[CK] Pre-emptively add groovy/ folder and skip TheRock CI
filter (#8378)
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## Motivation
The CK Groovy library is growing and will be reorganized into a
self-describing `groovy/` folder rather than living under `src/` and
`vars/`. This PR creates that folder pre-emptively and adds it to the
TheRock CI skip-list so that future Groovy additions do not
unnecessarily trigger TheRock builds.
## Technical Details
- Added `projects/composablekernel/groovy/` with a `.gitkeep` to
establish the directory in the repo.
- Added `"projects/composablekernel/groovy/*"` to
`SKIPPABLE_PATH_PATTERNS` in `.github/scripts/therock_configure_ci.py`
alongside the existing `vars/*` entry, ensuring changes confined to
Groovy pipeline code are recognized as non-therock-relevant and skip the
TheRock CI pipeline.
## Test Plan
No code logic was changed. Verified that `therock_configure_ci.py`
pattern list is consistent with the existing `vars/*` skip entry and
that the new pattern follows the same glob convention.
## Test Result
N/A — directory scaffolding and CI filter only; no functional code
affected.
## Submission Checklist
- [ ] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
[CK] Re-enable HIPRTC codegen tests for all CK PRs.
## Motivation
At the time when we introduced the smart test filter to only build and
run tests affected by the PR changes, we disabled the client examples,
which required full CK build, and also the hiprtc tests that were
grouped with the client examples. This caused a few PRs to sneak through
that caused the hiprtc compilation to fail.
By restoring the hiprtc tests in all PRs, we should close this gap.
## Technical Details
<!-- Explain the changes along with any relevant GitHub links. -->
## Test Plan
<!-- Explain any relevant testing done to verify this PR. -->
## Test Result
<!-- Briefly summarize test outcomes. -->
## Submission Checklist
- [ ] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
Revert "[CK_TILE] Implement RTC API for a subset of FMHA
functionality for MGX" (#8333)
Reverts ROCm/rocm-libraries#6086
Need to revert as the codegen test for fmha is failing due to including
std header:
2026-06-11T22:36:03.673Z] In file included from
/tmp/comgr-953928-0-473822/include/ck/host/device_fmha_fwd/fmha_fwd_wrapper.hpp:8:
[2026-06-11T22:36:03.673Z] In file included from
/bin/../lib/gcc/x86_64-linux-gnu/13/../../../../include/c++/13/cmath:49:
[2026-06-11T22:36:03.673Z] In file included from
/bin/../lib/gcc/x86_64-linux-gnu/13/../../../../include/c++/13/bits/std_abs.h:38:
[2026-06-11T22:36:03.673Z] /usr/include/stdlib.h:32:10: fatal error:
'stddef.h' file not found
[2026-06-11T22:36:03.673Z] 32 | #include <stddef.h>
[2026-06-11T22:36:03.673Z] | ^~~~~~~~~~
The ck_tile headers were never prepped for hiprtc compilation.
Users/tlakshma/ck/tile engine develop
## Motivation
This PR adds multiple new GPU kernel benchmarking operations to the CK
Tile Engine, expanding its coverage of GEMM-family operations:
- **gemm_multi_abd**: GEMM with multiple A, B, and D tensors, enabling
epilogue patterns such as scale/bias fusion.
- **batched_contraction**: Batched tensor contraction supporting
multi-dimensional batch (G), M, N, and K dimensions, targeting workloads
where the contraction indices span more than one logical axis.
- **mx_gemm**: MX-format GEMM with microscaling (e8m0) scale tensors.
- **gemm_rowcolquant**: Block-scale GEMM with row/column quantization.
- **gemm_tensor_quant**: Block-scale GEMM with tensor quantization.
- **grouped_gemm_rowcolquant**: Grouped GEMM with row/column
quantization.
- **grouped_gemm_tensorquant**: Grouped GEMM with tensor quantization.
- **batched_gemm**: Batched GEMM benchmarking support.
## Technical Details
### gemm_multi_abd
- New subdirectory: tile_engine/ops/gemm/gemm_multi_abd/
- CMakeLists.txt follows the same individual-target pattern as
gemm_universal / gemm_multi_d.
- gemm_multi_abd_instance_builder.py subclasses GemmKernelBuilder from
the shared gemm_instance_builder.py.
- gemm_multi_abd_benchmark.py delegates to the shared GemmBenchmark
parent class.
- Configs: default_config.json, default_ci_config.json,
user_provided_config.json.
- Supported GPU targets: gfx90a, gfx942, gfx950, gfx1201.
### batched_contraction
- New subdirectory: tile_engine/ops/gemm/batched_contraction/
- Extends GemmKernelBuilder via BatchedContractionKernelBuilder, adding
num_dim_g, num_dim_m, num_dim_n, num_dim_k, num_d_tensors, and
elementwise_function parameters.
- Layout string uses 3-character encoding (A+B+E), e.g. rcr.
- Self-contained benchmark sweep driver
(batched_contraction_benchmark.py) with JSON/CSV export and best-kernel
selection.
- Supported GPU targets: gfx90a, gfx942, gfx950.
### mx_gemm
- New subdirectory: tile_engine/ops/gemm/mx_gemm/
- Supports MX-format (e8m0) microscaling for A and B scale tensors.
### block_scale_gemm (gemm_rowcolquant, gemm_tensor_quant)
- New subdirectory: tile_engine/ops/gemm/block_scale_gemm/
- gemm_rowcolquant: row/column quantization epilogue.
- gemm_tensor_quant: tensor-level quantization epilogue.
### grouped_gemm_quant (grouped_gemm_rowcolquant,
grouped_gemm_tensorquant)
- New subdirectory: tile_engine/ops/gemm/grouped_gemm_quant/
- grouped_gemm_rowcolquant: grouped GEMM with row/column quantization.
- grouped_gemm_tensorquant: grouped GEMM with tensor quantization.
### batched_gemm
- New subdirectory: tile_engine/ops/gemm/batched_gemm/
- Batched GEMM benchmark support wired into the sampling/active-op
lists.
All new ops are registered in op_weights.json for budget allocation and
wired into the active-op sampling lists in CMakeLists.txt.
## Test Plan
<!-- Explain any relevant testing done to verify this PR. -->
## Test Result
<!-- Briefly summarize test outcomes. -->
## Submission Checklist
- [ ] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
Add cluster launch in test ck_tile mx gemm tdm wmma
## Motivation
Add cluster launch test in test_ck_tile_mx_gemm_pipeline_tdm_wmma on
gfx1250, so that we can check the performance on gfx1250 hardware.
## Technical Details
Added Out-of-bounds guard in RunGemm of MxGemmKernel to skip blocks
padded by cluster alignment.
Add ClusterEnable/ClusterDisable aliases and extend the tuple in
test_mx_gemm_pipeline_kernel_types.hpp by adding two kernel types with
ClusterEnable for F8 CompTDMV1 and CompTDMV2 respectively. The existing
F4 non-ClusterLaunch kernel types have issue to be fixed, so this PR
does not include F4 cases.
Read ClusterLaunch from the tuple in test_mx_gemm_pipeline_util.hpp.
Update invoke_mx_gemm to branch on ClusterLaunch, including Add cluster
size constants, Switch GemmShape type, TilePartitioner type, and the
kernel launch call.
## Test Plan
Tested the changes on gfx1250 FFM.
## Test Result
The added kernel types (instances) passed the tests on gfx1250 FFM.
## Submission Checklist
- [x ] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
[CK_TILE] Implement RTC API for a subset of FMHA
functionality for MGX (#6086)
## Motivation
Introduce a wrapper for the FmhaFwdKernel, for use in real time
compilation in MIGraphX.
## Technical Details
The intent of the API is to provide multiple instances of the
FmhaFwdKernelWrapper, suitable for a particular problem definition.
At the moment the wrapper only supports bias and causal masking, feature
expansion will come in a future pr.
The usage pattern is, in short:
1. Define fmha_fwd::Problem (input dimensions, data type, etc)
2. Fetch Solutions for target architecture (currently only gfx942) based
on Problem.
The solutions contain a map of template -> template parameter and can be
converted to a string representing the full instantiation of
FmhFwdKernelWrapper e.g. `ck_tile::FmhaFwdWrapper<ck_tile::fp16_t, 128,
64, 16, 32, 32, 32, 4, 1, 1, 4, 1, 1, 32, 32, 16, 32, 32, 16, false,
true, false, true, true, true, true, ck_tile::FmhaPipelineTag::QR>`
3. The instance can then be used in an RTC kernel. The kernel needs to:
* Construct a Descriptor (containing descriptions of all input tensors)
* Call IsValid() on the descriptor to check if the instance is
applicable. Note that this is constexpr by design so that it can fail
the kernel compilation as a signal that the kernel is not applicable.
