### 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>
### 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?
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>
### 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?
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?
Support FlashComm1 for Qwen3-Next. Fix some padding problems in Sequence
Parallel (SP)
and resolve precision problems in shared_out when both FlashComm1 is
enabled.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI
- vLLM version: v0.15.0
- vLLM main:
83b47f67b1
---------
Signed-off-by: zhaojiangjiang <zhaojiangjiang1@h-partners.com>
Co-authored-by: zhaojiangjiang <zhaojiangjiang1@h-partners.com>
### What this PR does / why we need it?
**NOTE: This PR is re-pull of #7016 since ci mistakenly marked
unfinished pr as having passed.**
This PR aims to delete mtp_proposer. By fixing a bug in both dsv32 and
glm5, now it should be ok to remove mtp_proposer. The bug is actually
about unnecessary slicing of `slot_mapping`.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
by ci
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: Zetong Li <slippersss@126.com>
### What this PR does / why we need it?
This PR adds split_qkv_rmsnorm_mrope kernel with interleaved for qwen3.5
and qwen3-vl to improve performance.
### Does this PR introduce _any_ user-facing change?
Does not.
### How to use?
```python
real_q, real_k, real_v, real_gate = torch.ops.vllm.triton_split_qkv_rmsnorm_mrope(
qkv=qkv,
q_weight=q_weight,
k_weight=k_weight,
cos_sin=cos_sin,
num_q_heads=num_q_heads,
num_kv_heads=num_kv_heads,
head_size=head_size,
eps=eps,
mrope_section=mrope_section,
is_interleaved=is_interleaved,
rope_dim=rope_dim,
has_gate=has_gate,
)
```
### How was this patch tested?
- vLLM version: v0.16.0
- Accuracy test script：
```shell
pytest tests/e2e/nightly/single_node/ops/singlecard_ops/triton/test_split_qkv_rmsnorm_mrope.py
```
---------
Signed-off-by: Fager <865071616@qq.com>
Signed-off-by: Fager10086 <77871921+Fager10086@users.noreply.github.com>
Signed-off-by: fager <865071616@qq.com>
### What this PR does / why we need it?
Adapt the graph mode (piecewise and full_decode_only) of PCP and DCP for
DeepSeek v3.2.
### How was this patch tested?
Test output:
{"object":"text_completion","model":"deepeek_v3","choices":[{"index":0,"text":"
the head of state and head of government of the United States,
indirectly elected to a four-year term by the American people through
the Electoral College. The officeholder leads the executive branch of
the federal government and is the commander-in-chief of the United
States","logprobs":null,"finish_reason":"length","stop_reason":null,"token_ids":null,"prompt_logprobs":null,"prompt_token_ids":null},{"index":1,"text":"
Paris. This is the largest city in France and its main political,
cultural and commercial center. The modern location of the city is the
north of the central part of the country, on the banks of the Seine
River Seine River Seine in
3\n\n","logprobs":null,"finish_reason":"length","stop_reason":null,"token_ids":null,"prompt_logprobs":null,"prompt_token_ids":null},{"index":2,"text":"
now\n\n# AI future is now\n\nThe world is changing at a rapid pace, and
artificial intelligence (AI) is at the forefront of this transformation.
From self-driving cars to virtual assistants, AI is already making a
significant impact on our daily
lives","logprobs":null,"finish_reason":"length","stop_reason":null,"token_ids":null,"prompt_logprobs":null,"prompt_token_ids":null},{"index":3,"text":"
a 3rd year student at the University of Lincoln studying Media
Production. This blog is about my work throughout my final year on the
course.\n\n## Tuesday 3 May 2016\n### Final Major Project -
Evaluation\n\nFor my final project
I","logprobs":null,"finish_reason":"length","stop_reason":null,"token_ids":null,"prompt_logprobs":null,"prompt_token_ids":null}],"service_tier":null,"system_fingerprint":null,"usage":{"prompt_tokens":27,"total_tokens":227,"completion_tokens":200,"prompt_tokens_details":null},"kv_transfer_params":null}
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
---------
Signed-off-by: xiaocongtou6 <2066962956@qq.com>
Signed-off-by: xiaocongtou6 <105542647+xiaocongtou6@users.noreply.github.com>
### What this PR does / why we need it?
Add e2e test cases for the Qwen-VL model adaptation to Ascend 310p
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
Signed-off-by: gcw_61wqY8cy <wanghengkang1@huawei.com>
### What this PR does / why we need it?
This PR optimizes the `split_qkv_rmsnorm_rope` operator by introducing a
new Triton kernel, `split_qkv_rmsnorm_rope_prefill_kernel`, for the
prefill stage (i.e., large batch sizes). The implementation now
dynamically selects between the existing decode kernel and the new
prefill kernel based on the batch size, which improves performance for
large batch scenarios.
