### What this PR does / why we need it?
This PR aims to fix padding logic in eagle proposer for kimi25. Main
changes involve:
1. modify the way to obtain draft model attention builder and backend
2. add block table padding & related tensor slicing in common metadata
when `draft_step>1` for solving fia verifying error
3. replace block table in `update_graph_params` for solving fia
verifying error
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
Signed-off-by: Zetong Li <slippersss@126.com>
### What this PR does / why we need it?
1. upgrade to 0.18.0
2. ensure kernel_block_sizes is int for Eagle drafter
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.17.0
- vLLM main:
8b6325758c
---------
Signed-off-by: Meihan-chen <jcccx.cmh@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?
This PR enables separate attention backend configuration for target and
draft models in speculative decoding, decoupling the previously bound
attention backend settings between the two models.
It solves the compatibility issue where some draft models do not support
the attention backend used by the target model, and allows users to
select the optimal attention backend for each model individually to
maximize inference performance. The change is fully backward compatible.
---------
Signed-off-by: SidaoY <1024863041@qq.com>
### What this PR does / why we need it?
Refactor `vllm_ascend/ops/fused_moe` to replace scattered MoE business
`**kwargs` with typed request objects and explicit stage boundaries.
- Prepare, dispatch, MLP, and quant stages now have clearer ownership.
- Main MoE path no longer depends on business `kwargs.get(...)` lookups.
- Comm and dispatcher interfaces are request-only on the main path.
- UTs can assert stage-level fields directly instead of inferring
behavior indirectly.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
CI passed.
---------
Signed-off-by: linfeng-yuan <1102311262@qq.com>
### What this PR does / why we need it?
Replace the '_npu_flash_attention_unpad' operator with the
'npu_fusion_attention' operator to ensure that the Qwen VL model can run
in the A5 environment and remove the 'mrope' operator call restriction
for A5.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
Signed-off-by: 汪越 <wangyue361@h-partners.com>
### What this PR does / why we need it?
1. Extracting duplicated code into a method.
That is defining _get_input_parallel_ in parent class
_CustomRowParallelOp_, and call the helper method in its 5 children
classes :
- MLPRowParallelOp
- OProjRowParallelOp
- Flashcomm2OProjRowParallelOp
- MatmulAllreduceRowParallelOp
- SequenceRowParallelOp
's _apply_impl_ method
2. Variable typo fixing: split instead of splitted for the past tense
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
Signed-off-by: idouba <zhangchaomeng@huawei.com>
### What this PR does / why we need it?
This PR adapts the `w8a8_mxfp8` quantization method to support Qwen
Vision-Language (VL) models. Key changes include:
- Reshaping multi-dimensional input tensors to 2D before the quantized
matrix multiplication.
- Reshaping the 2D output back to its original multi-dimensional format.
- Adding specific output reshaping for the visual components of Qwen VL
models.
- Casting the bias tensor to `float32` to comply with the
`npu_quant_matmul` kernel requirements.
These changes are necessary to enable `w8a8_mxfp8` quantization for
models with multi-modal inputs like Qwen VL.
### Does this PR introduce _any_ user-facing change?
No, this is a backend enhancement to extend quantization support to new
model architectures. There are no user-facing API or behavior changes.
### How was this patch tested?
CI is expected to pass. Manual testing should be performed with a Qwen
VL model using `w8a8_mxfp8` quantization to verify correctness and
performance.
- vLLM version: v0.17.0
- vLLM main:
4497431df6
---------
Signed-off-by: ksiyuan <ksiyuan@umich.edu>
### What this PR does / why we need it?
pr: https://github.com/vllm-project/vllm/pull/37136 break eplb because
it filters out redundant experts.
pr: https://github.com/vllm-project/vllm/pull/37322 fix it due to use
parallel_config.enable_eplb to determine whether to skip the weight
loading filter.
But in vllm-ascend, parallel_config.enable_eplb is always false. When we
use eplb, we temporarily set it to true.