* Pass the descriptor and input pointers to the wrapper Run method.
A more detailed example of usage can be found in
codegen/test/fmh_fwd.cpp
Beside work on creating the wrapper and the supporting API, the PR also
contains some changes necessary to enable compilation with HIPRTC.
The contents of the CK tile headers are embedded in a binary file which
is used to pass the header files as strings to HIPRTC.
Many of the ck tile headers contain host only code which leads to
compilation failures.
ck_tile_headers_preprocessor goes through the embedded headers and
removes the bodies of host only functions, thereby eliminating the
compilation failures.
## Test Plan
<!-- Explain any relevant testing done to verify this PR. -->
## Test Result
<!-- Briefly summarize test outcomes. -->
## Submission Checklist
- [ ] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
[CK] Padding on K for global load for grouped conv bwd data
(#8272)
## Motivation
Fix incorrect results caused by lack of padding during global load in
grouped convolution backward data kernel. It is needed since there is no
OOB check for global load.
## Technical Details
Add padding needed for global load which not use OOB check.
## Test Plan
test_grouped_convnd_bwd_data*
## Test Result
Passed locally
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
[CK dispatcher] - LGBM predict data_type FLOAT32->FLOAT64 in
ml_heuristic (#8132)
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## Summary
`ml_heuristic.hpp` calls `LGBM_BoosterPredictForMat(...,
/*data_type=*/0, ...)` (`C_API_DTYPE_FLOAT32`) against a
`std::array<double, NUM_FEATURES>` feature buffer. LightGBM reinterprets
the 8-byte doubles as 4-byte floats → invalid predictions → the
heuristic's argmax always tie-breaks to the first/smallest enumerated
config.
**Fix:** `data_type 0 → 1` (`C_API_DTYPE_FLOAT64`), matching the
`double` buffer. After the fix, predictions vary and track real TFLOPS
(the model correctly prefers larger tiles).
## Verification
- The feature buffer `f` is `std::array<double, NUM_FEATURES>`
(NUM_FEATURES = 72) → `f.data()` is a `double*`.
- The changed `0` is the 3rd positional `data_type` argument (not
`nrow`/`ncol`/`is_row_major`).
One-line functional change.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
[CK] increase time limit for fmha_bwd tests to prevent
timeouts (#8241)
## Motivation
Observed a CI failure due to fmha_bwd test timeout which never happened
before. Going to increase the time limit for the test to prevent any
further CI failures.
## Technical Details
<!-- Explain the changes along with any relevant GitHub links. -->
## Test Plan
<!-- Explain any relevant testing done to verify this PR. -->
## Test Result
<!-- Briefly summarize test outcomes. -->
## Submission Checklist
- [ ] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
[CK TILE] Fix performance regression caused by Dispatcher
codegen compiler flag. (#8019)
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## Motivation
Currently CK Tile two codegen paths: CK Builder and CK Tile Dispatcher.
The CK Tile Dispatcher codegen uses an additional compiler flag that is
not present in the CK Builder codegen workflow. The additional compiler
flag can cause performance regression for so instances as it disables
relevant compiler optimizations.
## Technical Details
Removed compiler flag `-mllvm -enable-noalias-to-md-conversion=0` from
the CMakeLists.txt that creates instance library from Dispatcher
codegen.
## Test Plan
Required testing is contained in the CI/CD pipeline.
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
[CK] Fix scale init in profile_grouped_conv_fwd_outelementop
(#8208)
## Motivation
Wrong scale initialization caused random errors on CI.
## Technical Details
InvScale was initialized by 0 what caused nans during division. At now
zero are excluded from randing.
## Test Plan
TestGroupedConvndFwdConvInvscale3d
## Test Result
Passed in 100 runs
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
AICK-1400
[CK] Magic division for long_index_t
## Motivation
Improve performance for long_index_t kernels
## Technical Details
Support magic division for long_index_t
## Test Plan
test_grouped_convnd*
## Test Result
Passed locally
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
AICK-1386
[CK_Tile] Add wmma_bf16f32_16x16x32_bf16 warp-gemm test
(#8035)
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## Summary
Adds the warp-gemm unit test for `wmma_bf16f32_16x16x32_bf16`. **Stacked
on #8028** (the API change) and based on its branch, so #8028 shows the
isolated API diff and this PR shows just the test.
## Test
gfx125-guarded `WmmaBf16f32.ResidualPrecisionContrast`: computes `Y_bf16
= X_bf16·W_bf16 + R_fp32` via `WarpGemm::mac_downconvert`, compares
against an fp32 reference (within bf16 tolerance), and asserts it is at
least as accurate as the bf16-accumulate path — i.e. it demonstrates the
precision benefit of the fp32 accumulator (`C`) carried into the fused
bf16 down-convert.
Passes on gfx1250.
[ck] Unify Build_CK and buildHipClangJob into buildAndTest
(#8108)
## Motivation
`projects/composablekernel/vars/ck.groovy` had two near-identical build
functions, `buildHipClangJob` (lean: static checks, FMHA, tile-engine,
conv) and `Build_CK` (main per-arch matrix). This removes the
duplication and fixes a latent GitHub-status bug that lived in both.
## Technical Details
- Merged both into one `buildAndTest(Map conf)` gated by an explicit
`is_main_build` flag (default `false` = lean path; `true` adds the GPU
check + arch-gated inductor/perf/hipTensor; only `runBuildCKAndTests`
sets it).
- Deleted the `Build_CK_and_Reboot` / `buildHipClangJobAndReboot`
wrappers (they only logged and re-threw); all 13 call sites now call
`buildAndTest` directly.
- Widened the shared `catch` to `Exception` so build / image-pull / "GPU
not found" failures report **failure** instead of leaving the check
stuck **pending** (failing stages now go red).
- Removed the dead `no_reboot` key. No change to what is built or
tested.
## Test Plan
- Jenkins linter on the `Jenkinsfile`.
- One branch run covering both paths (per-arch matrix + lean stages),
spot-checking gfx1250 and a nogpu stage.
## Test Result
- Verified statically: no `buildHipClangJob*` / `Build_CK*` references
remain; `buildAndTest` defined once, all call sites wired.
- Pending: linter + branch run before merge.
## Submission Checklist
- [ ] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
[CK Tile] Stream-K RDNA Support
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## Motivation
Currently, CK Tile Stream-K only supports CDNA architectures. This
change adds Stream-K support on RDNA3/3.5 and RDNA4 architectures.
## Technical Details
Stream-K currently has 3 reduction strategies: 1) atomics, 2) linear,
and 3) tree. The linear and tree reductions require inter-workgroup
communication to a global flags buffer and a global partials buffer. To
ensure cache coherency, we use cache modifiers to skip cache levels that
are not visible to all workgroups. On CDNA architectures, scalar load
and scalar store instructions are available, which we use to read and
write to the flags buffer with appropriate cache skipping modifiers.
However, RDNA architectures do not support scalar store instructions, so
workgroups must use a buffer store instruction to write to flags.
Additionally, cache modifiers differ between CDNA and RDNA; they also
differ between RDNA3 and RDNA4. Given this information, the main changes
are as follows:
- Added RDNA flag signaling: Use buffer store instructions for writing
to global flags buffer
- Add appropriate cache modifiers for reading and writing to flags and
partials:
- RDNA3 (gfx11): Use `glc | dlc` coherence flags
- RDNA4 (gfx12): Use `DEVICE` coherence scope
- SFINAE-guarded overloads: Added compile-time dispatch for
`SignalStorePartialDone()` and `WaitStorePartialDone()` based on target
architecture
- RDNA alignment requirements: Increased flags buffer alignment from
128B to 256B due to RDNA cache line size
**A note about the `amd_buffer_coherence_enum`:**
- **Problem:** The `amd_buffer_coherence_enum` uses preprocessor
conditionals (`#if defined(__gfx12__)`) to define architecture-specific
values. Template specializations reference enum values from different
architectures (e.g., `glc_dlc` for GFX11). Due to C++ two-phase name
lookup, non-dependent names are resolved during template parsing
regardless of which architecture is being compiled, causing compilation
failures when referenced values do not exist in the active preprocessor
branch.
