dependabot[bot] opened a new pull request, #39003: URL: https://github.com/apache/beam/pull/39003
Bumps [vllm](https://github.com/vllm-project/vllm) from 0.10.1.1 to 0.23.0. <details> <summary>Release notes</summary> <p><em>Sourced from <a href="https://github.com/vllm-project/vllm/releases";>vllm's releases</a>.</em></p> <blockquote> <h2>v0.23.0</h2> <h1>vLLM v0.23.0 Release Notes</h1> <p>Please note that Minimax M3 is not yet supported in this version. Please follow <a href="https://recipes.vllm.ai/MiniMaxAI/MiniMax-M3";>vLLM recipe</a> for usage guides for M3.</p> <h2>Highlights</h2> <p>This release features 408 commits from 200 contributors (63 new)!</p> <ul> <li><strong>DeepSeek-V4 matures across backends</strong>: Following its introduction in v0.22.0, DeepSeek-V4 received another large hardening and optimization pass. Its sparse MLA metadata is now decoupled from DeepSeek-V3.2 (<a href="https://redirect.github.com/vllm-project/vllm/issues/44699";>#44699</a>), it gained a TRTLLM-gen attention kernel (<a href="https://redirect.github.com/vllm-project/vllm/issues/43827";>#43827</a>), EPLB support for the Mega-MoE (<a href="https://redirect.github.com/vllm-project/vllm/issues/43339";>#43339</a>), selective prefix-cache retention for sliding-window KV cache (<a href="https://redirect.github.com/vllm-project/vllm/issues/43447";>#43447</a>), and an index-share feature for DSA MTP (<a href="https://redirect.github.com/vllm-project/vllm/issues/44420";>#44420</a>). The model was also detached from <code>torch.compile</code> (<a href="https://redirect.github.com/vllm-project/vllm/issues/43746";>#43746</a>, <a href="https://redirect.github.com/vllm-p roject/vllm/issues/43891">#43891</a>), its attention and RoPE paths were refactored (<a href="https://redirect.github.com/vllm-project/vllm/issues/44569";>#44569</a>, <a href="https://redirect.github.com/vllm-project/vllm/issues/44262";>#44262</a>, <a href="https://redirect.github.com/vllm-project/vllm/issues/43926";>#43926</a>), and an XPU attention decode path was added (<a href="https://redirect.github.com/vllm-project/vllm/issues/42953";>#42953</a>).</li> <li><strong>Model Runner V2 expands to more dense models</strong>: MRv2 is now selected by default for <strong>Llama and Mistral dense models</strong> (<a href="https://redirect.github.com/vllm-project/vllm/issues/43458";>#43458</a>) in addition to Qwen3. It gained a FlashInfer sampler (<a href="https://redirect.github.com/vllm-project/vllm/issues/42472";>#42472</a>), breakable CUDA graphs (<a href="https://redirect.github.com/vllm-project/vllm/issues/44050";>#44050</a>), pipeline-parallel bubble elimination (<a href="https://redirect.github.com/vllm-project/vllm/issues/42187";>#42187</a>), kernel block-size support for hybrid models (<a href="https://redirect.github.com/vllm-project/vllm/issues/38831";>#38831</a>), and Gemma 4 MTP (<a href="https://redirect.github.com/vllm-project/vllm/issues/43241";>#43241</a>).</li> <li><strong>Rust frontend grows up</strong>: The experimental Rust frontend added a streaming <code>generate</code> endpoint (<a href="https://redirect.github.com/vllm-project/vllm/issues/43779";>#43779</a>), dynamic LoRA endpoints (<a href="https://redirect.github.com/vllm-project/vllm/issues/43778";>#43778</a>), <code>/version</code> (<a href="https://redirect.github.com/vllm-project/vllm/issues/43854";>#43854</a>) and <code>/server_info</code> (<a href="https://redirect.github.com/vllm-project/vllm/issues/43942";>#43942</a>) endpoints, a server-router extension hook (<a href="https://redirect.github.com/vllm-project/vllm/issues/43774";>#43774</a>), request-ID headers (<a href="https://redirect.github.com/vllm-project/vllm/issues/43883";>#43883</a>), and many new tool parsers (InternLM2 <a href="https://redirect.github.com/vllm-project/vllm/issues/43481";>#43481</a>, hy_v3 <a href="https://redirect.github.com/vllm-project/vllm/issues/43872";>#43872</a>, Phi-4-mini <a href="https://redir ect.github.com/vllm-project/vllm/issues/44213">#44213</a>, Gemma4 <a href="https://redirect.github.com/vllm-project/vllm/issues/43850";>#43850</a>).