01bd52bdb5
[CK] Fix gfx950 AITER Sync Regressions MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Summary Fixes three gfx950 regressions in the AITER downstream CI that surfaced after the internal/gfx1250 re-sync (ROCm/rocm-libraries#6978): > **Companion aiter PR:** ROCm/aiter#3392 — host-side adaptations (`Kernel::BlockSize()` `constexpr` drops, blockscale `KBatch=1` clamp) plus the CK submodule bump used to validate these fixes together. - **FlyDSL MoE AOT cache miss** — the AITER MoE tests run with `check_aot_cache=True` and fail on any FlyDSL JIT cache miss, but the CI never pre-compiles the FlyDSL MoE kernels, so gfx950 always misses. Pre-compile them at the start of the AITER test stage. - **`buffer.load.lds.v4i32` link error** — ROCm/rocm-libraries#6978 reintroduced a clang-version guard mapping `llvm.amdgcn.raw.buffer.load.lds` to a `.v4i32`-suffixed name. That name exists in no LLVM (the rsrc operand is a fixed, non-overloaded `<4 x i32>`, so the intrinsic is never type-mangled), so gfx950 4-DWORD direct-to-LDS (e.g. fp4 MoE bpreshuffle) fails to link with `lld: undefined symbol: llvm.amdgcn.raw.buffer.load.lds.v4i32`. Use the canonical plain name unconditionally. - **mixed-precision flatmm warp-GEMM call** — ROCm/rocm-libraries#6978 generalized the scaled `WarpGemmImpl::operator()` from a fixed `<index_t opselA, index_t opselB>` signature to a variadic `<typename... Params>` one and updated the `mx_flatmm` pipeline to pass the op-selectors as `OpSelA<>`/`OpSelB<>` types, but missed the mixed-precision flatmm pipeline (`F8xMXF4`/`F16xMXF4`), which still passed raw integer op-selectors. These no longer bind to `typename... Params` (`error: no matching member function for call to 'operator()'`), breaking compilation of the fp8/bf16 × fp4 cktile MoE gemm1 instances on gfx950 (aiter `test_moe_2stage`). Wrap the op-selectors in `OpSelA<>`/`OpSelB<>`. ## Changes - `Jenkinsfile`: pre-compile the FlyDSL MoE AOT cache (`python3 aiter/aot/flydsl/moe.py`) before the AITER tests. - `include/ck/utility/amd_buffer_addressing_builtins.hpp` and `include/ck_tile/core/arch/amd_buffer_addressing_builtins.hpp`: drop the `__clang_major__` guard and always use `__asm("llvm.amdgcn.raw.buffer.load.lds")`. The plain name is the canonical one for all sizes including the gfx950 16-byte form, as the upstream LLVM gfx950 tests confirm. - `include/ck_tile/ops/flatmm/pipeline/mixed_prec_flatmm_pipeline_agmem_bgmem_creg_v1.hpp`: wrap the warp-GEMM op-selectors in `OpSelA<>`/`OpSelB<>` at the five call sites, matching the `mx_flatmm` pipeline. ## Test plan Validated via CI.
Composable Kernel Tile
concept
ck_tile provides a programming model with templated abstractions to enable users to implement performance-critical kernels for machine learning workloads. introduces following basic concepts to help users building your own operator
- tensor coordinate transformation, this is the core concept of layout/index transform abstraction in both compiler time and run time.
- tile-based programming model, including tile-level api and the concept of distributed tensor.
ck_tile is independently from the old ck, located under /include/ck_tile. You don't need to include anything from old CK, ck_tile has similiar (indeed almost the same) implementations for users to build operators. We will have a transition period to pull everything from old ck into ck_tile, stay tuned.
component
ck_tile is splitted into several componenets including core, host, ops/gemm, ops/fmha... each component you only need to include a single header (e.g #include "ck_tile/core.hpp", #include "ck_tile/ops/fmha.hpp") then you are able to use the function/structure inside (different from old ck)
[core]
ck_tile/core contains all the basic data structure and function to build the kernel, you can only include this header and build your own operators that utilizing all the basic building blocks introduced in ck.
core/container
- array, store runtime variables with fixed length (tensor index, register buffer, etc...)
- tuple, same as std::tuple, hold different type of data, and one of the solution to achieve multiple buffer.
- sequence, compile time integer sequence used to build various internal structures, or to describe tile size
- other convenient structure build on top of above 3
core/numeric
- gpu data type like
fp16_t,bf16_t,fp8_t... and the conversion between each other - constexpr integer similiar to std::integral_constant to be used as compile time integer.
- math functions and numeric utilities
core/algorithm
- coordinate transformation system, used to build tensor transform and compile time indexing. This is the core idea introduced in old
ckto describe how a tensor is build by several basic transform primitives likemerge/unmerge/embedetc... and how we indexing into a ND tensor that finally mapped to 1D memory offset.
core/tensor
- tensor descriptor, to describe how a ND tensor
- distributed tensor, describe the storage of this tensor, and the distribution of how a collection of threads collaborately work for this tensor.
- tile level API, including
load_tile,store_tile,shuffle_tile,slice_tile, etc...
[host]
ck_tile/host contains all the host side utilities to launch a kernel, create the device buffer, and some reference implementations. This can be used to create examples (like that under ck_tile example folder) and simple executable to invoke this kernel, so if you only need ck_tile to build your own device library then it's OK to not include this. Based on this, it is recommended to include the specific header you needed under this folder to avoid including unwanted headers (e.g, only include ck_tile/host/kernel_launch.hpp), unless you are writing a host executable.
[ops/gemm, ops/fmha, ops/reduce...]
our implementation of different device operators.
- warp, warp tile level operator
- block, block tile level operator
- pipeline, pipeline that can achieve a customized tile level mainloop (or epilogue). By switching different pipeline to the kernel template you can have different kind of pipeline optimizations.
- kernel, template interface for users to instantiate a particular kernel
[ops/epilogue]
epilogue part of our kernel. We may extend this epilogue part to let users to build their own cutomized epilogues.
[ref]
reference implementation of cpu or gpu. This folder is supposed to include a specific header on demand.
examples
currently we put all ck_tile related example under /example/ck_tile folder. Please check each example's subfolder.