- **Temporary Solution**: Added compatibility enum values to each
architecture block. For example, I added `glc_dlc` in the `__gfx12__`
block. I will create a ticket to refactor this enum with a design that
has better scalability and tries to avoid the use of preprocessor
conditionals.
## Test Plan
### Summary
gtests were added to test wmma variants of Stream-K. These tests were
stressed tested locally on gfx11 and gfx12.
### More details
This PR makes the following changes/additions to the Stream-K gtests:
- Split tests into MFMA (CDNA) and WMMA (RDNA) variants
- Added 16 WMMA kernel types: FP16/BF16/FP8/BF8 × Linear/Tree reduction
- WMMA uses 16×16×16 wave tiles for RDNA (this is the only tile size
supported on RDNA)
- Fixed RDNA WGP mode: multiply multiProcessorCount by 2 for actual CU
count
- As described in [HIP
documentation](https://rocm.docs.amd.com/projects/HIP/en/docs-7.2.0/doxygen/html/group___global_defs.html#ggacc0acd7b9bda126c6bb3dfd6e2796d7ca3ac50041beb59111a5c76edf03da0898),
when in Workgroup Processor (WGP) mode, the value of
`hipDeviceAttributeMultiprocessorCount` is half of CUs, because a single
WGP contains two CUs. The default mode on RDNA is WGP mode, so when
creating (M, N, K) instances for gtests using the CU count, we need to
multiply the CU count by 2 to get the correct value. This is not needed
in the kernel host code, because the occupancy ensures that overall
`max_active_wgs` is correct.
## Test Result
All tests pass locally.
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
Using named functors instead of lambdas
## Motivation
Currently, in block-level GEMM pipelines, there is significant code
repetition for prefetching and tail handling, where lambda functions
create a unique instantiations at each call. This includes repeated
static_for instantiations and large loops such as MRepeat. Each
repetition results in additional instantiations, which increases
compilation time and binary bloat.
## Technical Details
Refactor repeated code blocks into named functors so the compiler can
reuse already instantiated code instead of generating multiple copies.
Scope of changes:
1. WMMAOPS pipeline internals:
projects/composablekernel/include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_wmmaops_base.hpp,
projects/composablekernel/include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_wmmaops_v1.hpp,
projects/composablekernel/include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_wmmaops_v3.hpp
2. XDLOPS and preshuffle pipeline variants across
projects/composablekernel/include/ck/tensor_operation/gpu/block
(v1/v2/v3/v4/v5, scale, dequant, gufusion, moe, mx, blockscale,
skip-b-lds, dpp, xdlops)
Shared functor file:
projects/composablekernel/include/ck/utility/vector_load_functor.hpp
## Test Plan
Note that the provided compilation traces by -ftime-trace do not report
unnamed lambda instantiations, so a clear baseline for instantiation
counts cannot be established. As a result, the impact of this change
will be evaluated based on runtime performance rather than direct
instantiation-count comparisons.
## Test Result
The effects of this were timed by the compilation of a single HIP object
through an example (grouped_gemm_wmma_splitk_fp16.cpp). The average user
time and speedup of this using the average of 100 compilations is:
- Mean compile time before the changes: 37.734 s
- Mean compile time after: 32.087 s
- Speedup: 17.6%
Ran a full CK compilation on Alola with the following results:
| Metric | Before (min) | After (min) | Absolute Reduction (min) | %
Reduction |
| ------ | ------------ | ----------- | ------------------------ |
[CK] Remove Stream-K from old CK
## Motivation
Since Stream-K has a CK Tile implementation, we no longer need Stream-K
in old CK. Hence, this PR removes Stream-K from old CK.
## Technical Details
All Stream-K artifacts in old CK have been removed including examples,
tests, kernels, and CK profiler artifacts.
## Test Plan
Ran a CI run on the branch before publishing PR.
## Test Result
All tests passed.
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
Co-authored-by: Claude Sonnet 4 <noreply@anthropic.com>
[CK Tile] Add WAVELET pipeline for forward grouped
convolution (#8009)
## Motivation
CK Tile forward grouped convolution trails classic CK on 3x3
convolutions whose
output-channel count is not divisible by 8, where the narrow output
store limits
the compute CShuffle epilogue. This ports the WAVELET pipeline (added
for
backward-weight in #7937) to the forward kernel to close that gap.
## Technical Details
- Kernel (`grouped_convolution_forward_kernel.hpp`): WAVELET
load/math-wave wiring,
mirroring the backward-weight implementation; the non-WAVELET path is
unchanged.
- Generator: implement `parse_native_fwd_instance`, the forward
native-instance parser.
- Registered WAVELET instances: profiler bf16 3 / fp16 5, tests 1 each.
WAVELET requires input channels divisible by 8 (it does not apply to
depthwise).
The bf16/fp16 instance asymmetry is intentional and measured: the VecC=8
tiles
never beat the compute pool in bf16 but win about 20% of divisible-by-8
3x3 shapes
in fp16, so VecC=8 is registered for fp16 only.
## Test Plan
- Correctness (CPU reference) for every registered profiler instance,
across VecC variants.
- Per-shape best-instance performance sweep over the 34 RetinaNet shapes
(bf16) and
a 200-shape cross-model sweep (bf16 and fp16), compared against classic
CK.
## Test Result
- Correctness: PASS for all instances.
- RetinaNet (bf16, vs classic CK): faster on 28 of 34 shapes, geomean
+19.5%; the
not-divisible-by-8 shapes up to 3.7x. One 1x1 stride-2 shape stays ~20%
behind
classic CK, unrelated to WAVELET.
- Cross-model (200 shapes): WAVELET wins 3x3 not-divisible-by-8 in both
dtypes
(up to 61% over the next-best compute instance); for divisible-by-8 3x3
it wins
about 20% of shapes in fp16 (3-11%) and none in bf16.
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
[CK Tile] Fix Stream-K flag store: wave-uniform SGPR address
for scalar s_store/s_load (#8099)
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## Motivation
Stream-K grouped-conv (and GEMM) kernels fail to assemble for some
instances: the inline scalar flag store/load gets a VGPR address
operand, which scalar-memory instructions reject (`invalid operand for
instruction`). This blocks Stream-K instances from building.
## Technical Details
- `StreamKReductionOps::{Signal,Wait}StorePartialDone` (shared by GEMM
and conv, added in #5393) take `kargs` by `const&` and feed
`kargs.workspace_ptr` / `cta_idx` into inline
`s_store_dword`/`s_load_dword` with `"s"` constraints. For some
instantiations the compiler can't keep the pointer wave-uniform and
emits a VGPR address.
- Fix: route the pointer and offset through `amd_wave_read_first_lane`
so the scalar-memory address is a wave-uniform SGPR before the asm. Same
instructions, no algorithm change.
- Not arch-specific: the affected instance fails on
gfx908/gfx90a/gfx942/gfx950 without the fix; whether the compiler spills
to a VGPR depends on the instantiation (tile/warp/pipeline), not the
target.
## Test Plan
- Compile the previously-failing dispatcher instance for
gfx908/gfx90a/gfx942/gfx950.
- `test_ck_tile_grouped_conv_bwd_weight_streamk` on gfx942, gfx90a,
gfx950 hardware.
- gfx950 perf A/B (example, bf16/tree, 10 runs each) with vs without the
change.
## Test Result
- Failing instance now assembles on all four archs; previously failed on
every one.
- 30/30 conv Stream-K tests pass on gfx942, gfx90a, gfx950.
- gfx950 perf delta -0.13% (within run-to-run noise) — no regression
from the added readfirstlane on the cold flag path.
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
[CK] Grouped Convolution Global Load/Store instances
## Motivation
Support global load and store in grouped convolutions using instance
factory.
## Technical Details
- add new instances for each direction
- add new tests for large cases
## Test Plan
New test for large cases
## Test Result
pending
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
AICK-1255
[Ck][CK Tile] Global Load/Store for Large Tensors support
(#6744)
## Motivation
Create solution to support large tensors in the entire ck tile.
## Technical Details
- add possiblity to use global load
- int64 indexing
## Test Plan
conv fwd tests
## Test Result
passed locally
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
AICK-913
[CK Tile] Extend type support EightWave pipeline
## Motivation
EightWave pipeline was designed for 8 bit types. This PR extend support
for any FP type
## Technical Details
- Generalize policy to support any FP type
- Change LDS layout to fix bank conflicts. This removes all bank
conflicts in the pipeline (checked for all supported types). Remaining
bank conflicts are related to Cshuffle epilogue.