### 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.
-->
### How was this patch tested?
| dataset | version | metric | mode | vllm-api-stream-chat |
|----- | ----- | ----- | ----- | -----|
| aime2024 | 604a78 | accuracy | gen | 86.67 |
Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
### What this PR does / why we need it?
This PR introduces a new fused Triton kernel,
`split_qkv_tp_rmsnorm_rope` for Minimax-m2.5.
The implementation includes two Triton kernels:
1. `_split_qkv_and_compute_local_qk_var_kernel`: Splits the QKV input
and computes the local variance for RMSNorm.
2. `_apply_global_rmsnorm_kernel`: Applies global RMSNorm (considering
TP all-reduce for variance) and Neox-style RoPE.
### Does this PR introduce _any_ user-facing change?
Does not.
### How was this patch tested?
```python
pytest tests/e2e/nightly/single_node/ops/singlecard_ops/triton/test_split_qkv_tp_rmsnorm_rope.py
```
### Test Data
A3 TP16
基线
| data | TTFT(ms) | TPOT(ms) | TPS |
|------------|---------:|---------:|-------:|
| 4k/1k@bs1 | 267.55 | 25.5 | 38.85 |
| 4k/1k@bs4 | 542.4 | 26.51 | 148.06 |
测试线
| data | TTFT(ms) | TPOT(ms) | TPS |
|------------|---------:|---------:|-------:|
| 4k/1k@bs1 | 234.64 | 20.96 | 47.24 |
| 4k/1k@bs4 | 508.36 | 22.16 | 176.69 |
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
Signed-off-by: xutianyi <xutianyi5@huawei.com>
Co-authored-by: xutianyi <xutianyi5@huawei.com>
### What this PR does / why we need it?
This PR fixes the layer name mapping logic in `AscendModelSlimConfig`
for quantization config loading.
1. **kimi_k2 model layer name mapping issue**: The `kimi_k2` model has a
unique layer naming convention that differs from the standard
`hf_to_vllm` mapping. One layer was defined in the mapper but was not
being correctly applied, causing quantization config lookup failures.
2. **Manual mapping registration timing issue**: The manual mapping
check in `apply_vllm_mapper` was executed before `vllm_config` was
initialized, causing `model_type` to be unavailable. This prevented some
models with manual mappings from being correctly registered.
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
Tested with `kimi_k2` model to verify the special layer name mapping
works correctly. Also tested with other models that have manual mappings
defined in `QUANT_MODEL_PREFIX_MAPPINGS` to ensure the registration
timing fix works properly.
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: Matrix_K <zhangke144@huawei.com>
Signed-off-by: Feng-xiaosuo <tengchang1@huawei.com>
Co-authored-by: Matrix_K <zhangke144@huawei.com>
Co-authored-by: Wang Kunpeng <1289706727@qq.com>
### What this PR does / why we need it?
remove deprecated environment variables related to MLP prefetching
### Does this PR introduce _any_ user-facing change?
yes, the deprecated env vars can not be used then.
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
1. Mamba Cache Support on 310P: Implemented logic to correctly
initialize and allocate KV cache for Mamba models on the 310P platform,
including handling of state tensors and page size alignment.
2. Increased Attention Head Size Support: Modified the attention backend
to support attn_head_size larger than 128 by dynamically selecting
appropriate kernel block sizes based on hardware limitations (e.g.,
block_size * head_size <= 16384).
3. Refactored KV Cache Allocation: Consolidated and improved the KV
cache allocation mechanism, moving from separate size calculation and
allocation steps to a unified _allocate_kv_cache_tensors method that
handles both Attention and Mamba specific cache structures.
4. Dynamic Mamba Config Patching: Introduced conditional loading of
Mamba configuration patches, specifically using patch_mamba_config_310
for the 310P platform to ensure platform-specific optimizations and
validations.
