs are extracted and reused for eplb UT. This is done so
that if the basic configs are changed later, eplb UT does not need to be
modified repeatedly.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.15.0
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.15.0
Signed-off-by: bigsir007 <xujiacheng12@huawei.com>
Co-authored-by: bigsir007 <xujiacheng12@huawei.com>
### What this PR does / why we need it?
1. Fix a vec error caused by unaligned UB accesss in the
DispatchFFNCombine;
2. Fix expert_token_nums tensor defined on host instead of NPU in
moe_comm_method.py
3. Fix multi-core copy issue of expert_token_nums in dispatchffnCombine
op (single aiv copy is sufficient)
### Does this PR introduce _any_ user-facing change?
No, this PR does not introduce any user-facing changes. The fix only
addresses internal memory access logic and does not modify any public
APIs, interfaces, or user-visible behaviors.
### How was this patch tested?
`export VLLM_ASCEND_ENABLE_FUSED_MC2=1`
vLLM version: v0.15.0
- vLLM version: v0.15.0
- vLLM main:
9562912cea
Signed-off-by: xulei_ict <xulei292@huawei.com>
Co-authored-by: xulei_ict <xulei292@huawei.com>
### What this PR does / why we need it?
If users run bash format.sh with `git bash` on windows system, there
exists `Executable /bin/bash not found` error. This is because in
Windows Git Bash environment, the Bash executable is actually located at
`/usr/bin/bash`, while the `/bin` directory may not exist, or may be
just an empty directory or a broken symlink that does not contain bash.
### Does this PR introduce _any_ user-facing change?
None
### How was this patch tested?
With this PR and `pre-commit` installed, windows coders can directly run
`bash format.sh` to clean lint issues.
- vLLM version: v0.15.0
- vLLM main:
9562912cea
Signed-off-by: whx-sjtu <2952154980@qq.com>
NZ Format Support for Linear Layers: Implemented support for the NZ
(N-dimensional Z-order) format for linear layer weights on Ascend 310P,
enhancing performance for both quantized and unquantized layers.
Unquantized Linear Method for Ascend 310P: Introduced
AscendUnquantizedLinearMethod310 to specifically handle and apply NZ
format casting to unquantized linear layer weights during the loading
process.
MRotaryEmbedding Integration: Extended Rotary Embedding support by
adding AscendMRotaryEmbedding310 to provide an Ascend-specific
implementation for MRotaryEmbedding.
Quantization Method Updates: Updated the w8a8_static quantization method
to directly transpose weights and apply NZ format casting, ensuring
consistency with the new format.
- vLLM version: v0.15.0
- vLLM main:
9562912cea
---------
Signed-off-by: Tflowers-0129 <2906339855@qq.com>
### What this PR does / why we need it?
This PR integrates the `npu_add_rms_norm` fused kernel for RMSNorm
operations with residual connections on 310P devices. This change
optimizes the computation by replacing a two-step process (manual
residual addition followed by RMSNorm) with a single, more efficient
fused operation. This is needed to improve the performance of models
utilizing RMSNorm with residual connections on the 310P architecture.
Fixes #
### Does this PR introduce _any_ user-facing change?
No, this PR introduces an internal optimization and does not change any
user-facing APIs or behaviors.
### How was this patch tested?
This patch was tested with updated unit tests
(`test_RMSNorm_forward_310p`) that mock the `npu_add_rms_norm` operation
to verify the correctness of the fused kernel integration.
---------
Signed-off-by: Tflowers-0129 <2906339855@qq.com>
### What this PR does / why we need it?
Integrating inductor pass and npugraph ex pass, see RFC:
https://github.com/vllm-project/vllm-ascend/issues/6347
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
all tests passed.
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: wxsIcey <1790571317@qq.com>
### What this PR does / why we need it?
This PR adapts bugfixes from `norm_quant_fusion_pass` to
`graphex_norm_quant_fusion_pass` for the `npugraph_ex` backend.
The main changes are:
- Replaced `torch.ops.npu.npu_add_rms_norm` with
`torch.ops._C_ascend.npu_add_rms_norm_bias`.
- For patterns without bias, `None` is passed as the bias argument.
- For patterns with bias, the separate `add` operation for bias is
removed and the bias is passed directly to `npu_add_rms_norm_bias`. This
improves fusion.