</li> <li><strong>Gemma 4</strong>: Added encoder-free <strong>Gemma 4 Unified</strong> support (<a href="https://redirect.github.com/vllm-project/vllm/issues/44429";>#44429</a>) and Gemma 4 MTP (<a href="https://redirect.github.com/vllm-project/vllm/issues/43241";>#43241</a>), plus numerous accuracy and startup fixes.</li> <li><strong>Transformers v5 compatibility</strong>: vLLM now targets Transformers v5, with vendored MiniCPM-V/O processors (<a href="https://redirect.github.com/vllm-project/vllm/issues/44282";>#44282</a>) and compatibility fixes for Sarvam (<a href="https://redirect.github.com/vllm-project/vllm/issues/38804";>#38804</a>) and Voxtral (<a href="https://redirect.github.com/vllm-project/vllm/issues/44559";>#44559</a>).</li> <li><strong>Multi-tier KV cache offloading</strong>: The offloading framework gained an <strong>object-store secondary tier</strong> (<a href="https://redirect.github.com/vllm-project/vllm/issues/41968";>#41968</a>), HMA enabled by default for capable connectors (<a href="https://redirect.github.com/vllm-project/vllm/issues/41847";>#41847</a>), tiering support for HMA models (<a href="https://redirect.github.com/vllm-project/vllm/issues/44287";>#44287</a>), and a per-request offloading policy via the <code>on_new_request</code> lifecycle hook (<a href="https://redirect.github.com/vllm-project/vllm/issues/43205";>#43205</a>).</li> <li><strong>Unified parser</strong>: Reasoning and tool-call parsing are now unified behind a single <code>Parser.parse()</code> interface (<a href="https://redirect.github.com/vllm-project/vllm/issues/44267";>#44267</a>), with the Responses parser migrated to it (<a href="https://redirect.github.com/vllm-project/vllm/issues/42977";>#42977</a>).</li> </ul> <h3>Model Support</h3> <ul> <li><strong>New models</strong>: Step-3.7-Flash (<a href="https://redirect.github.com/vllm-project/vllm/issues/43859";>#43859</a>), Cosmos3 Reasoner (<a href="https://redirect.github.com/vllm-project/vllm/issues/43356";>#43356</a>), Gemma 4 Unified encoder-free (<a href="https://redirect.github.com/vllm-project/vllm/issues/44429";>#44429</a>), JetBrains Mellum v2 (<a href="https://redirect.github.com/vllm-project/vllm/issues/43992";>#43992</a>), Granite Speech Plus (<a href="https://redirect.github.com/vllm-project/vllm/issues/43519";>#43519</a>), Cohere Mini Code (<a href="https://redirect.github.com/vllm-project/vllm/issues/44707";>#44707</a>).</li> <li><strong>Gemma 4</strong>: Encoder-free Unified support (<a href="https://redirect.github.com/vllm-project/vllm/issues/44429";>#44429</a>), MTP (<a href="https://redirect.github.com/vllm-project/vllm/issues/43241";>#43241</a>), native ViT linear layers (<a href="https://redirect.github.com/vllm-project/vllm/issues/43798";>#43798</a>), vision-embedder excluded from quantization (<a href="https://redirect.github.com/vllm-project/vllm/issues/44571";>#44571</a>), and fixes for MTP under TP>1 (<a href="https://redirect.github.com/vllm-project/vllm/issues/43909";>#43909</a>), block-table mismatch under concurrency (<a href="https://redirect.github.com/vllm-project/vllm/issues/43982";>#43982</a>), transformers-processor startup crash (<a href="https://redirect.github.com/vllm-project/vllm/issues/44232";>#44232</a>), and CPU init (<a href="https://redirect.github.com/vllm-project/vllm/issues/44615";>#44615</a>).