5. Reserve reasonable memory to allocate KV cache to avoid OOM issue
with default gpu_memory_utilization.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Qwen3.5 E2E test
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: pu-zhe <zpuaa@outlook.com>
### What this PR does / why we need it?
1.fix "TypeError: get_attn_backend() remove variable": [Refactor
`check_and_update_config`](https://github.com/vllm-project/vllm/pull/35122)
2.fix [Rename `compile_ranges_split_points` to
`compile_ranges_endpoints`](https://github.com/vllm-project/vllm/pull/36027)
3.fix "RuntimeError: device_allocator not a DeviceAllocator":[Replace
memory related torch.cuda
APIs"](https://github.com/vllm-project/vllm/pull/37031)
4.fix [Support multiple KV groups in OffloadingSpec
](https://github.com/vllm-project/vllm/pull/36610) removed
self.offloaded_block_size and changed self.gpu_block_size from a scalar
to a tuple of per-group block sizes, adding block_size_factor.
5.fix [Consolidate
SupportsEagle](https://github.com/vllm-project/vllm/pull/36063) renamed
get_eagle3_aux_hidden_state_layers() to
get_eagle3_default_aux_hidden_state_layers() and added a
supports_eagle3() guard before calling it.
### Does this PR introduce _any_ user-facing change?
NA
### How was this patch tested?
E2E
- vLLM version: v0.17.0
- vLLM main:
8a680463fa
---------
Signed-off-by: leo-pony <nengjunma@outlook.com>
Co-authored-by: Claude Code <noreply@anthropic.com>
### What this PR does / why we need it?
Adapt to the model type of Qwen3-VL-8B-Instruct-W8A8
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: betta18 <jiangmengyu1@huawei.com>
Co-authored-by: betta18 <jiangmengyu1@huawei.com>
### What this PR does / why we need it?
This PR fixes the logger initialization in patches so that the log info
can be displayed as expected.
### Does this PR introduce _any_ user-facing change?
No.
- vLLM version: v0.17.0
- vLLM main:
4497431df6
---------
Signed-off-by: Angazenn <supperccell@163.com>
### What this PR does / why we need it?
The rotary algorithm in deepseek indexer should be neox-style instead of
gptj style. PR #4641 fix this accuracy bug in original pytorch version.
But PR #5701 accidentally removed the fixed code line and reverted the
implementation back to the problematic version. This PR fixes it.
Signed-off-by: rjg-lyh <1318825571@qq.com>
### What this PR does / why we need it?
LayerwiseConnector supports the virtual push functionality on node D.By
adding a do_virtual flag to request metadata, the system can now
identify and process certain requests virtually, bypassing the actual KV
cache transfer process. This allows for immediate completion of these
requests from the consumer's perspective, potentially enabling
optimizations or specific testing scenarios where physical data transfer
is not required.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By ci
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: wangxiaoteng <wangxiaoteng@huawei.com>
### What this PR does / why we need it?
#### Problem
When decode node enables prefix cache and the local prefix cache fully
hits, the following assertion error occurs:
```
(EngineCore_DP3 pid=34912) File "/usr/local/python3.11.14/lib/python3.11/site-packages/vllm/v1/engine/core.py", line 520, in step_with_batch_queue
(EngineCore_DP3 pid=34912) engine_core_outputs = self.scheduler.update_from_output(
(EngineCore_DP3 pid=34912) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore_DP3 pid=34912) File "/usr/local/python3.11.14/lib/python3.11/site-packages/vllm/v1/core/sched/scheduler.py", line 1520, in update_from_output
(EngineCore_DP3 pid=34912) self._update_from_kv_xfer_finished(kv_connector_output)
(EngineCore_DP3 pid=34912) File "/usr/local/python3.11.14/lib/python3.11/site-packages/vllm/v1/core/sched/scheduler.py", line 2120, in _update_from_kv_xfer_finished
(EngineCore_DP3 pid=34912) assert RequestStatus.is_finished(req.status)
(EngineCore_DP3 pid=34912) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore_DP3 pid=34912) AssertionError
```
The error is triggered in scheduler.py at _update_from_kv_xfer_finished:
```
if req.status == RequestStatus.WAITING_FOR_REMOTE_KVS:
self.finished_recving_kv_req_ids.add(req_id)
else:
assert RequestStatus.is_finished(req.status)
```
#### Root Cause
When decode node has prefix cache enabled and local prefix cache fully
hits:
1. get_num_new_matched_tokens returns ext_tokens=0, load_kv_async=False
when decode prefix cache fully hits
2. Request status becomes RUNNING (not WAITING_FOR_REMOTE_KVS)
3. However, update_state_after_alloc still adds the request to
_reqs_need_recv because remote_block_ids exists in kv_transfer_params
4. Worker processes the request in _handle_request:
- _transfer_kv_cache returns immediately (no actual transfer,
local_block_ids is empty)
- finally block still calls update_done_task_count(request_id)