These changes ensure consistency and correctness for RMSNorm and
quantization fusion patterns when using `npugraph_ex`.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.15.0
- vLLM main:
9562912cea
Signed-off-by: huyuanquan1 <huyuanquan1@huawei.com>
Co-authored-by: huyuanquan1 <huyuanquan1@huawei.com>
### What this PR does / why we need it?
Added support for A2 in the GLM-5 doc.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
vLLM version: v0.15.0
vLLM main:
9562912cea
- vLLM version: v0.15.0
- vLLM main:
9562912cea
### What this PR does / why we need it?
mooncake layerwise support pcp function
PCP (Prefill Context Parallelism) Support: Introduced explicit support
for Prefill Context Parallelism (PCP) and Decode Context Parallelism
(DCP) in the Mooncake layerwise KV cache transfer mechanism, allowing
for more granular control and awareness of parallel configurations
during data transfer.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By ci
- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd
---------
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?
refer to https://github.com/vllm-project/vllm-ascend/issues/6391,
Currently adapted the complete process of event publishing in vllm:
* `kv_connector_model_runner_mixin` invoke kv-connector
`get_kv_connector_kv_cache_events` func to collect kvevents
* in `scheduler.py` , it's `update_from_output` func will invoke
`_update_from_kv_xfer_finished` which invoke
`connector.update_connector_output` to collect kv-events from all
kv-worker, and then scheduler will invoke `connector.take_events` api to
collect all kv-events and add it to the events which from
`kv_cache_manager`
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
You can add `--kv-events-config` parameter to the `vllm server` command
to enable this feature.
- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd
---------
Signed-off-by: yejj710 <abyss1999@163.com>
Co-authored-by: fems14 <1804143737@qq.com>
### What this PR does / why we need it?
1. support ND format gmm weight input.
Before this pr, gmm1_weight and gmm2_weight could only be passed as
input to the DispatchGmmCombineDecode operator in NZ data format. After
the modification, they are allowed to be passed in ND data format.
2. support bf16/float16 gmm weight
The current PR modification enables the DispatchGmmCombineDecode
operator to support non-W8A8 scenarios, allowing gmm1_weight and
gmm2_weight to be passed as float16/bfloat16 which is correspond with
input token data type.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
Signed-off-by: lih827 <383084552@qq.com>
### What this PR does / why we need it?
vllm model runner v2 use uva buffer to prepare input data, but npu
doesn't support uva yet, this pr implement a uvawrapper class to mimic
gpu's uva backend. what's more, this pr make some modifications to adapt
to the newer main branch.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
- vLLM main:
13397841ab
---------
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
### What this PR does / why we need it?
Fix the issue where, in graph mode, the fused `AddRMSNormQuant` operator
does not take effect when there is no bias.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd
---------
Signed-off-by: ZYang6263 <zy626375@gmail.com>
This pull request refines the GLM-5 deployment documentation by updating
the Docker run command to include a more comprehensive set of device
mappings and by removing an extraneous quantization flag from the `vllm
serve` commands. These changes aim to correct and clarify the deployment
instructions, ensuring users can successfully set up and run the GLM-5
model as intended.
- vLLM version: v0.15.0
- vLLM main:
9562912cea
Signed-off-by: Canlin Guo <961750412@qq.com>
### What this PR does / why we need it?
This pull request enables the `npugraph_ex` backend by default to
improve performance on Ascend NPUs, as proposed in the
[RFC](https://github.com/vllm-project/vllm-ascend/issues/6214).
### Does this PR introduce _any_ user-facing change?
Yes. `npugraph_ex` is now enabled by default. Users can disable it by
setting `enable: false` in the `npugraph_ex_config` section of the
`additional_config`.
### How was this patch tested?
CI passed. The changes are covered by existing and new E2E tests
(`test_aclgraph_accuracy.py`) and unit tests (`test_ascend_config.py`)
that have been updated to reflect the new default behavior. The tests
verify correctness and consistency with `npugraph_ex` enabled and
disabled, as well as with the new static kernel option.
Signed-off-by: huyuanquan1 <huyuanquan1@huawei.com>
Co-authored-by: huyuanquan1 <huyuanquan1@huawei.com>
### What this PR does / why we need it?
Add GLM5 doc
### 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: nakairika <982275964@qq.com>
### What this PR does / why we need it?
GLM5 adaptation
1. use torch_npu.npu_lightning_indexer for GLM5
2. forbid eagle proposer when fullgraph mode is enabled because of bugs
3. add quatization config for GLM5
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
by ci
- vLLM main:
978a37c823
---------
Signed-off-by: yydyzr <liuyuncong1@huawei.com>
Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
Co-authored-by: shenchuxiaofugui <1311027364@qq.com>
### What this PR does / why we need it?
This PR aims to update `target_probs` to `target_logits` in
`rejection_sample`, for catching up with
https://github.com/vllm-project/vllm/pull/32852. Otherwise, sampling
with temperature will incur accuracy problem where tokens can be
accepted or rejected unreasonably.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
by ci
- vLLM version: v0.15.0
- vLLM main:
13397841ab
Signed-off-by: Zetong Li <slippersss@126.com>