## Test Plan
Added GEMM tests with new supported types. Note that FP6 is also
supported for MX GEMM but the PR was reverted so no tests were added for
it.
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
[tile_engine] Integrate gemm_streamk into budget-based
sampling system (#8079)
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## Motivation
`gemm_streamk` was the only GEMM op not participating in the tile
engine's budget-based sampling system. Without a budget cap, it would
always generate its full feasible set, making build times unpredictable
and inconsistent with the other ops.
## Technical Details
- **CMake budget propagation** (`ops/gemm/CMakeLists.txt`): Added
`gemm_streamk` to the active-ops detection loop so it receives a share
of the sampling budget. Because `gemm_streamk` lives in a sibling
subdirectory (`ops/gemm_streamk/`), its allocation is written via `CACHE
STRING "" FORCE` to make the variable visible across the CMake directory
boundary.
- **Per-combo budget division** (`ops/gemm_streamk/CMakeLists.txt`,
`ops/gemm/grouped_gemm/CMakeLists.txt`): Added the same per-combo
`MAX_INSTANCES` division that exists in `gemm_universal` and
`gemm_preshuffle`. The total budget is divided by `n_datatypes ×
n_layouts` before the inner `foreach` loop so that sampling fires
independently per `(dtype, layout)` combo rather than acting as a single
global cap.
- **Sampling integration** (`gemm_streamk_instance_builder.py`): Added
`_apply_sampling()` method to `GemmKernelBuilder`, mirroring the
Sobol+LHS+maximin sampling used by other ops. New constructor
parameters: `gpu_target`, `max_instances`, `seed`, `tier`,
`manifest_path`. New CLI arguments: `--gpu_target`, `--max-instances`,
`--seed`, `--tier`, `--manifest-path`. The `--gpu_target` argument is
now also forwarded on the `--list_kernels` invocation.
- **`GEMM_STREAMK_AXES`** (`sampling/feasible_set.py`): Defined as
`GEMM_AXES + ["reduction_strategy"]` to account for the extra axis
unique to stream-K. Added `reduction_strategy` to `CATEGORICAL_AXES`.
- **Weight rebalancing** (`sampling/op_weights.json`): Allocated 10%
weight to `gemm_streamk` by proportionally reducing `gemm_universal`
(0.35 → 0.30) and `gemm_preshuffle` (0.30 → 0.25). Total remains 1.00.
## Test Plan
- Configure with `TILE_ENGINE_SAMPLING_TIER=daily` and verify that
`gemm_streamk` receives a non-zero budget allocation and that
`GEMM_STREAMK_MAX_INSTANCES` is set correctly.
- Configure with `TILE_ENGINE_SAMPLING_TIER=daily` across multiple
`(dtype, layout)` combos and confirm per-combo budget = total /
n_combos.
- Configure with `-DGEMM_STREAMK_MAX_INSTANCES=50` explicit override and
verify the override is respected (budget allocation skipped).
- Verify `chosen_instances.json` manifest is written to the working path
when tier is active.
- Confirm `op_weights.json` weights still sum to 1.00.
## Test Result
<!-- Briefly summarize test outcomes. -->
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
[CK] [MIOPEN] Split convolution library by layout
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# Split Composable Kernel convolution operations by data layout
TLDR:
1. This is a reorganization of files, folders, and CMakeLists for
convolution kernels and facilitates a splitting of the convolution
library into layouts.
2. The speedup can range anywhere between 15-40% depending on the target
architecture for miopen only builds of CK. For TheRock nightly builds of
CK, which includes both miopen and hip tensor kernel instances, this
constituted in a 10% decrease in compile time for gfx1100.
## Overview
Based on https://github.com/ROCm/composable_kernel/pull/3010/ (except
keeping 1 static library)
## What MIOpen Actually Uses
MIOpen **exclusively uses:
- **NHWGC** for all 2D convolutions
- **NDHWGC** for all 3D convolutions
This is because MIOpen's tensor descriptors natively use channel-last,
group-aware formats.
## Key Changes
### 1. Layout-Based Directory Structure
Reorganized convolution instance files from flat per-operation to
hierarchical layout-based structure. For example:
**Before:**
grouped_conv2d_fwd/
├── device_grouped_conv2d_fwd_xdl_nhwgc_*.cpp (MIOpen-required)
├── device_grouped_conv2d_fwd_xdl_gnhwc_*.cpp (optional)
└── device_grouped_conv2d_fwd_xdl_ngchw_*.cpp (optional)
**After:**
grouped_conv2d_fwd/
├── nhwgc/ ← MIOpen-required
│ ├── xdl/device_grouped_conv2d_fwd_xdl_*.cpp
│ └── wmma/device_grouped_conv2d_fwd_wmma_*.cpp
├── gnhwc/ ← Optional (excluded with MIOPEN_REQ_LIBS_ONLY)
└── ngchw/ ← Optional (excluded with MIOPEN_REQ_LIBS_ONLY)
### 2. Preserved Umbrella Library
As before, all convolution operations are consolidated into a single
static `device_conv_operations` library:
- Aggregates layout-specific instance object files via
`ADD_CONV_LAYOUT_INSTANCES` macro
- **Default build:** Includes all layouts (NHWGC + GNHWC + NGCHW +
NDHWGC + GNDHWC + NGCDHW)
- **MIOpen build (`MIOPEN_REQ_LIBS_ONLY=ON`):** Includes only NHWGC and
NDHWGC layouts
### 3. Binary Size Reduction
When building with `MIOPEN_REQ_LIBS_ONLY=ON`:
**Layouts Included (26 targets):**
- 7× NHWGC instances (2D operations + variants)
- 19× NDHWGC instances (3D operations + variants)
**Layouts Excluded (16 targets):**
- 3× GNHWC instances (2D operations)
- 3× NGCHW instances (2D operations)
- 3× GNDHWC instances (3D operations)
- 3× NGCDHW instances (3D operations)
- 2× GNWC instances (1D operations)
- 1× NWGC instance (1D operations)
- 1× additional NHWGC instance (grouped_conv1d_fwd, not needed by
MIOpen)
This represents a **~38% reduction in instance targets** (16 excluded
out of 42 total
layout-specific targets).
### Testing
- ✅ All existing CK tests link against the umbrella library
- ✅ MIOpen links successfully with the reduced umbrella library
- ✅ Profiler builds with all layout-specific targets explicitly listed
Notes from the Author:
Since this refactor moved most of the convolution files further into
subdirectories, I concentrated on ensuring that no source files were
excluded, including sharded sources: Targets are correctly migrated — no
missing targets, no shard count mismatches.
=?UTF-8?q?[CK=20TILE]=20Unification=20Work=20=E2=80=93=20?=
=?UTF-8?q?Add=20MFMA=20specialisations=20for=20`tf32=5Ft`=20(#6768)?=
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## Motivation
This PR adds two specialisations related to `tf32_t`.
## Technical Details
This change treats `tf32_t` as a concrete type rather than an empty
`struct`. It also adds two new specialisations for MFMA dense builtins
and resolves existing circular include issues.
## Test Plan
All the new wrappers were added to the test suite in
test_amdgcn_mma_layout.inc.
## Test Result
Test should pass.
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
[CK_TILE] [QuantGEMM] Fix SplitK tail handling and other
improvements (#7199)
This pull request introduces improved and more robust split-K support
for quantized GEMM. The main changes add runtime validation, utility
functions for split-K batch calculations, pointer offset handling for
split-K in grouped kernels, and enhanced support for various tensor
layouts. The changes also improve error handling and provide more
flexibility for runtime tail handling in split-K pipelines.
**Split-K Support and Validation Enhancements:**
* Added runtime validation to ensure `k_batch` is a positive integer and
that split-K configurations do not produce empty final batches or
mismatched pipeline tails, with detailed error messages and logging for
misconfiguration.
[[1]](diffhunk://#diff-d000149a681cd42bfb9947872c603e556cea26cbd7fd4f8f60afc6595d975871R1184-R1211)
[[2]](diffhunk://#diff-d000149a681cd42bfb9947872c603e556cea26cbd7fd4f8f60afc6595d975871L1161-R1250)
* Introduced utility functions `get_splitk_batch_k_read` and
`get_splitk_last_batch_k` to compute per-batch K read sizes and handle
split rounding, ensuring correct and consistent split-K batch
partitioning.