</li> <li><strong>Transformers v5</strong>: Vendor MiniCPM-V/O processors (<a href="https://redirect.github.com/vllm-project/vllm/issues/44282";>#44282</a>), Sarvam compat (<a href="https://redirect.github.com/vllm-project/vllm/issues/38804";>#38804</a>), Voxtral <code>fetch_audio</code> for transformers≥5.10 (<a href="https://redirect.github.com/vllm-project/vllm/issues/44559";>#44559</a>).</li> <li><strong>Model fixes & enhancements</strong>: Qwen3-VL/Qwen3-omni-thinker deepstack accuracy under <code>torch.compile</code> (<a href="https://redirect.github.com/vllm-project/vllm/issues/43617";>#43617</a>), EVS for Qwen3-VL (<a href="https://redirect.github.com/vllm-project/vllm/issues/44205";>#44205</a>), GLM-5.1 PP loading (<a href="https://redirect.github.com/vllm-project/vllm/issues/42944";>#42944</a>), GLM-4.1V processor logits (<a href="https://redirect.github.com/vllm-project/vllm/issues/43575";>#43575</a>), GLM-4.6V video loader (<a href="https://redirect.github.com/vllm-project/vllm/issues/44417";>#44417</a>), OlmoHybrid init (<a href="https://redirect.github.com/vllm-project/vllm/issues/43846";>#43846</a>), HyperCLOVAX remote-code removal (<a href="https://redirect.github.com/vllm-project/vllm/issues/43860";>#43860</a>), Bailing-MoE rotary factor (<a href="https://redirect.github.com/vllm-project/vllm/issues/43770";>#43770</a>), Step3 PP residual KeyError (<a href="htt ps://redirect.github.com/vllm-project/vllm/issues/37622">#37622</a>), MiniCPM-V-4.6 video (<a href="https://redirect.github.com/vllm-project/vllm/issues/44509";>#44509</a>), MiniCPM-O audio unpadding (<a href="https://redirect.github.com/vllm-project/vllm/issues/38053";>#38053</a>), MiniCPM-V batched preprocessing (<a href="https://redirect.github.com/vllm-project/vllm/issues/44609";>#44609</a>), FunASR-Nano init (<a href="https://redirect.github.com/vllm-project/vllm/issues/44215";>#44215</a>), Cohere routing method (<a href="https://redirect.github.com/vllm-project/vllm/issues/44021";>#44021</a>), Kimi-K2.5 FlashInfer ViT metadata (<a href="https://redirect.github.com/vllm-project/vllm/issues/44493";>#44493</a>).</li> <li><strong>Multimodal</strong>: Auto-select registered video loader for VLMs (<a href="https://redirect.github.com/vllm-project/vllm/issues/44126";>#44126</a>), O(log n) multimodal item handling per step (<a href="https://redirect.github.com/vllm-project/vllm/issues/44212";>#44212</a>), local image encoding in benchmarks (<a href="https://redirect.github.com/vllm-project/vllm/issues/43843";>#43843</a>), interleaved custom image benchmark datasets (<a href="https://redirect.github.com/vllm-project/vllm/issues/43636";>#43636</a>).</li> <li><strong>Pooling/Classification</strong>: Proper exceptions for pooling UX (<a href="https://redirect.github.com/vllm-project/vllm/issues/44593";>#44593</a>), <code>extra_repr()</code> for pooler classes (<a href="https://redirect.github.com/vllm-project/vllm/issues/44805";>#44805</a>), LoRA-adapter-name pooling fix (<a href="https://redirect.github.com/vllm-project/vllm/issues/44410";>#44410</a>), resettled generative scoring entrypoint (<a href="https://redirect.github.com/vllm-project/vllm/issues/44153";>#44153</a>), expanded pooler unit tests (<a href="https://redirect.github.com/vllm-project/vllm/issues/43818";>#43818</a>, <a href="https://redirect.github.com/vllm-project/vllm/issues/44471";>#44471</a>).</li> <li><strong>Refactor</strong>: AutoWeightsLoader for InternLM2 (<a href="https://redirect.github.com/vllm-project/vllm/issues/38278";>#38278</a>).