5. finished_recving contains this request
6. When _update_from_kv_xfer_finished processes finished_recving,
request status is RUNNING
7. Assertion fails
#### Solution
In _handle_request, only notify scheduler (update_done_task_count) when
actual KV transfer happened (local_block_ids is not empty). The signals
to notify Prefill to release KVCache
(_send_done_signal_to_free_remote_port and _send_done_recv_signal) are
still sent regardless.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
Signed-off-by: LCAIZJ <leichao139636@163.com>
### What this PR does / why we need it?
Some bug fixes, mainly including:
1. For A2, the number of experts each single card cannot be greater than
16 when using MC2. The PR fixed the error in the A2 moe communication
method selection, which would cause the selection of an incorrect
communication method when the number of model experts exceeds 256. For
example, when using an A2 16-cards model to load the PD-disaggregation D
node with Qwen3.5 series models, the incorrect MC2 method would be
chosen.
2. Fixed the issue where the layerwise connector sends the kv-cache of
the MTP layer multiple times when `num_spec_tokens` > 1. Now, the
kv-cache is sent only when the MTP layer is forward for the first time.
3. Fix the accuracy issue of qwen3.5 when using MTP for PD
disaggregation. The cause is that `num_decode_draft_tokens` does not
consider that `spec_tokens` are not existed during the first inference
when PD disaggregation (`spec_tokens` are generated during the first
inference). However, `spec_tokens_padding` is added by
`recomputed_scheduler`. As a result, `gdn_metadata` incorrectly
considers that the prefill with a length of 2 is performed.
---------
Signed-off-by: nwpu-zxr <zhouxuerong2@huawei.com>
Signed-off-by: zxr2333 <64738772+nwpu-zxr@users.noreply.github.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
### What this PR does / why we need it?
Qwen3.5 Moe supports enabling the dispatch_ffn_combine fusion operator.
Fix problem: In the w8a8 quantization scene, Qwen3.5 model's config.json
lacks the quantize field. The previous logic strictly relied on
quant_type == "w8a8_dynamic" to enable VLLM_ASCEND_ENABLE_FUSED_MC2.
This caused the dispatch_ffn_combine fusion operator to fail to activate
even when the environment variable was set.
Enable dispatch_ffn_combine fusion operator for BF16 scenarios.
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: asunxiao <asunxiao@qq.com>
### What this PR does / why we need it?
This PR optimizes bias handling in `AscendRMSNorm` without changing the
intended
functional behavior.
In the current implementation, bias may be initialized for
`AscendRMSNorm` based
on configuration-level detection, even though some norm layers never
actually
load a bias weight. This can cause the inference path to enter the bias
branch
and execute an unnecessary `add_` operator.
To improve this, this PR introduces a loader-based flag to record
whether the
bias has actually been loaded. The bias addition is then executed only
when the
bias is truly present.
This optimization reduces redundant computation in inference and makes
the bias
application logic better aligned with the actual model weights.
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
Signed-off-by: rjg-lyh <1318825571@qq.com>
### What this PR does / why we need it?
This PR fixes the bug for eagle3 and cp enable introduced by the
parallel speculative inference PR.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
tests and ut
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: lilinsiman <lilinsiman@gmail.com>
Co-authored-by: kunpengW-code <1289706727@qq.com>
Co-authored-by: linsheng1 <1950916997@qq.com>
### What this PR does / why we need it?
Currently, chunked prefill is forcibly enabled. DeepSeek V3.1 W8A8C8
supports only the PD separation scenario. C8 refers to quantizing the KV
cache to int8, which aims to reduce the GPU memory usage of the KV cache
and improve the inference throughput.
Constraints:
1. Only the PD separation mode can be used and
MooncakeLayerwiseConnector can be used to run the model.