[[1]](diffhunk://#diff-d000149a681cd42bfb9947872c603e556cea26cbd7fd4f8f60afc6595d975871R206-R234)
[[2]](diffhunk://#diff-635b89bdffa96b2b42f1632520cde36701d7d631e864185591f6b32f7645cf47L104-R107)
[[3]](diffhunk://#diff-d000149a681cd42bfb9947872c603e556cea26cbd7fd4f8f60afc6595d975871L388-R417)
[[4]](diffhunk://#diff-d000149a681cd42bfb9947872c603e556cea26cbd7fd4f8f60afc6595d975871L1161-R1250)
* Changed the default value of `k_batch` in `QuantGemmHostArgs` to 1 (no
split-K) for safer default behavior.
**Pointer Offsets and Grouped Kernel Handling:**
* Updated `QuantGroupedGemmKernel` to apply split-K per-batch offsets to
all input pointers, mirroring the behavior of non-grouped kernels and
ensuring correctness for split-K launches.
* Modified AQ tensor view handling to correctly reflect the remaining
K-groups from the split-K batch's offset position, improving accuracy
for split-K in grouped kernels.
**Pipeline and Layout Flexibility:**
* Added support for runtime selection of split-K tail handling via a new
template parameter `RuntimeSplitKTail_`, with new helper methods to
dispatch GEMM pipelines accordingly.
[[1]](diffhunk://#diff-d000149a681cd42bfb9947872c603e556cea26cbd7fd4f8f60afc6595d975871R273)
[[2]](diffhunk://#diff-d000149a681cd42bfb9947872c603e556cea26cbd7fd4f8f60afc6595d975871R1496-R1567)
[[3]](diffhunk://#diff-d000149a681cd42bfb9947872c603e556cea26cbd7fd4f8f60afc6595d975871L1427)
[[4]](diffhunk://#diff-d000149a681cd42bfb9947872c603e556cea26cbd7fd4f8f60afc6595d975871L1447-R1629)
[[5]](diffhunk://#diff-d000149a681cd42bfb9947872c603e556cea26cbd7fd4f8f60afc6595d975871L1459-R1641)
* Improved handling for tensor layout cases, including preshuffled B and
both row-major and column-major AQ layouts, ensuring correct pointer
arithmetic and compatibility checks.
[[1]](diffhunk://#diff-d000149a681cd42bfb9947872c603e556cea26cbd7fd4f8f60afc6595d975871R438-R454)
[[2]](diffhunk://#diff-d000149a681cd42bfb9947872c603e556cea26cbd7fd4f8f60afc6595d975871L464-R516)
[[3]](diffhunk://#diff-d000149a681cd42bfb9947872c603e556cea26cbd7fd4f8f60afc6595d975871R1184-R1211)
[CK Tile] Async support preshuffle GEMM
## Motivation
Add async support to existing preshuffle GEMM pipeline
## Technical Details
Notes:
the implementation avoids previous strategy of duplicating pipelines for
async support and instead add a switch `Async` to the ops Problem to
enable async pipeline. Then, integrate the async pipeline in the
existing one. This allows to avoid code duplication and facilitate the
integration of buffer load to lds in existing pipelines. In my opinion,
it should be used also for other pipelines which don't support buffer
load to lds yet and it would also be a good idea to refactor the
existing async GEMM pipelines with the same approach.
Summary:
- integrate buffer load to lds in existing pipeline
- add optimal tensor descriptors for vmem loading and lds reading. They
are currently optimized for 16x16 wave tiles but they also work for
32x32 wave tiles. Optimizations for 32x32 wave tile requires different
lds layout and it will be done in a follow-up issue
- Add async config to examples
- Add test (gfx950 only)
## Test Plan
New test for gfx950 `test_ck_tile_gemm_pipeline_wp_async`
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
[CK_Tile] Add wmma_bf16f32_16x16x32_bf16 via
fused-downconvert override (#8028)
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## Summary
Adds `__builtin_amdgcn_wmma_bf16f32_16x16x32_bf16` (fp32 accumulate →
bf16 output) to the CK Tile WMMA warp-gemm path. **API only** — the unit
test is split into a stacked PR (#8035) so this API change can be
reviewed in isolation.
## Changes (4 files)
- **16-bit trait:** `wmma_intrinsic_downconvert` (calls the bf16f32
builtin — fp32 C in, bf16 C out) plus `COutDataType = bf16_t` /
`COutVecType`.
- **`WarpGemmAttributeWmmaImpl` / `WarpGemmAttributeWmma`:**
`mac_downconvert(c_fp32, a, b)` (kTransC-aware) returning the bf16
C-output vector.
- **`WarpGemmImpl`:** `mac_downconvert` tail handler producing a bf16
C-output tile from the fp32 accumulator tile, reusing
`CWarpDstrEncoding` (output layout identical to the f32 C tile).
Verified on gfx1250 (via the stacked test PR #8035): the test passes;
the existing WMMA warp-gemm test is unaffected (additive change only).
[CK Tile] PermuteN support MX GEMM
## Motivation
Add PermuteN support to preshuffle MX GEMM
## Technical Details
- Modify `shuffle_b_permuteN` to support MX preshuffled layout
- Add `preShuffleScalePermuteN` with same functionality of
`preShuffleScale` but layout consistent with PermuteN
- Include MX pre-processing functions in the library
## Test Plan
Add test configuration for permuteN with preshuffle (both FP4 and FP8)
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
Co-authored-by: Cong Ma <congma13@amd.com>
[ck] Updated CK Tile documentation to use mermaid diagrams
(#7955)
## Motivation
There were mermaid diagrams in the CK Tile doc that were converted to
svg. However, there is an extension for mermaid diagrams. The conf.py
and requirements.in have been updated to use that extension instead of
the svg files.
[CK] Load ck.groovy via Jenkins Shared Library
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## Motivation
This allows the CI service to have a configuration source-of-truth
outside the PR under test, allowing rapid system changes. Bug fixes on
the develop branch propagate immediately to all pipelines that don't
override the parameter -- no rebase required.
A new `USE_CURRENT_BRANCH_FOR_CK_GROOVY` parameter lets contributors
test pipeline changes on their own branch without any extra
configuration.
## Technical Details
- `loadCk()` in the Jenkinsfile is updated to call
`library("ck@${branch}").ck.get()` instead of `checkout scm` + `load
"vars/ck.groovy"`. The `checkout scm` inside `loadCk()` is removed since
Jenkins now handles the library fetch internally.
- A `USE_CURRENT_BRANCH_FOR_CK_GROOVY` boolean parameter (default: off)
is added. When off, `ck.groovy` is always loaded from `develop` — all
normal PR builds are unaffected. When on, `ck.groovy` is loaded from the
current branch automatically via `env.CHANGE_BRANCH`, so contributors
testing pipeline changes just tick the box.
- `return this` is removed from the end of `ck.groovy`. This was
required by the `load` convention but is not needed (and can cause
errors) in a shared library context.
- `loadCk()` is kept at every call site rather than called once at the
top, preserving restart-from-stage safety — if a build is restarted from
a mid-pipeline stage, `ck` is still initialized correctly.
- The Jenkins Shared Library named `"ck"` must be registered in Jenkins
Global Pipeline Libraries
## Test Plan
1. Trigger "Build with Parameters" on the PR branch with
`USE_CURRENT_BRANCH_FOR_CK_GROOVY=true`
2. Verify "Determine CI Execution" stage completes and the library()
calls indicates the current branch
3. Verify "Static checks" stage completes.
4. Trigger a second build with `USE_CURRENT_BRANCH_FOR_CK_GROOVY=false`
(default) to confirm normal builds still load from `develop`.
## Test Result
Verified both paths. The develop library is loaded by default, the
branch library is loaded when the parameter is enabled.
## Submission Checklist
- [ X ] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
[CK] Fix latest build issues with staging compiler.
## Motivation
Fixing new warnings with staging compiler.
## Technical Details
<!-- Explain the changes along with any relevant GitHub links. -->
## Test Plan
<!-- Explain any relevant testing done to verify this PR. -->
## Test Result
<!-- Briefly summarize test outcomes. -->
## Submission Checklist
- [ ] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
[ck] Enforce ASCII-only C/C++ sources for hipRTC
compatibility (#7829)
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## Summary
CK source files must be compilable via **hipRTC (HIP runtime
compilation)**, whose preprocessor does not accept non-ASCII bytes
anywhere in a translation unit — **including in comments**. Bytes that
are harmless under `hipcc` (em-dashes, smart quotes, multiplication
signs, Greek letters, box-drawing glyphs, etc.) cause hipRTC to fail at
preprocessing time. These regularly leak in via LLM-assisted authoring
or copy/paste from formatted documents and silently break hipRTC paths
that are not exercised by the default `hipcc`-based build matrix.
