koboldcpp/ci
Jim Wu a1f96d4fc2
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>
2026-08-06 10:43:26 +02:00
..
README-MUSA.md musa: upgrade musa sdk to 4.3.0 (#16240) 2025-09-26 02:56:38 +02:00
README.md ci : migrate ggml ci to self-hosted runners (#16116) 2025-09-21 16:50:45 +03:00
run.sh ci : onboard AMD ROCm CI with gfx1151 fixes (#26544) 2026-08-06 10:43:26 +02:00

CI

This CI implements heavy-duty workflows that run on self-hosted runners. Typically the purpose of these workflows is to cover hardware configurations that are not available from Github-hosted runners and/or require more computational resource than normally available.

It is a good practice, before publishing changes to execute the full CI locally on your machine. For example:

mkdir tmp

# CPU-only build
bash ./ci/run.sh ./tmp/results ./tmp/mnt

# with CUDA support
GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt

# with SYCL support
source /opt/intel/oneapi/setvars.sh
GG_BUILD_SYCL=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt

# with MUSA support
GG_BUILD_MUSA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt

# etc.

Adding self-hosted runners

  • Add a self-hosted ggml-ci workflow to .github/workflows/build.yml with an appropriate label
  • Request a runner token from ggml-org (for example, via a comment in the PR or email)
  • Set-up a machine using the received token (docs)
  • Optionally update ci/run.sh to build and run on the target platform by gating the implementation with a GG_BUILD_... env