2. Currently, only the activation value supports dynamic quantization,
and the KV cache supports static quantization. C8 quantization with MTP
is not supported. You can use ModelSlim for quantization. The
quantization procedure is as follows:
pip install transformers==4.48.2
git clone https://gitcode.com/Ascend/msmodelslim.git
cd msmodelslim
bash install.sh
cd example/DeepSeek/
python3 quant_deepseek_w8a8.py --model_path <path/weight> --save_path
<path/quant_weight>
--anti_dataset../common/deepseek_anti_prompt_50_v3_1.json
--calib_dataset../common/deepseek_calib_prompt_50_v3_1.json --rot
--trust_remote_code True --fa_quant --dynamic --anti_method m6
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: pichangping <1337510399@qq.com>
Signed-off-by: Wang Kunpeng <1289706727@qq.com>
Co-authored-by: Wang Kunpeng <1289706727@qq.com>
### What this PR does / why we need it?
Two problems have been solved in this pr.
These problems occur in the `FULL_DECODE_ONLY` mode that `num_tokens`
should be padded to some value in `cudagraph_capture_sizes`.
1. We found the length of `seq_lens_list` in drafter's `attn_metadata`
is 1 shorter than expected. It will raise a kernel exception to make
vllm crash.
e.g., `num_reqs` = 3, `cudagraph_capture_sizes` = [20],
`actual_seq_lengths_q` is padded well to [4, 8, 12, 20]. But
`seq_lens_list` = [5742, 4700, 7996], it is not padded.
3. Though the length of `seq_lens_list` in target's `attn_metadata` is
the same as expected in `FULL_DECODE_ONLY`, some data are corrupted at
the end of the list.
e.g., `num_reqs` = 3, `cudagraph_capture_sizes` = [20],
`actual_seq_lengths_q` is padded well to [4, 8, 12, 20]. But
`seq_lens_list` = [5742, 4700, 7996, 5738], it has corrupted at the end
of the list.
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
Signed-off-by: drslark <slarksblood@qq.com>
### What this PR does / why we need it?
This PR fixes a bug in Xlite
backend(https://atomgit.com/openeuler/GVirt/issues/3).
This PR adds support for mrope (Mixture-of-RoPE) and deepstack features
in the xlite backend. These features are necessary for running certain
multimodal models that utilize them.
The main changes include:
- Updating `_build_model_config` to parse mrope and deepstack
configurations from the model's `hf_config`.
- Modifying `XliteWrapper.__call__` to handle `deepstack_input_embeds`
and mrope positions during the model forward pass.
- Replacing `ModelAttnMeta` with the newer `AttnMeta` to accommodate the
new metadata fields required by these features.
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
online server config:
```
python -m vllm.entrypoints.openai.api_server \
--model /mnt/nvme0n1/models/checkpoint-8200 \
--additional-config='{"xlite_graph_config": {"enabled": true}}' \
--tensor-parallel-size 4 \
--gpu-memory-utilization 0.9 \
--max-num-batched-tokens 8192 \
--max-num-seqs=20 \
--block-size 128 \
--max-model-len 8192 \
--trust-remote-code \
--served-model-name Qwen3-VL-8B \
--host localhost \
--generation-config vllm \
--port 6777
```
test_config:
```
vllm bench serve \
--max-concurrency ${maxconcurrency} \
--num-prompts ${num_prompts} \
--host ${HOST} \
--port ${PORT} \
--model ${MODEL_NAME} \
--dataset-name random \
--backend openai-chat \
--random-input-len 512 \
--random-output-len 512 \
--random-range-ratio 0.2 \
--temperature 0.6 \
--metric-percentiles "50,90,99" \
--tokenizer ${TOKENIZER_PATH} \
--endpoint /v1/chat/completions \
--ignore-eos
```
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
Signed-off-by: LVYANGGUO <lvyangguo@huawei.com>
Co-authored-by: LVYANGGUO <lvyangguo@huawei.com>
### What this PR does / why we need it?
Optimize the performance of the triton operator _topk_log_softmax_kernel
in model_runner_v2 to 1.04xH100，which is 7% of its original value.(issue
https://github.com/vllm-project/vllm-ascend/issues/5208)
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: wangx700 <wangxin700@huawei.com>
### What this PR does / why we need it?
This PR restores #7029, which adds W8A8C8 support for dsv3.2/glm5 using
the `lightning_indexer_quant` ops in the pd-mix stage.