This PR (a) cleans every existing violation (53 files) and (b) adds a
pre-checkin gate so new violations are rejected before merge.
## File extensions covered
Both the cleanup scan and the new Jenkins enforcement stage use the same
predicate:
```
*.h *.hpp *.cpp *.h.in *.hpp.in *.cpp.in *.inc *.cl
```
(excluding `*/build/*` and `*/include/rapidjson/*`). This is a strict
superset of the existing `Clang Format` stage's predicate — `*.inc` is
added so test-fixture include files are also gated. The local pre-commit
hook's `c++/inc` type filter covers the same set.
## Why no enforcement today
CK is opted out of the rocm-libraries root `.pre-commit-config.yaml`, so
the existing `pre-commit` workflow doesn't touch CK. The local CK
`.pre-commit-config.yaml` only runs for developers who installed hooks.
The **authoritative gate is therefore the new Jenkins stage** in this
PR; the local hook is convenience.
## Commit layout (bisect-friendly)
1. `79798aa6261` — **`[ck] Convert reflect/ rendering to ASCII for
hipRTC compatibility`**
Behavior change, isolated. `TreeFormatter` swaps `├─ / └─ / │ ` for `|-
/ +- / | ` (3-col width preserved so alignment is unchanged).
`conv_description.hpp` swaps `×` for `x` as the dimension separator.
`test_conv_description.cpp` expected strings updated in lockstep so the
snapshot test stays green. This is the only commit in the series with
observable runtime impact.
2. `738fdb0d81c` — **`[ck] Strip non-ASCII bytes from C++ sources for
hipRTC compatibility`**
Mechanical text cleanup across 53 files. Replacements happen in comments
or in `std::cout` strings that are not asserted on by any test. None of
the 174 `.inc` files in the tree required edits, but they were in the
scan's predicate so the enforcement stage's predicate is a superset of
what was scanned. Full replacement table in the commit message.
3. `1d7cd8ba235` — **`[ck] Enforce ASCII-only C/C++ sources for hipRTC
compatibility`**
- New `projects/composablekernel/script/check_ascii_only.sh` (modeled on
`check_copyright_year.sh`).
- New entry in `projects/composablekernel/.pre-commit-config.yaml` under
the local-hooks block (`types_or: [c++, inc]`).
- New `ASCII Only Check` parallel stage in
`projects/composablekernel/Jenkinsfile`'s `Static checks` block,
mirroring the existing `Clang Format` stage but with `*.inc` added to
the find predicate. Always-on, no `RUN_CPPCHECK` gate.
The tree is buildable at every commit boundary. Commit 1 leaves 50 known
violations; commit 2 leaves 0; commit 3 wires the gate.
## Demo
Script output on a synthesized violation:
```
$ printf '// em-dash test \xe2\x80\x94 here\n' > /tmp/bad.cpp
$ projects/composablekernel/script/check_ascii_only.sh /tmp/bad.cpp
ERROR: /tmp/bad.cpp contains non-ASCII bytes:
1:// em-dash test — here
Fix: replace with ASCII (em-dash -> --, smart quotes -> ", arrows -> ->, etc.)
$ echo $?
1
```
Full repo scan after the cleanup commits (note the `-name '*.inc'`
clause):
```
$ cd projects/composablekernel && find . -type f \( -name '*.h' -o -name '*.hpp' -o -name '*.cpp' \
-o -name '*.h.in' -o -name '*.hpp.in' -o -name '*.cpp.in' -o -name '*.inc' -o -name '*.cl' \) \
-not -path '*/build/*' -not -path '*/include/rapidjson/*' -print0 \
| xargs -0 -P 8 -n 64 script/check_ascii_only.sh
$ echo $?
0
```
## Test plan
- [ ] Jenkins PR build: confirm new `Static checks -> ASCII Only Check`
stage runs green over the full predicate (incl. `*.inc`) and existing
`Clang Format` stage is unaffected.
- [ ] `test_conv_description` passes against the ASCII tree-formatter
output (touched in commit 1).
- [ ] Local: `pre-commit run ascii-only-checker --all-files` runs
cleanly after installing CK pre-commit hooks via
`script/install_precommit.sh`.
- [ ] Manually inject a non-ASCII byte in any `.cpp/.hpp/.inc` file,
push: confirm Jenkins fails the new stage with a clear error.
- [ ] Spot-check a representative subset of touched files under hipRTC
compilation to confirm no remaining hipRTC-blocking content (optional,
since the static byte check is a sufficient condition for hipRTC
preprocessor acceptance on this dimension).
🤖 Generated with [Claude Code](https://claude.com/claude-code)
composablekernel: remove stray *.hpp.bk backup artifacts
(#7974)
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Four `*.hpp.bk` files were accidentally committed to
`projects/composablekernel/`, likely as leftovers from a prior merge or
conflict resolution. Each is an older snapshot of its `.hpp` counterpart
— the canonical `.hpp` files are newer and contain the correct current
content.
## Deleted files
| File | vs. `.hpp` counterpart |
|---|---|
| `ck_tile/core/tensor/tile_window.hpp.bk` | Older version: uses legacy
`bool isL1Cache`/`PrefetchL1` template params; missing
`DataCachePrefetchKind`-based prefetch API and `data_cache_prefetch.hpp`
include |
| `ck_tile/core/tensor/load_tile_transpose.hpp.bk` | Older version:
missing `#if defined(__gfx950__)` guard and `Quad` struct (~90 lines)
for gfx1250 architecture |
| `ck_tile/ops/gemm/warp/warp_gemm_dispatcher.hpp.bk` | Older version:
missing `WmmaTag`, `IsScale16` template param, and several newer
dispatcher specializations |
|
`ck_tile/ops/gemm_quant/block/block_universal_gemm_as_bs_bquant_cr.hpp.bk`
| Older version: `KPackA`/`KPackB` (since renamed `KPack`); uses
`static_ford` (since refactored to nested `static_for`) |
## Verification
- No other `.bk` files exist in `projects/composablekernel/`.
- No build scripts, CMake files, includes, or documentation reference
these `.bk` files.
- No `.hpp` files were modified.
[CK TILE][Windows] add `msvc::no_unique_address` support for
Windows (#7786)
## Motivation
While building Flash Attention 2 with CK backend, this warning will spam
in every kernel:
```
DEBUG [1/1837] hipcc.exe ...
DEBUG In file included from H:\ROCm\flash-attention\build\fmha_fwd_d32_bf16_batch_b64x64x16x32x32x32_r4x1x1_r4x1x1_w16x16x16_w16x16x16_qr_vr_pssk_nlogits_alibi_mask_lse_ndropout_nskip_nqscale_ntrload_nsink_gfx12.cu:6:
DEBUG In file included from H:\ROCm\flash-attention\csrc\composable_kernel\example\ck_tile\01_fmha\fmha_fwd.hpp:6:
DEBUG In file included from H:\ROCm\flash-attention\csrc\composable_kernel\include\ck_tile/core.hpp:111:
DEBUG H:\ROCm\flash-attention\csrc\composable_kernel\include\ck_tile/core/tensor/tile_scatter_gather.hpp:1246:7: warning: unknown attribute 'no_unique_address' ignored [-Wunknown-attributes]
DEBUG 1246 | [[no_unique_address]] std::conditional_t<kUseGlobalLoad_, PageIdxArray, gl_field_empty_t>
DEBUG | ^~~~~~~~~~~~~~~~~
DEBUG H:\ROCm\flash-attention\csrc\composable_kernel\include\ck_tile/core/tensor/tile_scatter_gather.hpp:1254:7: warning: unknown attribute 'no_unique_address' ignored [-Wunknown-attributes]
DEBUG 1254 | [[no_unique_address]] std::conditional_t<kUseGlobalLoad_, index_t, gl_field_empty_t>
DEBUG | ^~~~~~~~~~~~~~~~~
DEBUG 2 warnings generated when compiling for host.
...
```
## Technical Details
`[[no_unique_address]]` is not working on Windows LLVM, should use
`[[msvc::no_unique_address]]`.