</li> </ul> <h3>Engine Core</h3> <ul> <li><strong>Model Runner V2</strong>: Default for Llama and Mistral dense models (<a href="https://redirect.github.com/vllm-project/vllm/issues/43458";>#43458</a>), FlashInfer sampler (<a href="https://redirect.github.com/vllm-project/vllm/issues/42472";>#42472</a>), breakable CUDA graphs (<a href="https://redirect.github.com/vllm-project/vllm/issues/44050";>#44050</a>), removed Eagle's dedicated CUDA graph pool (<a href="https://redirect.github.com/vllm-project/vllm/issues/44078";>#44078</a>), pipeline-parallel bubble elimination (<a href="https://redirect.github.com/vllm-project/vllm/issues/42187";>#42187</a>), kernel block size for hybrid models (<a href="https://redirect.github.com/vllm-project/vllm/issues/38831";>#38831</a>), zeroing of freshly allocated KV blocks for hybrid + FP8 KV cache (<a href="https://redirect.github.com/vllm-project/vllm/issues/43990";>#43990</a>), actual batch <code>max_seq_len</code> for attention metadata (<a href="https://redirect.github.com/vllm-project/ vllm/issues/43991">#43991</a>), rejection-sampling acceptance-rate fix (<a href="https://redirect.github.com/vllm-project/vllm/issues/40651";>#40651</a>), KVConnector + PP cleanup (<a href="https://redirect.github.com/vllm-project/vllm/issues/43732";>#43732</a>), speculator-prefill warmup/capture (<a href="https://redirect.github.com/vllm-project/vllm/issues/44253";>#44253</a>).</li> <li><strong>Speculative decoding (DFlash)</strong>: Causal DFlash (<a href="https://redirect.github.com/vllm-project/vllm/issues/43445";>#43445</a>), proper lookahead-slot allocation (<a href="https://redirect.github.com/vllm-project/vllm/issues/43733";>#43733</a>), prefix-cache corruption fix (<a href="https://redirect.github.com/vllm-project/vllm/issues/42971";>#42971</a>); independent drafter attention-backend selection (<a href="https://redirect.github.com/vllm-project/vllm/issues/39930";>#39930</a>), attention-group split by <code>num_heads_q</code> for drafts (<a href="https://redirect.github.com/vllm-project/vllm/issues/43543";>#43543</a>), EAGLE/MTP lookahead caching in the SWA prefix-cache mask (<a href="https://redirect.github.com/vllm-project/vllm/issues/44082";>#44082</a>).</li> <li><strong>Attention & hybrid/Mamba</strong>: FlexAttention/FlashAttention num-blocks-first layouts (<a href="https://redirect.github.com/vllm-project/vllm/issues/42095";>#42095</a>), OOT MLA prefill backend registration (<a href="https://redirect.github.com/vllm-project/vllm/issues/43325";>#43325</a>), FlashAttention upstream sync (<a href="https://redirect.github.com/vllm-project/vllm/issues/44065";>#44065</a>), Mamba LINEAR attention-module refactor (<a href="https://redirect.github.com/vllm-project/vllm/issues/43556";>#43556</a>), corrupted MLA + linear attention fix (<a href="https://redirect.github.com/vllm-project/vllm/issues/43961";>#43961</a>), KDA conv-state unification (<a href="https://redirect.github.com/vllm-project/vllm/issues/44539";>#44539</a>) and gate/cumsum fusion (<a href="https://redirect.github.com/vllm-project/vllm/issues/43667";>#43667</a>), Mamba SSD <code>do_not_specialize</code> (<a href="https://redirect.github.com/vllm-project/vllm/issues/43803";>#43803< /a>), Qwen3.5 mixed prefill+decode split routing (<a href="https://redirect.github.com/vllm-project/vllm/issues/44700";>#44700</a>), MiniMax-M2 gate kernel (<a href="https://redirect.github.com/vllm-project/vllm/issues/38445";>#38445</a>).