Additionally, the RoPE implementation is updated to support partial
rotation dimensions (`rope_dim`), making the operator more flexible.
### Does this PR introduce _any_ user-facing change?
No. This is a performance optimization and is not expected to introduce
any user-facing changes.
### How was this patch tested?
CI should pass with existing tests. The new prefill path is triggered
when the batch size is larger than the number of available vector cores.
The partial RoPE feature can be tested by passing the `rope_dim`
argument.
- vLLM version: v0.15.0
- vLLM main:
83b47f67b1
---------
Signed-off-by: guzhiyong <guzhiyong5@h-partners.com>
Signed-off-by: frank <2547457096@qq.com>
Co-authored-by: guzhiyong <guzhiyong5@h-partners.com>
### What this PR does / why we need it?
This PR aims to delete mtp_proposer. By fixing a bug in both dsv32 and
glm5, now it should be ok to remove mtp_proposer. The bug is actually
about unnecessary slicing of `slot_mapping`.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
by ci
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
---------
Signed-off-by: Zetong Li <slippersss@126.com>
## Problem
When MTP is enabled, prefill requests with `prompt_tokens ==
num_spec_tokens + 1` are incorrectly classified as decode requests,
causing accuracy issues.
## Root Cause
The `uniform_decode` condition only checked:
- `max_num_scheduled_tokens == uniform_decode_query_len`
- `num_tokens == max_num_scheduled_tokens * num_reqs`
This is insufficient because a prefill request with specific prompt
length satisfies these conditions as well.
## Fix
Add `is_all_decode` check to ensure all requests have
`num_computed_tokens > 0` before classifying as uniform decode, since
decode requests must have computed at least one token.
- vLLM version: v0.15.0
- vLLM main:
83b47f67b1
---------
Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
### What this PR does / why we need it?
This PR refactors the nightly CI workflows (A2 and A3) to support
running tests against a specific PR's code, in addition to the existing
scheduled/dispatch runs using pre-built images.
#### Motivation:
Previously, nightly tests could only be triggered by schedule or
workflow_dispatch, always using the pre-built nightly image. This change
allows developers to trigger nightly tests against their own PR's source
code, enabling early validation without waiting for a nightly build.
#### Changes
Trigger logic (parse-trigger job)
A new parse-trigger job is introduced in both
schedule_nightly_test_a2.yaml and schedule_nightly_test_a3.yaml to
centralize trigger evaluation:
`schedule / workflow_dispatch`: runs all tests with the pre-built image
(existing behavior preserved)
`pull_request (labeled + synchronize)`: runs only when:The PR has the
nightly-test label, and /nightly [test-names] comment exists (latest one
wins)
1. /nightly or /nightly all — runs all tests
2. /nightly test1 test2 — runs only named tests (comma-wrapped for exact
matching)
#### How to trigger
1. Add the nightly-test label to your PR
2. Comment /nightly (all tests) or /nightly test1 test2 (specific tests)
4. Re-triggering: add another /nightly comment and push a new commit
(synchronize event)
### Does this PR introduce _any_ user-facing change?
None
### How was this patch tested?
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
### What this PR does / why we need it?
fix test_qwen3_moe_external_launcher_ep_tp2 by
wait_until_npu_memory_free
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
## What this PR does / why we need it?
Implements [RFC
#6954](https://github.com/vllm-project/vllm-ascend/issues/6954):
NPUWorker Profiler profile_prefix full adaptation for API parity with
upstream vLLM.
### Changes
- **Lazy profiler init**: Defer profiler creation until first
`profile(is_start=True)` call
- **profile_prefix param**: Add `profile_prefix` to `profile()`; compute
`trace_name` from prefix + `get_worker_rank_suffix()`
- **Refactor `_init_profiler` → `_create_profiler(trace_name)`**: Pass
`worker_name` to `tensorboard_trace_handler` for unique trace files per
worker
- Unique trace files per worker; no collision in multi-worker setups
### Testing
- Unit tests updated/added in `tests/ut/worker/test_worker_v1.py`
- `pytest tests/ut/worker/test_worker_v1.py::TestNPUWorker` passed
## Does this PR introduce _any_ user-facing change?
Yes. Trace file naming may differ (more descriptive with worker rank
suffix). `profile(is_start=True, profile_prefix="warmup")` now
supported.