The original PR was reverted by #7288 because the patch did not work
with the recompute scheduler.
This PR also fixes the patching issue so that it works correctly with
the recompute scheduler.
### Does this PR introduce _any_ user-facing change?
Yes. To enable LI C8, users need to set the `enable_sparse_c8` option to
`"true"` in `additional_config`.
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: rjg-lyh <1318825571@qq.com>
### What this PR does / why we need it?
**Refactor: Replace npu_ring_mla with FIA in MLA prefill**
This PR refactors the MLA (Multi-Layer Attention) prefill implementation
by replacing `npu_ring_mla` with `npu_fused_infer_attention_score` (FIA)
operator, unifying the attention backend with the standard attention
implementation.
**Key changes:**
1. **Core prefill refactoring (`mla_v1.py`)**
- Replace `npu_ring_mla` with `npu_fused_infer_attention_score` in
`_forward_prefill` and `_compute_prefill_context`
- Use TND layout with `softmax_lse_flag=True` for prefill attention
- Use `npu_attention_update` to merge multiple chunk outputs with LSE
(Log-Sum-Exp)
- Change `attn_mask` from `get_final_mla_mask()` to
`get_splitfuse_attn_mask()` for FIA compatibility
2. **Data type handling**
- Add automatic float16 → bfloat16 conversion (FIA with TND layout only
supports bfloat16)
- Convert output back to original dtype after FIA computation
3. **Metadata optimization**
- Pre-calculate `actual_seq_lengths_q` in `AscendMLAPrefillMetadata`
- Pre-calculate `chunk_actual_seq_lengths_kv_list` in
`ChunkedContextMetadata`
- Move `torch.cumsum` operations from forward pass to metadata building
phase
4. **CP compatibility (`mla_cp.py`)**
- Add `_ring_mla_mask_builder` to get `npu_ring_mla`-compatible masks
for Context Parallel scenarios
- Add `chunk_actual_seq_lengths_kv_list` field to
`CPChunkedContextMetadata`
**Why we need it:**
- **Backend unification**: Aligns MLA prefill with standard attention
implementation (`attention_v1.py`)
- **Better chunked context support**: FIA + `npu_attention_update`
provides native LSE-based output merging
- **Future compatibility**: Prepares for eventual `npu_ring_mla` removal
across the codebase
### Does this PR introduce _any_ user-facing change?
**No.** This is a pure refactoring with no functional changes - same
behavior, unified backend.
---
- Related issue: #5463 (item 7)
- vLLM version: v0.14.1
Signed-off-by: lico67373 <918688502@qq.com>
### What this PR does / why we need it?
This reverts commit 7ed9e9de69, which
introduces an issue that the patch doesn't work with recompute scheduler
enabled.
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
Fix the error that reports while initializing qwen3-reranker-0.6b model
with `--enable-lora`.
And add a testcase to verify the fix.
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: paulyu12 <507435917@qq.com>
Co-authored-by: Mengqing Cao <cmq0113@163.com>
### What this PR does / why we need it?
To support prefix cache for Qwen3.5/Next in vLLM-Ascend, this PR mainly
follows the design in
[#30877](https://github.com/vllm-project/vllm/pull/30877) and inherits
changes to functions which are overridden in vLLM-Ascend.
Note:
1. `--mamba-cache-mode align` && PD disaggregation is still not
supported yet in vLLM v0.17.0(see
https://github.com/vllm-project/vllm/blob/main/vllm/v1/core/sched/scheduler.py#L295).
2. The current implementation of hybrid kv cache might result in a very
large block_size when scheduling. For example, if we run Qwen3.5-35B-A3B
with `-tp 2`, the block_size is adjusted to 2048, which means that any
prefix shorter than 2048 will never be cached. Although this behavior is
consistent with vLLM, it still needs improvements in the future.