</li> <li><strong>KV cache & scheduler</strong>: Pluggable <code>KVCacheSpec</code> (<a href="https://redirect.github.com/vllm-project/vllm/issues/37505";>#37505</a>), <code>scheduler_block_size</code> threaded into KVCacheManager/Coordinator (<a href="https://redirect.github.com/vllm-project/vllm/issues/44165";>#44165</a>), <code>max_concurrent_batches</code> moved to <code>VllmConfig</code> (<a href="https://redirect.github.com/vllm-project/vllm/issues/44274";>#44274</a>), config validation rejecting 0/negative knobs (<a href="https://redirect.github.com/vllm-project/vllm/issues/43794";>#43794</a>, <a href="https://redirect.github.com/vllm-project/vllm/issues/44057";>#44057</a>, <a href="https://redirect.github.com/vllm-project/vllm/issues/44207";>#44207</a>), KV-cache scale boilerplate removed from weight loading (<a href="https://redirect.github.com/vllm-project/vllm/issues/43167";>#43167</a>).</li> <li><strong>Core</strong>: Freeze the garbage collector in workers after model init (<a href="https://redirect.github.com/vllm-project/vllm/issues/44363";>#44363</a>), sparse NCCL weight transfer for in-place updates (<a href="https://redirect.github.com/vllm-project/vllm/issues/40096";>#40096</a>), graceful spinloop ext-load failure handling (<a href="https://redirect.github.com/vllm-project/vllm/issues/43659";>#43659</a>), scheduled-function deprecations (<a href="https://redirect.github.com/vllm-project/vllm/issues/43358";>#43358</a>).</li> </ul> <h3>Large Scale Serving & Distributed</h3> <ul> <li><strong>KV cache offloading</strong>: Object-store secondary tier (<a href="https://redirect.github.com/vllm-project/vllm/issues/41968";>#41968</a>), HMA on by default for capable connectors (<a href="https://redirect.github.com/vllm-project/vllm/issues/41847";>#41847</a>) and tiering (<a href="https://redirect.github.com/vllm-project/vllm/issues/44287";>#44287</a>), per-request offloading policy (<code>on_new_request</code>) (<a href="https://redirect.github.com/vllm-project/vllm/issues/43205";>#43205</a>) and <code>on_schedule_end()</code> hook (<a href="https://redirect.github.com/vllm-project/vllm/issues/44206";>#44206</a>), token-offset selective offload (<a href="https://redirect.github.com/vllm-project/vllm/issues/39983";>#39983</a>), skip decode-phase blocks in CPU offload (<a href="https://redirect.github.com/vllm-project/vllm/issues/43797";>#43797</a>), page-size block alignment (<a href="https://redirect.github.com/vllm-project/vllm/issues/43689";>#43689</a>), Triton fast-p ath for small CPU→GPU <code>swap_blocks_batch</code> (<a href="https://redirect.github.com/vllm-project/vllm/issues/42212";>#42212</a>), stale sliding-window block fix (<a href="https://redirect.github.com/vllm-project/vllm/issues/42959";>#42959</a>).</li> <li><strong>KV connectors / disaggregated serving</strong>: PP-aware handshake aggregation and intermediate-PP output plumbing (<a href="https://redirect.github.com/vllm-project/vllm/issues/43720";>#43720</a>), multiple-async-KV-load deadlock fix (<a href="https://redirect.github.com/vllm-project/vllm/issues/44560";>#44560</a>), Nixl Mamba prefix-caching mode (<a href="https://redirect.github.com/vllm-project/vllm/issues/42554";>#42554</a>), NixlConnector <code>kv_both</code> role deprecation cycle (<a href="https://redirect.github.com/vllm-project/vllm/issues/43874";>#43874</a>), Mooncake fixes (<a href="https://redirect.github.com/vllm-project/vllm/issues/43742";>#43742</a>, <a href="https://redirect.github.com/vllm-project/vllm/issues/44103";>#44103</a>, <a href="https://redirect.github.com/vllm-project/vllm/issues/42694";>#42694</a>), LMCache <code>LMCacheMPConnector</code> (<a href="https://redirect.github.com/vllm-project/vllm/issues/42865";>#42865</a>), EC connector shutdown API (< a href="https://redirect.github.com/vllm-project/vllm/issues/42423";>#42423</a>) and non-blocking lookup (<a href="https://redirect.github.com/vllm-project/vllm/issues/41627";>#41627</a>), KV-transfer tokens excluded from <code>iteration_tokens_total</code> (<a href="https://redirect.github.com/vllm-project/vllm/issues/43346";>#43346</a>).