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20 changes: 20 additions & 0 deletions .github/workflows/build-self-hosted.yml
Original file line number Diff line number Diff line change
Expand Up @@ -71,6 +71,26 @@ jobs:
nvidia-smi
GG_BUILD_CUDA=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp

gpu-rocm:
runs-on: [self-hosted, Linux, AMD]

steps:
- name: Clone
id: checkout
uses: actions/checkout@v6

- name: Test
id: ggml-ci
# GGML_CUDA_ENABLE_UNIFIED_MEMORY=1: workaround for a coherence issue on
# integrated RDNA3.5 (gfx1151) where GPU kernels reading mmap-loaded
# weights can return incorrect output. Unified (managed) memory restores
# coherence. Remove once the underlying ROCm/HIP issue is fixed.
env:
GGML_CUDA_ENABLE_UNIFIED_MEMORY: "1"
run: |
rocminfo
GG_BUILD_ROCM=1 GG_BUILD_AMDGPU_TARGETS=gfx1151 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp

gpu-vulkan-nvidia-cm:
runs-on: [self-hosted, Linux, NVIDIA]

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5 changes: 4 additions & 1 deletion ci/run.sh
Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,9 @@
# # with CUDA support
# GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
#
# # with ROCm support
# GG_BUILD_ROCM=1 GG_BUILD_AMDGPU_TARGETS=gfx1151 bash ./ci/run.sh ./tmp/results ./tmp/mnt
#
# # with SYCL support
# GG_BUILD_SYCL=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
#
Expand Down Expand Up @@ -89,7 +92,7 @@ if [ ! -z ${GG_BUILD_CUDA} ]; then
fi

if [ ! -z ${GG_BUILD_ROCM} ]; then
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_HIP=ON"
CMAKE_EXTRA="${CMAKE_EXTRA} -DCMAKE_HIP_COMPILER=$(hipconfig -l)/clang -DGGML_HIP=ON -DGGML_HIP_ROCWMMA_FATTN=ON"
if [ -z ${GG_BUILD_AMDGPU_TARGETS} ]; then
echo "Missing GG_BUILD_AMDGPU_TARGETS, please set it to your GPU architecture (e.g. gfx90a, gfx1100, etc.)"
exit 1
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6 changes: 5 additions & 1 deletion ggml/src/ggml-cuda/ggml-cuda.cu
Original file line number Diff line number Diff line change
Expand Up @@ -4033,7 +4033,11 @@ static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cud
continue;
}
#ifndef NDEBUG
assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device));
// On integrated GPUs (APUs, e.g. RDNA3.5) the scheduler may place a
// node's output on the host-visible buffer, which the compute path
// handles. Allow that here, mirroring the src-tensor check below.
assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) ||
(integrated && ggml_backend_buft_is_cuda_host(node->buffer->buft)));
for (int j = 0; j < GGML_MAX_SRC; j++) {
if (node->src[j] != nullptr) {
assert(node->src[j]->buffer);
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8 changes: 8 additions & 0 deletions tests/test-llama-archs.cpp
Original file line number Diff line number Diff line change
Expand Up @@ -435,6 +435,14 @@ static bool arch_supported(const llm_arch arch) {
}
#endif // GGML_USE_WEBGPU

// FIXME: jamba produces incorrect output (~0.55 NMSE vs CPU) on the HIP
// backend on RDNA3.5 (gfx1151); the SSM kernels need investigation.
#ifdef GGML_USE_HIP
if (arch == LLM_ARCH_JAMBA) {
return false;
}
#endif // GGML_USE_HIP

return true;
}

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