3. `--mamba-cache-mode align` requires to copy mamba states during
forward steps. vLLM uses a triton kernel to implement it. However, the
original version run into some bugs on Ascend hardwares. Thus we patch a
new triton kernel to avoid this bug.
### Does this PR introduce _any_ user-facing change?
To use mamba prefix cache, set `--enable-prefix-caching` and
`--mamba-cache-mode align`. Note that the mamba state copy function(see
[do_mamba_copy_block](https://github.com/vllm-project/vllm/blob/main/vllm/v1/worker/mamba_utils.py#L132))
does not provide a torch native version, thus it might have trouble if
users can't use triton.
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: Angazenn <supperccell@163.com>
### What this PR does / why we need it?
Conditional Import for Mooncake: The import of
mooncake.engine.TransferEngine was moved into a try-except block within
the GlobalTE class's constructor. This ensures that mooncake is only
imported when needed and provides a clear error message with
installation instructions if it's missing.
### Does this PR introduce _any_ user-facing change?
The error message "ascend_transport.so: cannot open shared object file:
No such file or directory" in the CI is fixed to ensure the normal
running of the CI.
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: 房建伟 <fangjianwei@fangjianweideMacBook-Air.local>
Co-authored-by: 房建伟 <fangjianwei@fangjianweideMacBook-Air.local>
### What this PR does / why we need it?
This pr fix the Unified draft parallel feature.
1. In Draft model proposer, there are exceed 1 attention layers in
target model, thus removing the assertion on layer number.
2. we should get block size through `draft_attn_groups` instead of
`attn_metadata_builder` after 0.17.0.
3. `attn_update_stack_num_spec_norm` shouldn't be done when unified
draft parallel is enabled
### How was this patch tested?
Test pass with
`tests/e2e/singlecard/spec_decode/test_v1_spec_decode.py::test_parallel_drafting_acceptance`,
which is already included in CI
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
Reapply the auto-detect quantization format feature (originally in
#6645, reverted in #6873) and extend it to support remote model
identifiers (e.g., `org/model-name`).
Changes:
- Reapply auto-detection of quantization method from model files
(`quant_model_description.json` for ModelSlim, `config.json` for
compressed-tensors)
- Add `get_model_file()` utility to handle file retrieval from both
local paths and remote repos (HuggingFace Hub / ModelScope)
- Update `detect_quantization_method()` to accept remote repo IDs with
optional `revision` parameter
- Update `maybe_update_config()` to work with remote model identifiers
- Add platform-level `auto_detect_quantization` support
- Add unit tests and e2e tests for both local and remote model ID
scenarios
Closes#6836
### Does this PR introduce _any_ user-facing change?
Yes. When `--quantization` is not explicitly specified, vllm-ascend will
now automatically detect the quantization format from the model files
for both local directories and remote model IDs.
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
### What this PR does / why we need it?
LMCache-Ascend is LMCache's solution on the Ascend platform and one of
the KVCache pooling solutions for Ascend. We hope to integrate
LMCache-Ascend into the vLLM-Ascend community as one of the official
KVCache pooling solutions for vLLM-Ascend.
We added a new LMCacheAscendConnector in vLLM-Ascend and registered it.
### Does this PR introduce _any_ user-facing change?
Users can specify the kvconnector using `--kv-transfer-config`, allowing
them to freely choose which kvconnector to use, without any user-facing
change.
### How was this patch tested?
Test by specifying `--kv-transfer-config
'{"kv_connector":"LMCacheAscendConnector","kv_role":"kv_both"}'`
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
---------
Signed-off-by: chloroethylene <jjysama@gmail.com>
### What this PR does / why we need it?
Drop 0.16.0 support in main
- Fix eagle proposer break introduced by
https://github.com/vllm-project/vllm/pull/34552. Mainly change to use
the draft attention group to initialize the attention metadata builder.
- Fix the `ModelRunner` has no attribute `cudagraph_capture_sizes`
error, which is a bug in vLLM v0.17.0, and fixed by a later pr
https://github.com/vllm-project/vllm/pull/30515
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
This PR supports W8A8C8 in dsv3.2/glm5 with lightning_indexer_quant ops
in pd-mix stage mainly.