</li> <li><strong>EPLB</strong>: Async EPLB by default (<a href="https://redirect.github.com/vllm-project/vllm/issues/43219";>#43219</a>), EPLB for DeepSeek-V4 Mega-MoE (<a href="https://redirect.github.com/vllm-project/vllm/issues/43339";>#43339</a>), Nixl zero-copy EPLB transfers (<a href="https://redirect.github.com/vllm-project/vllm/issues/41633";>#41633</a>).</li> <li><strong>Data parallel</strong>: DP Ray placement groups on specific nodes (<a href="https://redirect.github.com/vllm-project/vllm/issues/44669";>#44669</a>) and grouped-node allocation fix (<a href="https://redirect.github.com/vllm-project/vllm/issues/43998";>#43998</a>), SSL for the DP supervisor (<a href="https://redirect.github.com/vllm-project/vllm/issues/43688";>#43688</a>), DP-coordinator startup timeout raised to 120s (<a href="https://redirect.github.com/vllm-project/vllm/issues/42343";>#42343</a>), per-GPU-worker RDMA NIC selection (<a href="https://redirect.github.com/vllm-project/vllm/issues/42083";>#42083</a>).</li> </ul> <h3>Hardware & Performance</h3> <ul> <li><strong>NVIDIA / kernels</strong>: FP8 FlashInfer attention for ViT (<a href="https://redirect.github.com/vllm-project/vllm/issues/38065";>#38065</a>), Triton MoE backend on Hopper by default (<a href="https://redirect.github.com/vllm-project/vllm/issues/44220";>#44220</a>), CUTLASS FP8 scaled-mm padding bypass (+20%) (<a href="https://redirect.github.com/vllm-project/vllm/issues/43706";>#43706</a>), MoE-permute buffer pre-allocation (+9–14%) (<a href="https://redirect.github.com/vllm-project/vllm/issues/43014";>#43014</a>), <code>Fp8BlockScaledMM</code> <code>new_empty()</code> optimization (<a href="https://redirect.github.com/vllm-project/vllm/issues/43677";>#43677</a>), TurboQuant shared dequant buffers (<a href="https://redirect.github.com/vllm-project/vllm/issues/40941";>#40941</a>), tuned <code>selective_state_update</code> for H200/RTX PRO (<a href="https://redirect.github.com/vllm-project/vllm/issues/44251";>#44251</a>), Inductor fast-path fallback for vLLM/AITER custom op s (<a href="https://redirect.github.com/vllm-project/vllm/issues/42129";>#42129</a>), Gemma RMS all-reduce fusion (<a href="https://redirect.github.com/vllm-project/vllm/issues/42646";>#42646</a>), NUMA auto-binding on DGX B300 (<a href="https://redirect.github.com/vllm-project/vllm/issues/43270";>#43270</a>).</li> <li><strong>AMD ROCm</strong>: ROCm 7.2.3 (<a href="https://redirect.github.com/vllm-project/vllm/issues/43136";>#43136</a>), AITER v0.1.13.post1 (<a href="https://redirect.github.com/vllm-project/vllm/issues/44265";>#44265</a>), native W4A16 (<a href="https://redirect.github.com/vllm-project/vllm/issues/41394";>#41394</a>) and fused-MoE W4A16 HIP (<a href="https://redirect.github.com/vllm-project/vllm/issues/44075";>#44075</a>) kernels for RDNA3 (gfx1100), AITER top-k/top-p sampler by default (<a href="https://redirect.github.com/vllm-project/vllm/issues/43331";>#43331</a>), attention-sink support in AITER FA (<a href="https://redirect.github.com/vllm-project/vllm/issues/43817";>#43817</a>), AITER hipBLASLt GEMM online tuning (<a href="https://redirect.github.com/vllm-project/vllm/issues/40426";>#40426</a>), <code>permute_cols</code> for ROCm (<a href="https://redirect.github.com/vllm-project/vllm/issues/44674";>#44674</a>), blocks-first KV layout for AMD (<a href="https://redirect.githu b.com/vllm-project/vllm/issues/43660">#43660</a>), N=5 wvSplitK for spec decode (<a href="https://redirect.github.com/vllm-project/vllm/issues/40687";>#40687</a>), MoRI connector improvements (<a href="https://redirect.github.com/vllm-project/vllm/issues/43303";>#43303</a>, <a href="https://redirect.github.com/vllm-project/vllm/issues/41751";>#41751</a>, <a href="https://redirect.github.com/vllm-project/vllm/issues/40344";>#40344</a>).