* docker: add OCI image labels to all published images
* docker: propagate OCI labels as manifest and index annotations
* docker: drop hardcoded org URL and revert accidental intel version bump
The OCI image url and source are now driven by build args with a sensible default. The workflow passes the actual repository url so fork builds get labels pointing at the fork instead of upstream. Also restores the IGC, compute runtime, and IGDGMM versions in the intel Dockerfile labeled stage which I accidentally bumped in the first commit.
* docker: add skip_s390x workflow_dispatch input for fast test runs
Lets maintainers and PR authors trigger the docker workflow without the s390x build target, which depends on the IBM Z runner and is by far the slowest job in the matrix. The flag filters the s390x row out of the build matrix before merge_matrix is derived, so the merge job sees a consistent shape too.
Signed-off-by: Samaresh Kumar Singh <ssam3003@gmail.com>
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Signed-off-by: Samaresh Kumar Singh <ssam3003@gmail.com>
* CI: Properly install rocwmma for hip builds
on windows we now windows install rocwmma from ubuntu pacakges
* CI: update linux rocm docker build to use rocm 7.0
* rocm.Dockerfile: added gfx1200,gfx1201 architectures to support AMD Radeon RX 9000 series
https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.1/reference/system-requirements.html#rdna-os
states the Radeon RX 9000 series is supported support from Ubuntu 24.04.2, and the dockerfile is using 24.04 which is ROCm 6.4.
This fixed the `ROCm error: invalid device function` I was getting when trying to use the rocm container.
This commit adds support for MFMA instructions to MMQ. CDNA1/GFX908 CDNA2/GFX90a and CDNA3/GFX942 are supported by the MFMA-enabled code path added by this commit. The code path and stream-k is only enabled on CDNA3 for now as it fails to outperform blas in all cases on the other devices.
Blas is currently only consistently outperformed on CDNA3 due to issues in the amd-provided blas libraries.
This commit also improves the awareness of MMQ towards different warp sizes and as a side effect improves the performance of all quant formats besides q4_0 and q4_1, which regress slightly, on GCN gpus.
Aldehir Rojas