## How was this patch tested?
- Unit tests:`pytest tests/ut/worker/test_worker_v1.py::TestNPUWorker`
- Manual: vLLM serve with profiler config, start/stop profile, verified
trace files
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
---------
Signed-off-by: realliujiaxu <realliujiaxu@163.com>
This pull request refactors the speculative decoding proposer interface
to align with upstream vLLM, removing the local `Proposer` interface and
renaming methods to `propose`.
This is the first step. In the future we should remove the class
register and just add few Ascend specified method once the arch in vLLM
is ready.
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
This PR add docs of batch invariance and make some extra operators
according to validation result.
please see https://github.com/vllm-project/vllm-ascend/issues/5487 to
track progress.
### 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: Ronald1995 <ronaldautomobile@163.com>
### What this PR does / why we need it?
Add DeepSeek-V3.2 nightly ci
Fix PD routing to exclude headless nodes when collecting
prefiller/decoder IPs
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
### What this PR does / why we need it?
This PR introduces global CPU slicing for Ascend NPUs to ensure
non-overlapping CPU partitions, addresses IRQ binding logical errors on
A3, and enhances the logic for determining total NPUs in CPU allocation.
These changes are necessary to optimize CPU resource management and
improve system stability.
- **Global CPU Slicing**: Introduced a global CPU slicing mechanism for
Ascend NPUs to ensure non-overlapping CPU partitions across multiple
processes or data parallel groups, preventing resource contention.
- **Improved IRQ Binding for A3 Devices**: Refined the IRQ binding logic
specifically for Ascend A3 devices, correctly mapping logical NPU IDs to
physical card and chip IDs for accurate npu-smi queries and preventing
multi-process overwrite of IRQ settings.
- **Enhanced NPU Count Determination**: Improved the logic for
determining the total number of logical NPUs, prioritizing NPU mapping
information to ensure more accurate CPU allocation.
- **Minimum CPU Requirement**: Established a minimum requirement of 5
CPUs per NPU for binding, reserving specific cores for IRQ, main, ACL,
and release operations to ensure stable operation.
### Does this PR introduce _any_ user-facing change?
No user-facing changes are introduced.
### How was this patch tested?
CI passed with new added/existing tests.
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
---------
Signed-off-by: c00818886 <chenchuwei@huawei.com>
### What this PR does / why we need it?
- This PR fixes an issue with weight format conversion for unquantized
models running on Ascend 310P devices.
- The changes refactor the logic for converting weights to the
FRACTAL_NZ format. Previously, this was handled in a 310P-specific
linear layer implementation (`AscendUnquantizedLinearMethod310`). This
implementation has been removed, and the logic is now centralized in the
`maybe_trans_nz` utility function. This function now checks if the
device is a 310P and applies the NZ format cast accordingly for
`float16`/`bfloat16` weights.
- This refactoring simplifies the code by removing platform-specific
duplication and ensures correct weight handling for unquantized models
on 310P.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
ut and local test
- vLLM version: v0.15.0
- vLLM main:
83b47f67b1
---------
Signed-off-by: Tflowers-0129 <2906339855@qq.com>
### What this PR does / why we need it?
[CI] Upgrade CANN to 8.5.1
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed with existing test.
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
Signed-off-by: wxsIcey <1790571317@qq.com>
### What this PR does / why we need it?
This pull request introduces significant enhancements for 310P device
support, primarily by enabling W8A8S quantization and facilitating the
saving of models with W8A8SC state outputs. It provides an example
script for saving sharded and compressed model states, implements the
core W8A8S quantization method, and integrates metadata generation
within the 310P worker to accurately describe the quantization types of
saved parameters. These changes aim to improve efficiency and
compatibility for quantized models on 310P hardware.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
W8A8S accuarcy test and W8A8SC states save.
<img width="886" height="184" alt="image"
src="https://github.com/user-attachments/assets/e9bcac54-1f69-4d3a-a5b8-221a147ef99d"
/>
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
---------
Signed-off-by: pu-zhe <zpuaa@outlook.com>
### What this PR does / why we need it?
Add muls_add triton kernel with related fusion pass. What's more, this
PR refactors `AscendCompilationConfig` and delete `NpugraphExConfig`.