</li> <li><strong>Intel XPU</strong>: vllm-xpu-kernel v0.1.7 (<a href="https://redirect.github.com/vllm-project/vllm/issues/41019";>#41019</a>), <code>block_fp8_moe</code> (<a href="https://redirect.github.com/vllm-project/vllm/issues/42139";>#42139</a>), block-scaled W8A8 FP8 path (<a href="https://redirect.github.com/vllm-project/vllm/issues/39968";>#39968</a>), WNA16 oracle for GPTQ sym-int4 (<a href="https://redirect.github.com/vllm-project/vllm/issues/41426";>#41426</a>), rms_norm/act quant fusions (<a href="https://redirect.github.com/vllm-project/vllm/issues/43963";>#43963</a>), GDN-attention MTP (<a href="https://redirect.github.com/vllm-project/vllm/issues/43565";>#43565</a>), Triton selective-scan op (<a href="https://redirect.github.com/vllm-project/vllm/issues/43421";>#43421</a>), transparent sleep mode (<a href="https://redirect.github.com/vllm-project/vllm/issues/37149";>#37149</a>), CPU/tiering offloading on XPU (<a href="https://redirect.github.com/vllm-project/vllm/issues/36423 ">#36423</a>), DeepSeek-V4 attention decode path (<a href="https://redirect.github.com/vllm-project/vllm/issues/42953";>#42953</a>).</li> <li><strong>CPU & other architectures</strong>: zentorch-accelerated W8A8/W4A16 on AMD Zen CPUs (<a href="https://redirect.github.com/vllm-project/vllm/issues/41813";>#41813</a>), CPU top-k/top-p Triton sampling (<a href="https://redirect.github.com/vllm-project/vllm/issues/43633";>#43633</a>), non-divisible GQA decode in mixed batches (<a href="https://redirect.github.com/vllm-project/vllm/issues/43032";>#43032</a>), <code>cpu_awq</code> folded into <code>awq_marlin</code> (<a href="https://redirect.github.com/vllm-project/vllm/issues/43841";>#43841</a>), RISC-V RVV WNA16 helpers (<a href="https://redirect.github.com/vllm-project/vllm/issues/42730";>#42730</a>), fused GDN gated-delta-rule kernels (<a href="https://redirect.github.com/vllm-project/vllm/issues/43534";>#43534</a>), PowerPC SHM communicator (<a href="https://redirect.github.com/vllm-project/vllm/issues/43754";>#43754</a>), arm64 CI image (<a href="https://redirect.github.com/vllm-project/vllm/issues/41303";>#41303</a>).< /li> <li><strong>TPU</strong>: tpu-inference upgraded to v0.20.0 (<a href="https://redirect.github.com/vllm-project/vllm/issues/43394";>#43394</a>) then v0.21.0 (<a href="https://redirect.github.com/vllm-project/vllm/issues/44621";>#44621</a>).</li> <li><strong>torch stable ABI</strong>: Continued migration of kernels to the libtorch stable ABI — merge_attn_states/mamba/sampler [8/n] (<a href="https://redirect.github.com/vllm-project/vllm/issues/43361";>#43361</a>), attention/cache kernels [9/n] (<a href="https://redirect.github.com/vllm-project/vllm/issues/43717";>#43717</a>), header files (<a href="https://redirect.github.com/vllm-project/vllm/issues/44013";>#44013</a>), cuda_view/silu_and_mul [10/n] (<a href="https://redirect.github.com/vllm-project/vllm/issues/44334";>#44334</a>), custom all-reduce/DeepSeek-V4 fused MLA/MXFP8 MoE [10b/n] (<a href="https://redirect.github.com/vllm-project/vllm/issues/44365";>#44365</a>); ROCm fallback to regular ABI (<a href="https://redirect.github.com/vllm-project/vllm/issues/44648";>#44648</a>), <code>_has_module</code> trial-import verification (<a href="https://redirect.github.com/vllm-project/vllm/issues/44035";>#44035</a>).