### 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?
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?
Fix lint failed due to the merging of a previous PR.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
On the 310P device, when running ACLGraph together with the n-gram
speculative decoding algorithm, both graph capture and graph replay
require `uniform_decode_query_len` and do not depend on
`attention_state`. This leads to a rather interesting and unexpected
issue on 310P: during decode-only, execution does **not** enter the
graph, while in the split-fuse state (that is, the chunked prefill
state), it instead enters graph execution directly.
The issue can be resolved by forcibly setting `uniform_decode_query_len`
to `1`, so that 310P captures only the decode-only graph, and replay is
then controlled through `attention_state`.
### Does this PR introduce _any_ user-facing change?
NO
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: Tflowers-0129 <2906339855@qq.com>
### What this PR does / why we need it?
`mxfp_compat` only provides dtype/symbol compatibility helpers for
different `torch_npu` versions, but it was placed under
`vllm_ascend.quantization`. Importing it from device/ops paths could
trigger `quantization/__init__.py` and pull in heavy quantization method
dependencies, increasing startup coupling and causing import-cycle risk
(especially on 310P paths).
### Does this PR introduce _any_ user-facing change?
No functional behavior change intended.
### How was this patch tested?
CI passed.
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
---------
Signed-off-by: linfeng-yuan <1102311262@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>
### What this PR does / why we need it?
Added a check in the may_reinitialize_input_batch method to verify
whether the backend implements the get_supported_block_size method
### Does this PR introduce _any_ user-facing change?
no user-facing change
### How was this patch tested?
Only a few lines of code within the methods were modified, and the
format check test has been passed.
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: Debuuuuger <huangzr@cmbchina.com>
Signed-off-by: debuger <102402761+huangazazaz@users.noreply.github.com>
Signed-off-by: Debuuuuger <12110718@mail.sustech.edu.cn>
Co-authored-by: Debuuuuger <huangzr@cmbchina.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?
* Refactor the LayerNorm and activation operator classes to decouple the
310P device implementation from the main branch.
* Refactor `mm_encoder_attention` on 310P to use the
`torch_npu._npu_flash_attention_unpad` operator.
* Refactor the QKV inputs in the prefill stage of `attention_v1` on 310P
so they are no longer padded to 16× alignment.
* Refactor `model_runner` on 310P to align the KV-cache initialization
logic with the mainline implementation.
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
use the e2e tests.
- vLLM version: v0.13.0
- vLLM main:
d68209402d
---------
Signed-off-by: Tflowers-0129 <2906339855@qq.com>
