b10293
apache/answerb10293Aug 6, 2026by github-actions[bot]
AI Summary
Onboards AMD ROCm CI infrastructure with specific fixes for the RDNA3.5 gfx1151 architecture, handling integrated GPU output buffers and async execution issues.
Key Highlights
- Onboarded AMD ROCm CI with gfx1151 fixes
- Allowed integrated-GPU host output buffer in debug builds
- Used HIP_LAUNCH_BLOCKING for gfx1151 job to restore correctness
- Skipped specific tests on HIP backend due to lack of CUB support
New Features
- AMD ROCm CI integration
- gfx1151 architecture support fixes
Full Release Notes
<details open> ci : onboard AMD ROCm CI with gfx1151 fixes (#26544) * ci: prepare for amd rocm ci Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * ci: fix editorconfig-checker Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * ci: fix device not recognised Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * ci: rename gpu-amd to gpu-hip Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * ci: gpu-hip to gpu-rocm haha Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * CUDA: allow integrated-GPU host output buffer in debug assert On integrated GPUs (APUs), the scheduler can legitimately place a graph node's output on the host-visible buffer, which ggml_cuda_compute_forward already handles. The debug assert in ggml_cuda_graph_evaluate_and_capture required every node output to be on the device buffer, so a debug build aborts on such a node (e.g. attn_residual ADD -> ROCm_Host on RDNA3.5). The source-tensor assert directly below already permits this via the integrated + cuda_host exception; apply the same exception to the node's own output buffer. Debug-only; no effect on release/compute. Fixes test-recurrent-state-rollback on gfx1151 (Strix Halo). * ci: enable unified memory for ROCm gfx1151 job Work around a coherence issue on integrated RDNA3.5 (gfx1151) where GPU kernels reading mmap-loaded weights can return incorrect output, which makes test-llama-archs (and real inference) intermittently wrong. GGML_CUDA_ENABLE_UNIFIED_MEMORY=1 uses managed memory, which restores coherence. Remove once the underlying ROCm/HIP issue is fixed. * test-llama-archs: skip jamba on HIP backend jamba produces incorrect output (~0.55 NMSE vs CPU) on the HIP backend on RDNA3.5 (gfx1151); the SSM kernels need separate investigation. Skip it for now, matching the existing per-backend carve-outs (WebGPU), so the ROCm CI can run the test for the remaining architectures. * ci: use HIP_LAUNCH_BLOCKING for ROCm gfx1151 job The gfx1151 ROCm CI job produced incorrect inference output (qwen3 perplexity ~88 vs ~9.4) due to an async-execution correctness issue in the HIP path. Serializing kernel launches with HIP_LAUNCH_BLOCKING=1 restores correctness. This replaces the earlier GGML_CUDA_ENABLE_UNIFIED_MEMORY workaround, which did not fix batched inference. * test-backend-sampler: skip top-k subtests on HIP backend The ROCm backend does not support the TOP_K/ARGSORT op at vocab scale (no CUB; bitonic argsort is capped at ncols <= 1024), so top-k/top-p backend samplers cannot be offloaded. The penalties, set_sampler, mixed, and top_p subtests assert that offload happened, so they fail on HIP. Skip them until TOP_K is supported on the ROCm backend. * Update tests/test-backend-sampler.cpp Co-authored-by: Aaron Teo <taronaeo@gmail.com> * Update tests/test-backend-sampler.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> Co-authored-by: Aaron Teo <aaron.teo1@ibm.com> Co-authored-by: Jim Wu <ywu@xilinx.com> Co-authored-by: Aaron Teo <taronaeo@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> </details> **Website:** - <https://llama.app> **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b10293/llama-b10293-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23780) - [macOS Intel (x64)](https://github.com/ggml-org/llama.cpp/releases/download/b10293/llama-b10293-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b10293/llama-b10293-xcframework.zip) **Linux:** - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b10293/llama-b10293-bin-ubuntu-x64.tar.gz) - [Ubuntu arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b10293/llama-b10293-bin-ubuntu-arm64.tar.gz) - [Ubuntu s390x (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b10293/llama-b10293-bin-ubuntu-s390x.tar.gz) - [Ubuntu x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/b10293/llama-b10293-bin-ubuntu-vulkan-x64.tar.gz) - [Ubuntu arm64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/b10293/llama-b10293-bin-ubuntu-vulkan-arm64.tar.gz) - [Ubuntu x64 (ROCm 7.2)](https://github.com/ggml-org/llama.cpp/releases/download/b10293/llama-b10293-bin-ubuntu-rocm-7.2-x64.tar.gz) - [Ubuntu x64 (OpenVINO)](https://github.com/ggml-org/llama.cpp/releases/download/b10293/llama-b10293-bin-ubuntu-openvino-2026.2.1-x64.tar.gz) - [Ubuntu x64 (SYCL FP32)](https://github.com/ggml-org/llama.cpp/releases/download/b10293/llama-b10293-bin-ubuntu-sycl-fp32-x64.tar.gz) - [Ubuntu x64 (SYCL FP16)](https://github.com/ggml-org/llama.cpp/releases/download/b10293/llama-b10293-bin-ubuntu-sycl-fp16-x64.tar.gz) **Android:** - [Android arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b10293/llama-b10293-bin-android-arm64.tar.gz) **Windows:** - [Windows x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b10293/llama-b10293-bin-win-cpu-x64.zip) - [Windows arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b10293/llama-b10293-bin-win-cpu-arm64.zip) - [Windows arm64 (OpenCL Adreno)](https://github.com/ggml-org/llama.cpp/releases/download/b10293/llama-b10293-bin-win-opencl-adreno-arm64.zip) - [Windows x64 (CUDA 12)](https://github.com/ggml-org/llama.cpp/releases/download/b10293/llama-b10293-bin-win-cuda-12.4-x64.zip) - [CUDA 12.4 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/b10293/cudart-llama-bin-win-cuda-12.4-x64.zip) - [Windows x64 (CUDA 13)](https://github.com/ggml-org/llama.cpp/releases/download/b10293/llama-b10293-bin-win-cuda-13.3-x64.zip) - [CUDA 13.3 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/b10293/cudart-llama-bin-win-cuda-13.3-x64.zip) - [Windows x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/b10293/llama-b10293-bin-win-vulkan-x64.zip) - [Windows x64 (OpenVINO)](https://github.com/ggml-org/llama.cpp/releases/download/b10293/llama-b10293-bin-win-openvino-2026.2.1-x64.zip) - [Windows x64 (SYCL)](https://github.com/ggml-org/llama.cpp/releases/download/b10293/llama-b10293-bin-win-sycl-x64.zip) - [Windows x64 (HIP)](https://github.com/ggml-org/llama.cpp/releases/download/b10293/llama-b10293-bin-win-hip-radeon-x64.zip) **openEuler:** - [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23705) - openEuler x86 (310p) - openEuler x86 (910b, ACL Graph) - openEuler aarch64 (310p) - openEuler aarch64 (910b, ACL Graph) **UI:** - [UI](https://github.com/ggml-org/llama.cpp/releases/download/b10293/llama-b10293-ui.tar.gz)