</li> </ul> <h3>Quantization</h3> <ul> <li><strong>ModelOpt</strong>: LM-head quantization (<a href="https://redirect.github.com/vllm-project/vllm/issues/42124";>#42124</a>), MXFP8 non-gated MoE (<a href="https://redirect.github.com/vllm-project/vllm/issues/42958";>#42958</a>).</li> <li><strong>compressed-tensors</strong>: WNA8O8Int linears and WNInt embeddings (<a href="https://redirect.github.com/vllm-project/vllm/issues/44340";>#44340</a>), asymmetric MoE WNA16 Marlin (<a href="https://redirect.github.com/vllm-project/vllm/issues/44025";>#44025</a>), single-class NVFP4 linear refactor (<a href="https://redirect.github.com/vllm-project/vllm/issues/42443";>#42443</a>).</li> </ul> <!-- raw HTML omitted --> </blockquote> <p>... (truncated)</p> </details> <details> <summary>Commits</summary> <ul> <li><a href="https://github.com/vllm-project/vllm/commit/0fc695fc6d1d82e9a5ac6835ac8e4e1c83703665";><code>0fc695f</code></a> [Bugfix][Frontend] Cap fastapi < 0.137 to avoid prometheus-fastapi-instrument...</li> <li><a href="https://github.com/vllm-project/vllm/commit/91df0fad4dc98a67c7659d9dbd915245d5c43d96";><code>91df0fa</code></a> [Bugfix][CPU] Don't build triton-cpu on arm64 release image (<a href="https://redirect.github.com/vllm-project/vllm/issues/45401";>#45401</a>)</li> <li><a href="https://github.com/vllm-project/vllm/commit/78743ab5bffd381e88f97e1c8ba20473b0ae6d75";><code>78743ab</code></a> [Docker] Fix CUTLASS DSL cu13 install order in Dockerfile (<a href="https://redirect.github.com/vllm-project/vllm/issues/45204";>#45204</a>)</li> <li><a href="https://github.com/vllm-project/vllm/commit/b2d7294b0f1f2f1527e2b4dd5a3cdc703c0a3440";><code>b2d7294</code></a> [ROCm][Bugfix] Make intermediate_pad TP-aware in rocm_aiter_fused_experts (<a href="https://redirect.github.com/vllm-project/vllm/issues/4";>#4</a>...</li> <li><a href="https://github.com/vllm-project/vllm/commit/741ba421d847b76225379bce2a82b04fe88cc2d4";><code>741ba42</code></a> [Bugfix] [DSV4] [ROCm] Pin apache-tvm-ffi version to <code>0.1.10</code> (<a href="https://redirect.github.com/vllm-project/vllm/issues/45169";>#45169</a>)</li> <li><a href="https://github.com/vllm-project/vllm/commit/ac94893da37b35b19c8e72c1f91ccfcecfe37bb5";><code>ac94893</code></a> [ROCm][MLA][Bugfix] Reserve FP8 prefill workspace before lock for Kimi-K2.5 (...</li> <li><a href="https://github.com/vllm-project/vllm/commit/967c5c3bc38891f4465d3f4e99917ed837bb3833";><code>967c5c3</code></a> [ROCm][CI] Stage C mirrors (<a href="https://redirect.github.com/vllm-project/vllm/issues/42793";>#42793</a>)</li> <li><a href="https://github.com/vllm-project/vllm/commit/54c660c3a6c30c79e6457a162a19f4ed68042743";><code>54c660c</code></a> [XPU][Minor] format moe kernel name and add in kernel list (<a href="https://redirect.github.com/vllm-project/vllm/issues/44771";>#44771</a>)</li> <li><a href="https://github.com/vllm-project/vllm/commit/8fb0274415062912ec56c6629a04cbac60121fe3";><code>8fb0274</code></a> [MM][CG] Simplify ViT CUDA graph interfaces (<a href="https://redirect.github.com/vllm-project/vllm/issues/44484";>#44484</a>)</li> <li><a href="https://github.com/vllm-project/vllm/commit/eebce65756f0beb08547439728da98b5e1a7119c";><code>eebce65</code></a> [XPU]feat: add DeepSeek-V4 XPU attention decode path (<a href="https://redirect.github.com/vllm-project/vllm/issues/42953";>#42953</a>)</li> <li>Additional commits viewable in <a href="https://github.com/vllm-project/vllm/compare/v0.10.1.1...v0.23.0";>compare view</a></li> </ul> </details> <br /> [](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. 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