### Does this PR introduce _any_ user-facing change?
None
### How was this patch tested?
CI passed with new added test.
- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1
---------
Signed-off-by: whx-sjtu <2952154980@qq.com>
### What this PR does / why we need it?
Support platform.get_device_uuid function.
currently, the pytorch.npu.get_device_properties return uuid as full
zero, vllm-ascend implement the interface at first, once the
pytorch.npu.get_device_properties return the real uuid, vllm-ascend will
support without modification.
more details see
https://github.com/vllm-project/vllm-ascend/issues/6669
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- vLLM version: v0.15.0
- vLLM main:
9562912cea
root@localhost:/workspace/l00614971/vllm_test# python vllm_test.py
INFO 02-24 09:43:48 [__init__.py:43] Available plugins for group
vllm.platform_plugins:
INFO 02-24 09:43:48 [__init__.py:45] - ascend -> vllm_ascend:register
INFO 02-24 09:43:48 [__init__.py:48] All plugins in this group will be
loaded. Set `VLLM_PLUGINS` to control which plugins to load.
INFO 02-24 09:43:48 [__init__.py:217] Platform plugin ascend is
activated
device_uuid = 00000000-0000-0000-0000-000000000000
---------
Signed-off-by: liziyu <liziyu16@huawei.com>
Signed-off-by: wangxiaoteng <wangxiaoteng@huawei.com>
Signed-off-by: luomin2005 <luomin2005@huawei.com>
Co-authored-by: liziyu <56102866+liziyu179@users.noreply.github.com>
Co-authored-by: wangxiaoteng <wangxiaoteng@huawei.com>
### What this PR does / why we need it?
Add nightly test for Qwen3-235B-A22B with mooncake layerwise connector.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: release/v0.13.0
- vLLM main:
81786c8774
---------
Signed-off-by: wjunLu <wjunlu217@gmail.com>
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
Co-authored-by: hfadzxy <starmoon_zhang@163.com>
### What this PR does / why we need it?
[Refactor][EAGLE] 7/N Merged PCP and disable_padded interface into
eagle_proposer.py
This pull request significantly refactors the speculative decoding
mechanism by merging Parallel Context Processing (PCP) and Multi-Token
Prediction (MTP) functionalities directly into the eagle_proposer.py.
The changes aim to enhance the efficiency and correctness of distributed
speculative decoding, particularly by enabling the Eagle feature to work
seamlessly with the disable_padded interface. This involves detailed
adjustments to attention metadata, input/output processing, and state
management to ensure proper operation in parallel environments.
1. The PCP and MTP features are migrated to the eagle_proposer.py
2. The Eagle and PCP features are integrated
3. Enable the eagle feature to use the disable_padded interface
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Tests and UT
- vLLM version: v0.15.0
- vLLM main:
83b47f67b1
---------
Signed-off-by: lilinsiman <lilinsiman@gmail.com>
### What this PR does / why we need it?
This PR fixes a `Stale file handle` error that occurs during doctests in
the CI environment. The error appears when loading models from
ModelScope, likely due to issues with network file systems used in CI.
The fix involves setting the `MODELSCOPE_HUB_FILE_LOCK` environment
variable to `false` in the `run_doctests.sh` script. This disables file
locking in the ModelScope hub, which is a common workaround for this
type of file system error.
### Does this PR introduce _any_ user-facing change?
No, this change only affects the CI test execution environment and has
no impact on users.
### How was this patch tested?
This change is validated by the CI pipeline. A successful run of the
doctests indicates that the fix is effective.
Signed-off-by: leo-pony <nengjunma@outlook.com>
### What this PR does / why we need it?
This pull request resolves an attention accuracy issue by enhancing the
AttentionMaskBuilder310 to correctly handle the maximum model length.
The change ensures that the attention mask generation process is
properly parameterized by the model's configuration, rather than relying
on a fixed internal value. This leads to more accurate attention mask
creation, which is crucial for the correct functioning of the attention
mechanism.
Update fused_moe to main branch.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Qwen3 dense mode & moe model e2e test
- vLLM version: v0.15.0
- vLLM main:
83b47f67b1
---------
Signed-off-by: pu-zhe <zpuaa@outlook.com>
## 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?
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?
- 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>