Because the code for the current PD-disaggregated scenario is still
under refactoring and cleanup, this PR prioritizes ensuring the C8
functionality in the pd-mix scenario.
The next steps are planned in two parts:
① Once the optimized scatter operator is updated, we will replace the
original operator to improve the performance of storing k_scale.
② Once the code logic for the PD-disaggregated scenario becomes stable,
we will carry out more comprehensive validation and make appropriate
adaptations.
③ Because enabling C8 currently introduces several new operators whose
performance still needs improvement, performance may regress in some
scenarios. Therefore, only after all the operators are fully ready can
we ensure that this feature does not cause any performance degradation.
At that point, we will enable this feature by default and remove the
switch in `additional_config`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
CI passed with new added/existing test.
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: rjg-lyh <1318825571@qq.com>
### What this PR does / why we need it?
This PR aims to support aclgraph for model runner v2, please see RFC
#5208. The PR contains these modifications:
- adapt to newest commit of vllm main branch.
- supply a unified interface of extra forward context for both model
runner v1 and model runner v2.
- implement graph mode for main model.
### 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: Ronald1995 <ronaldautomobile@163.com>
### What this PR does / why we need it?
This PR fixes a bug in `reshape_kvcache_tensors` when reshaping the
Mamba cache for models like Qwen3.5. The previous implementation did not
correctly handle cases where the KV cache tensors have different data
types. This change ensures that slicing is performed based on byte
offsets before reshaping the tensors, which correctly handles
heterogeneous dtypes.
### 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?
When GLM5 target model uses rotary quant, the final hidden states passes
to MTP need to do an extra rotary.
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: Wangbingjie <wangbj1207@126.com>
Signed-off-by: wangbj127 <256472688+wangbj127@users.noreply.github.com>
### What this PR does / why we need it?
The merged graph of draft in `FULL` mode is broken now.
This pr solves it.
Also, `actual_seq_lengths_q` in `model_runner` is found redundant, so,
it is removed.
It depends on https://github.com/vllm-project/vllm-ascend/pull/7144 and
https://github.com/vllm-project/vllm-ascend/pull/7148.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
Test code is shown as below:
```python
prompts = [
"1.Who are you?",
"2. Who are you?",
]
sampling_params = SamplingParams(temperature=0.0, top_p=0.95, top_k=40, max_tokens=200)
llm = LLM(
model="/home/some-model/Meta-Llama-3.1-8B-Instruct",
tensor_parallel_size=1,
max_num_seqs=32,
# enforce_eager=True,
disable_log_stats=False,
distributed_executor_backend="mp",
gpu_memory_utilization=0.7,
async_scheduling=True,
speculative_config={
"enforce_eager": True,
"model": "/home/some-model/EAGLE3-LLaMA3.1-Instruct-8B",
"disable_padded_drafter_batch": False,
"method": "eagle3",
"num_speculative_tokens": 3,
},
compilation_config={
"cudagraph_mode": "FULL",
"cudagraph_num_of_warmups": 1,
},
max_model_len=4096,
enable_prefix_caching=False,
)
outputs = llm.generate(prompts, sampling_params)
```
The result before:
```text
File "/vllm-workspace/vllm-ascend/vllm_ascend/attention/attention_v1.py", line 575, in full_graph_fia
graph_params.events[num_tokens].append(event)
~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^
KeyError: 132
```
The result after:
```text
--------------------------------------------------
total_num_output_tokens: 400
num_drafts: 242
num_draft_tokens: 726
num_accepted_tokens: 156
mean acceptance length: 1.64
--------------------------------------------------
acceptance at token 0: 0.42
acceptance at token 1: 0.16
acceptance at token 2: 0.07
```
We also test `FULL_DECODE_ONLY` mode.
The result is:
```text
--------------------------------------------------
total_num_output_tokens: 400
num_drafts: 244
num_draft_tokens: 732
num_accepted_tokens: 155
mean acceptance length: 1.64
--------------------------------------------------
acceptance at token 0: 0.42
acceptance at token 1: 0.16
acceptance at token 2: 0.06
```
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
Signed-off-by: drslark <slarksblood@qq.com>