## Test Plan
Build FA2 with CK backend.
## Test Result
No warnings, no errors.
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
[GFX1250][CK_TILE] Add scale16 warp gemm unit tests
## Summary
- Add scale16 WMMA intrinsic overloads and int64_t forwarding to warp
gemm layers for gfx1250
- Add comprehensive wave-level unit tests for scale16 warp gemm
(16x16x128 and 32x32x128 tile sizes)
- Test all fp8/bf8 type combinations and TransposeC variants
- Fix WarpGemm wrapper for non-uniform scale16 configurations
Stacked on #7724 (FillUniformScaleDistribution / MX GEMM scale init).
Pipeline enablement follows in the next PR.
[CK] Grouped conv profiler updates
## Motivation
Reduce profiling time for no verification.
## Technical Details
Remove not needed code for no verification
## Test Plan
test_grouped_convnd*
## Test Result
pending
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
AICK-1230
=?UTF-8?q?[CK=20TILE]=20Unification=20Work=20=E2=80=93=20?=
=?UTF-8?q?More=20accurate=20tests=20for=20MmaPipelines=20(#6212)?=
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## Motivation
This PR solves several issues:
#### More accurate tests for MmaPipelines
The current tests for the MmaPipelines (test_amdgcn_sparse_mma,
test_amdgcn_wavewise_mma) use explicit input fragment vectors filled
with 1s, and only check the output of a single lane. We should have
tests that actually use the MmaPipelines with non-trivial input matrices
and verify the complete output.
Some other aspects of the current MmaPipelines tests that I noticed and
deserve some attention:
1. There is sometimes iteration over K outside of the pipeline, which is
then included in WaveTileK or FragK, which is not correct. We should
remove it, move K iteration inside of the pipeline, or be more clear
about this outer-K loop size and how it propagates downwards.
2. There is very tight coupling between the kernel, gtest code, and
test_pipeline helper, requiring a lot of information and functions to be
passed back and forth.
3. The test_pipeline helper is doing a bunch of register-related logic
on the host (related to point 1)
4. Without this register logic the only thing it does is check the
device, call the kernel, and check the output, but with a lot of
boilerplate.
#### Test helper for detecting target arch at HOST runtime
There is a really apparent issue we faced while writing tests:
Scenario:
1. Compile a test that supports both gfx950 and gfx1201 for gfx950
2. Run the test on a server that only has gfx1201 GPU
Actual:
Segmentation fault
Expected:
The test can correctly detect from HOST runtime that the DEVICE
target_id was different and skips the test.
Notes:
The only way of detecting the COMPILER_TARGET_ID in the existing "arch"
framework is launching a kernel and calling `get_compiler_target()` (so,
from a DEVICE code). This will create a segmentation fault if the
current arch differs from the target arch. To cope with this issue, we
propose to export the compiler target(s) (note they can be many) through
`projects/composablekernel/test/ck_tile/core/arch/CMakeLists.txt` and
define a test helper to deal with such cases.
#### Add composition support to Transforms
We have a small number of Transforms which act on MmaOp input and output
data, before and after the MmaOp call respectively. These are currently
implemented to work on an MmaTile level, but in theory they are also
supposed to work at a WaveTile level, i.e. after composition of multiple
MmaTiles to create larger effective MNK dimensions. Currently the
composed MmaTiles look like 2D C-style arrays of the individual MmaTile
level register vectors (see WaveWiseMmaPipeline). The transforms should
be able to take these and perform the proper transforms to the whole
WaveTile at once. This might allow for better performing
transformations.
Note: This PR handles the SparseTransform case and if we don't end up
doing scale as a transformation, there isn't really much left to do. If
we end up having only the sparse transform as a non-trivial transform,
then we could also consider removing the Transform framework.
[CK Tile] Fix V6 pipeline applicability and split-image
initialization (#7936)
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## Motivation
After adding code generation via CK Tile Dispatcher, some fwd and bwd
weight tests for CK Tile convolutions are failing. This PR introduced
correct applicability checks and fixes the split-image parameter
initialization such that non-applicable instances are not invoked during
test execution and split-image instances are correctly initialized.
## Technical Details
Investigation revealed two distinct problems
1. For bwd weight, the compute V3 uses prefetch of 3 distinct tiles,
which works incorrectly when the number of K-slices addressed by the
workgroup is 1. This occurs when a large split-K value is used for a
problem that results in a small Gemm-K value.
2. For fwd direction, the current CK Profiler/test infrastructure
doesn't initialize the split-image parameters for instance where
split-image is enable. Uninitialized split-image values result in
non-deterministic behavior where the tests might randomly fail.
Fixed problem 1. by adding a check in `IsSupportedArgument` that marks
the instance invalid if the `num_loops = ceil(GemmK / (k_batch *
KPerBlock)) < 4` for V6 pipeline kernel instances. The check is
compile-time eliminated for other kernels.
Fixed problem 2. by adding initialization of split-image parameters when
split-image is enabled. The default initialization corresponds to full
image with no split, i.e., the number of splits is 1 and it has the size
of the full image.
Added unit tests for the added logic.
## Test Plan
Running the following test suites cover the logic added in this PR
- test_grouped_convnd_fwd_tile
- test_ck_tile_grouped_conv_fwd
- test_grouped_convnd_bwd_weight_tile
- test_ck_tile_grouped_conv_bwd_weight
All test suites above are included in the automated test runs.
## Test Result
<!-- Briefly summarize test outcomes. -->
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
[CK_TILE][FMHA] Optimize long-context decoding on gfx11/12
(#7500)
## Motivation
Relevant issue: ROCM-22065
FMHA has less-than-optimal performance of long-context decoding (i.e.
when seqlen_q = 1) on gfx11/12.
This PR optimizes the splitkv pipeline and configs for such scenarios.
## Technical Details
Optimizations applied in this PR:
1. use tiles with smaller M0 (16 vs 64), these tiles are used when
seqlen_q <= 16
2. adapt qr_nwarp_sshuffle pipeline for gfx11, it allows to use more
warps even for M0 = 16 (the qr pipeline parallelizes work between warps
in M dim so with M0 = 16 it allows to use only 1 warp)
3. enable kMergeNumHeadGroupsSeqLenQ (an optimization that merges one
group of heads in GQA) for all hdim values, not only 128
4. increase the number of splits (multiply by the number of head groups)
if (3) is used
5. increase the number of splits for RDNAs (`multiProcessorCount` is the
number of WGPs on RDNAs, not CUs, so it should be doubled to have
meaning similar to CDNAs)
Performance on gfx1151:
| Case | develop (GB/s) | This PR (GB/s) |
|:-------|-------:|-------:|
| [fp16\|group\|bshd] b:1, h:32/32, s:1/45056, d:64/64 | 127.58 | 183.11
|
| [fp16\|group\|bhsd] b:1, h:32/32, s:1/45056, d:64/64 | 153.64 | 215.02
|
| [fp16\|group\|bshd] b:1, h:16/8, s:1/77184, d:128/128 | 120.51 |
225.76 |
| [fp16\|group\|bhsd] b:1, h:16/8, s:1/77184, d:128/128 | 130.62 |
223.84 |
| [fp16\|group\|bshd] b:1, h:32/32, s:1/9600, d:128/128 | 82.65 | 138.44
|
| [fp16\|group\|bhsd] b:1, h:32/32, s:1/9600, d:128/128 | 105.75 |
220.45 |
| [fp16\|group\|bshd] b:1, h:8/1, s:1/401024, d:256/256 | 16.27 | 187.89
|
| [fp16\|group\|bhsd] b:1, h:8/1, s:1/401024, d:256/256 | 16.28 | 188.19
|
## Test Plan
An additional test case is added to the exiting test. It uses seqlen_q =
1, GQA, no mask to trigger the changes
```
ninja test_ck_tile_fmha_fwd_fp16 && bin/test_ck_tile_fmha_fwd_fp16 --gtest_filter="*SplitKV*
ninja test_ck_tile_fmha_fwd_bf16 && bin/test_ck_tile_fmha_fwd_bf16 --gtest_filter="*SplitKV*
```
Manual testing can be done with these commands:
```
bin/tile_example_fmha_fwd -prec=fp16 -mode=1 -page_block_size=128 -b=1 -h=32 -h_k=32 -d=64 -s=1 -s_k=$((352 * 128)) -lse=1 -mask=0 -num_splits=0 -kname=1 -v=1
bin/tile_example_fmha_fwd -prec=fp16 -mode=1 -page_block_size=128 -b=1 -h=16 -h_k=8 -d=128 -s=1 -s_k=$((603 * 128)) -lse=1 -mask=0 -num_splits=0 -kname=1 -v=1
bin/tile_example_fmha_fwd -prec=fp16 -mode=1 -page_block_size=128 -b=1 -h=32 -h_k=32 -d=128 -s=1 -s_k=$((75 * 128)) -lse=1 -mask=0 -num_splits=0 -kname=1 -v=1
bin/tile_example_fmha_fwd -prec=fp16 -mode=1 -page_block_size=128 -b=1 -h=8 -h_k=1 -d=256 -s=1 -s_k=$((3133 * 128)) -lse=1 -mask=0 -num_splits=0 -kname=1 -v=1
```
## Test Result
All the tests must pass.
