Script 'mail_helper' called by obssrc Hello community, here is the log from the commit of package python-vllm for openSUSE:Factory checked in at 2026-08-11 17:15:40 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Comparing /work/SRC/openSUSE:Factory/python-vllm (Old) and /work/SRC/openSUSE:Factory/.python-vllm.new.17972 (New) ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Package is "python-vllm" Tue Aug 11 17:15:40 2026 rev:6 rq:1370608 version:0.27.0 Changes: -------- --- /work/SRC/openSUSE:Factory/python-vllm/python-vllm.changes 2026-08-09 21:34:34.996633670 +0200 +++ /work/SRC/openSUSE:Factory/.python-vllm.new.17972/python-vllm.changes 2026-08-11 17:16:51.293332010 +0200 @@ -1,0 +2,55 @@ +Tue Aug 11 05:49:32 UTC 2026 - Martin Pluskal <[email protected]> + +- Update to version 0.27.0: + * Kimi K3 support landing as a complete stack: model files and + kernels, Python and Rust frontends, AttnRes kernels, DeepGEMM + support, compressed-tensors quantized checkpoints and an option + to shard the shared expert instead of replicating it + * Further new models: Qwen3.5 text-only dense and MoE with EVS + video token pruning, K-EXAONE-2.0-750B-A37B, VaultGemma through + the Transformers modeling backend, jina-embeddings-v5-text-nano + * FlashAttention 4 on SM100 gains FP8 KV cache and headdim-256 + support, backed by new JIT and runner-owned Triton warmup + infrastructure that removes first-request compilation stalls + * DeepSeek-V4 performance work: sequence parallelism, roughly 2x + kernel improvement from skipping empty c128 launches, workspace + reuse, adaptive topk width, a compact MXFP4 indexer KV cache and + 448 MiB less GPU memory in the pipeline-parallel buffer + * Model Runner V2 extends to non-generative workloads: encoder-only + attention, sequence pooling for embedding and classification, + encoder token classification and embedding, BGE-M3 pooling and + multimodal on CPU + * Simplified fault tolerance framework for data-parallel and + expert-parallel deployments behind an external load balancer, + plus async preparation for elastic expert-parallel scaling + * Prefill/decode disaggregation for hybrid models: NIXL support for + hybrid MLA+SSM models, heterogeneous block sizes, and MoRIIO + heterogeneous read routing + * The Rust frontend grows a gRPC control plane covering health + reporting, abort control, server/model discovery and KV event + source discovery, and integrates vllm-bench into the vllm CLI + * CPU backend: INT8 fused MoE kernel for Arm CPUs, s390x oneDNN + INT8 GEMM, optimised GDN conv path for speculative decoding, + granite-4 enabled, and the CPU kernels bumped to the latest + version + * Quantization: FP4 Qutlass for compressed-tensors, CuTeDSL MoE for + ReLU2 NVFP4, MXFP8 linear in INC, AutoRound W4A16 MoE, and a KV + quant mode for TurboQuant + * API and frontend: Cohere chat v2, cache_salt in the Anthropic + Messages API, strict tool calling for GPT-OSS Harmony, a + per-request stream_interval sampling parameter and a standardised + VLLMError hierarchy + * Early enablement for NVIDIA Rubin (sm_107) and ROCm gfx1250 + * Removed the Plamo2 and Ouro models, and the no longer supported + max_num_partial_prefills and max_long_partial_prefills arguments + * Upstream moved its pinned build environment to PyTorch 2.13.0; + this package keeps building against the 2.12.0 that Factory ships +- Raise the python-mistral-common requirement to >= 1.11.6, matching + upstream's own floor for this release +- Rebase vllm-relax-cpu-requirements.patch and + vllm-cpu-disable-rust-frontend.patch onto 0.27.0: + * vllm-relax-cpu-requirements.patch now relaxes the torch pin to + >= 2.12.0, tracking what Factory ships rather than upstream's + exact 2.13.0 + +------------------------------------------------------------------- Old: ---- vllm-0.26.0.tar.gz New: ---- vllm-0.27.0.tar.gz ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Other differences: ------------------ ++++++ python-vllm.spec ++++++ --- /var/tmp/diff_new_pack.bArvyq/_old 2026-08-11 17:16:53.493425240 +0200 +++ /var/tmp/diff_new_pack.bArvyq/_new 2026-08-11 17:16:53.497425410 +0200 @@ -47,7 +47,7 @@ %define onednn_x86_version 3.10 %define acl_version 52.6.0 Name: python-vllm%{psuffix} -Version: 0.26.0 +Version: 0.27.0 Release: 0 Summary: A high-throughput and memory-efficient inference and serving engine for LLMs License: Apache-2.0 @@ -108,7 +108,7 @@ Requires: python-llguidance >= 1.7.0 Requires: python-lm-format-enforcer = 0.11.3 Requires: python-mcp -Requires: python-mistral-common >= 1.11.5 +Requires: python-mistral-common >= 1.11.6 Requires: python-model-hosting-container-standards >= 0.1.14 Requires: python-msgspec Requires: python-numba >= 0.65.0 @@ -206,7 +206,7 @@ %endif # Use the torch already installed in the build root (2.12.0) instead of the -# exact 2.11.0 pin, via vLLM's own helper. Strips torch/torchvision/torchaudio +# exact 2.13.0 pin, via vLLM's own helper. Strips torch/torchvision/torchaudio # pins from requirements/*.txt and pyproject.toml. %python_expand $python use_existing_torch.py --prefix ++++++ vllm-0.26.0.tar.gz -> vllm-0.27.0.tar.gz ++++++ /work/SRC/openSUSE:Factory/python-vllm/vllm-0.26.0.tar.gz /work/SRC/openSUSE:Factory/.python-vllm.new.17972/vllm-0.27.0.tar.gz differ: char 5, line 1 ++++++ vllm-cpu-disable-rust-frontend.patch ++++++ --- /var/tmp/diff_new_pack.bArvyq/_old 2026-08-11 17:16:53.601429816 +0200 +++ /var/tmp/diff_new_pack.bArvyq/_new 2026-08-11 17:16:53.605429986 +0200 @@ -1,6 +1,6 @@ ---- a/setup.py 2026-07-13 08:06:59.849797035 +0200 -+++ b/setup.py 2026-07-13 08:12:53.729680498 +0200 -@@ -1231,9 +1231,16 @@ +--- a/setup.py ++++ b/setup.py +@@ -1257,9 +1257,16 @@ # Rust artifacts, built via setuptools-rust and installed into the package # directory alongside the Python modules. ++++++ vllm-relax-cpu-requirements.patch ++++++ --- /var/tmp/diff_new_pack.bArvyq/_old 2026-08-11 17:16:53.625430833 +0200 +++ /var/tmp/diff_new_pack.bArvyq/_new 2026-08-11 17:16:53.633431172 +0200 @@ -11,8 +11,8 @@ +numba >= 0.65.0; platform_machine != "s390x" # Required for N-gram speculative decoding # Dependencies for CPUs --torch==2.11.0+cpu; platform_machine == "x86_64" or platform_machine == "s390x" or platform_machine == "aarch64" --torch==2.11.0; platform_system == "Darwin" or platform_machine == "ppc64le" or platform_machine == "riscv64" +-torch==2.13.0+cpu; platform_machine == "x86_64" or platform_machine == "s390x" or platform_machine == "aarch64" +-torch==2.13.0; platform_system == "Darwin" or platform_machine == "ppc64le" or platform_machine == "riscv64" - -# required for the image processor of minicpm-o-2_6, this must be updated alongside torch -torchaudio; platform_machine != "s390x" and platform_machine != "riscv64" @@ -25,8 +25,8 @@ - -# Intel Extension for PyTorch, only for x86_64 CPUs -intel-openmp==2024.2.1; platform_machine == "x86_64" -+torch>=2.11.0; platform_machine == "x86_64" or platform_machine == "s390x" or platform_machine == "aarch64" -+torch>=2.11.0; platform_system == "Darwin" or platform_machine == "ppc64le" or platform_machine == "riscv64" ++torch>=2.12.0; platform_machine == "x86_64" or platform_machine == "s390x" or platform_machine == "aarch64" ++torch>=2.12.0; platform_system == "Darwin" or platform_machine == "ppc64le" or platform_machine == "riscv64" # Use this to gather CPU info and optimize based on ARM Neoverse cores py-cpuinfo; platform_machine == "aarch64"