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
[CK] Fix gfx950 AITER Sync Regressions
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## 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.
[CK] Upgrade to new gfx1250 compiler and fix build issues
(#7960)
## Motivation
The docker image we've been using to build for gfx1250 is a few months
old, so we need to upgrade. Some of the changes in the latest compiler
version require changes in the code. TDM is temporarily disabled due to
changes in the lds load/store intrinsics.
## Technical Details
<!-- Explain the changes along with any relevant GitHub links. -->
## Test Plan
<!-- Explain any relevant testing done to verify this PR. -->
## Test Result
<!-- Briefly summarize test outcomes. -->
## Submission Checklist
- [ ] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
Replace nested static_for lambdas with compile-time search
helper (#6696)
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## Summary
- Add `sequence_find_value` and `find_in_tuple_of_sequences`
compile-time search helpers with O(1) template depth
- Replace nested `static_for` lambdas in
`TensorDescriptor::GetTransformAndItsUpperDimension` and
`InitializeElementSize`
- Apply same optimizations to `TensorAdaptor`
Supersedes #4287. Conflict-resolved rebase of
ROCm/composable_kernel#3600 onto current develop.
## Motivation
The `TensorDescriptor` and `TensorAdaptor` classes had excessive
template instantiation from:
1. Nested `static_for` loops with lambdas creating unique closure types
at every call site
2. `generate_tuple` with lambdas causing per-type instantiation overhead
The new helpers use constexpr array lookup and pack expansion instead of
recursive template patterns, achieving O(1) template depth.
## Results (`example_grouped_conv_fwd_xdl_fp16`, n=10, interleaved,
`-j1`, `-ftime-trace`)
| TU | Baseline (mean) | New (mean) | Delta | Wilcoxon p | Mann-Whitney
p |
|----|-----------------|------------|-------|-----------|---------------|
| `grouped_conv_fwd_xdl_fp16` (host) | 14,886 ms | 13,353 ms |
**-10.3%** | **0.002** | **0.0002** |
| `grouped_conv_fwd_xdl_fp16` (device) | 27,762 ms | 25,629 ms |
**-7.7%** | **0.002** | **0.0002** |
| **Total (all TUs)** | **57,732 ms** | **54,030 ms** | **-6.4%** | | |
Unrelated TUs (`device_memory`, `host_tensor`, `convolution_parameter`)
show no significant difference (p > 0.3), serving as negative controls.
### Methodology
- 10 interleaved runs (baseline₁, new₁, baseline₂, new₂, ...) on the
same node to eliminate ordering/warmup bias
- Wilcoxon signed-rank test (paired, non-parametric) and Mann-Whitney U
test (unpaired)
- Built with patched clang (LLVM 22) on ctr2-alola-compile-11, `-j1` for
accurate per-TU timing
- Raw data available in Slurm job 275230 results
## Test plan
- [x] 11 unit tests added (5 for `sequence_find_value`, 6 for
`find_in_tuple_of_sequences`)
- [x] Compile-time benchmark with statistical significance (p < 0.01)
- [ ] Full CI
Tracking issue: #4229
[CK_Tile][MI450] Add bf16 output wmma instruction (16x16x32)
(#7830)
Wire __builtin_amdgcn_wmma_bf16_16x16x32_bf16 into CK Tile for gfx1250,
enabling bf16-input bf16-output WMMA at the warp GEMM level.
- Add WmmaTraits specialization for <gfx125_t, bf16, bf16, bf16,
16,16,32>
- Add WarpGemmAttributeWmmaImpl typedef and WarpGemmWmma alias
- Add Dispatcher entry for bf16->bf16 16x16x32
- Add warp_gemm test with reference GEMM validation
## Motivation
<!-- Explain the purpose of this PR and the goals it aims to achieve.
-->
## Technical Details
<!-- Explain the changes along with any relevant GitHub links. -->
## Test Plan
<!-- Explain any relevant testing done to verify this PR. -->
## Test Result
<!-- Briefly summarize test outcomes. -->
## Submission Checklist
- [ ] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
[CK] Allow skipping split-K C-buffer zero-init in
xdl_cshuffle blockscale GEMM (#7935)
Add a `skip_zero_init` flag (default false) to the Problem/Argument of
the xdl_cshuffle block-scale GEMM device ops (multiple_d ab_scale and
blockscale b-preshuffle). When the flag is set, the device invoker skips
the internal hipMemsetAsync that zeroes p_c_grid before the KBatch > 1
split-K atomic-accumulation path. The flag is declared on the gridwise
Problem struct (inherited by Argument), so it is visible on both the
rotating-cache (arg_) and the normal (arg) launch paths in each device
op.
Why: callers that already pre-zero the output buffer otherwise pay for a
redundant device-wide memset before split-K atomic accumulation. Gating
the memset behind an opt-in flag lets such callers avoid the duplicate
work. Because the flag defaults to false, every existing call site is
unaffected and the observable behavior is unchanged.
## Motivation
<!-- Explain the purpose of this PR and the goals it aims to achieve.
-->
## Technical Details
<!-- Explain the changes along with any relevant GitHub links. -->
## Test Plan
<!-- Explain any relevant testing done to verify this PR. -->
## Test Result
<!-- Briefly summarize test outcomes. -->
## Submission Checklist
- [ ] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
Co-authored-by: Cursor <cursoragent@cursor.com>
[CK Tile] Add conv Wavelet GEMM pipeline and bwd_weight
instances (#7937)
## Motivation
CK Tile had no pipeline competitive with old CK's wavelet on the
RetinaNet K=36 C=256 3x3 conv bwd_weight class. This adds a
wave-specialized "wavelet" GEMM pipeline so CK Tile has a competitive
kernel for spatial small-K shapes.
## Technical Details
- New wavelet GEMM pipeline (`gemm_pipeline_ag_bg_cr_wavelet.hpp`):
workgroup split into math waves (LDS read + MFMA) and load waves (DRAM
read + LDS write).
- VGPR role-split: `operator()` has two top-level mutually-exclusive
`is_math` branches so the allocator overlays both roles onto the same
physical VGPRs, cutting arch VGPR ~33-40% and raising occupancy.
Correctness depends on identical `block_sync_lds` counts on both arms
plus a matching load-wave barrier stub in the epilogue
(`cshuffle_epilogue.hpp`).
- Kernel dispatch (`grouped_convolution_backward_weight_kernel.hpp`):
`kIsWavelet` path, `LaunchBlockSize`, load-wave barrier stub.
Uplift: wavelet is the fastest CK Tile pipeline on the RetinaNet K=36
C=256 3x3 family, beating the best non-wavelet CK Tile kernel by 10-27%
(googlenet K=320 by 16-23%); the role-split roughly halves the parity
gap vs old CK on the 13x13 fp16 shape.
## Test Plan
- `ckProfiler grouped_conv_bwd_weight`, NHWGC layout, fp16/bf16,
`split_k=all`, CPU verify on RetinaNet K=36 shapes (7x7, 13x13) and a
broad 2D sweep.
- Correctness: `-v=1` across `split_k` in {-1,1,2,4,8,16,32,64}
(barrier-parity / deadlock check).
- `test_grouped_convnd_bwd_weight` over the tests `.conf` wavelet
instances.
## Test Result
- All wavelet instances CPU-verify correct across the split-K sweep; no
hangs (dual-arm barrier sequence matches).
- Wavelet wins the RetinaNet K=36 C=256 3x3 family (10-27% over best
non-wavelet CK Tile) and googlenet K=320 (16-23%); at parity-or-better
vs old CK on the majority of spatial shapes.
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
