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Po Yen Chen c2ac7aa7b0 [rocm-libraries] ROCm/rocm-libraries#6051 (commit f0838b2) 
[CK] Add FP8 per-tensor quantization support for FMHA V3
 pipeline (#6051)

## Motivation

The existing FMHA V3 pipeline only supports fp16/bf16 data types. This
PR extends V3 to handle FP8 inputs with per-tensor descaling on gfx950,
enabling higher throughput for
  FP8 inference workloads using the assembly-optimized V3 code path.

  ## Technical Details

  **Warp GEMM:**
- Add FP8 32x32x32 warp gemm with C-transposed distribution
(`WarpGemmMfma_f32_32x32x32_fp8_fp8_CTransposed`) and dispatcher entries

  **V3 Kernel (`fmha_fwd_v3_kernel.hpp`):**
- Add per-tensor descale support for Q, K, V tensors, passing descale
pointers through to pipeline kargs

  **V3 Pipeline (`block_fmha_fwd_v3_pipeline.hpp`):**
  - Add FP8 data path with dtype-aware type selection
  - Add asm volatile P matrix conversion from f32 to fp8
  - Add FP8-aware instruction scheduling in `CoreLoopScheduler`

**V3 Pipeline Policy
(`block_fmha_fwd_v3_pipeline_default_policy.hpp`):**
- Add FP8 QK warp gemm selection (SwizzleB variant for V tile
distribution compatibility)

  **Codegen (`fmha_fwd.py`):**
  - Add gfx950 FP8BF16 V3 tile size (256x64x128x128x64x128)
- Add FP8BF16 V3 pipeline variants (mask: no/causal, qscale:
no/pertensor)
  - Extend `can_dispatch_v3` condition for fp8bf16 + pertensor

  **Misc:**
- Add LLVM scheduler `TRANS` mask to `LLVMSchedGroupMask` enum
(`arch.hpp`)
- Fix `mask_info` default initialization for `no_mask` case (`mask.hpp`)

V3 dispatch for FP8 is disabled by default (`F_is_v3_enabled=false`)
pending further validation.

## Performance: fmha_fwd V3 FP8 (avg runs 2-6, stock ROCm 7.1.1, gfx950)

  | Problem | Regular (TFlops) | Varlen (TFlops) |
  |---|---:|---:|
  | batch=1 heads=6/1 seqlen=1024 causal | 48.9 | 47.6 |
  | batch=1 heads=6/1 seqlen=2048 causal | 119.8 | 117.4 |
  | batch=1 heads=6/1 seqlen=4096 causal | 263.7 | 259.2 |
  | batch=1 heads=6/1 seqlen=8192 causal | 548.9 | 543.6 |
  | batch=1 heads=6/1 seqlen=16384 causal | 1043.0 | 1063.7 |
  | batch=1 heads=6/1 seqlen=32768 causal | 1237.2 | 1279.6 |
  | batch=1 heads=6/1 seqlen=65536 causal | 1315.4 | 1382.7 |
  | batch=1 heads=6/1 seqlen=131072 causal | 1326.3 | 1402.2 |
  | batch=1 heads=16/1 seqlen=65536 causal | 1298.7 | 1388.4 |
  | batch=1 heads=40/40 seqlen=37200 non-causal | 1248.9 | 1326.1 |

## Test Plan

Tested with aiter's `test_mha_fp8.py` test suite (176 cases) covering
batch sizes (1-2), sequence lengths (113-4096), head counts (5/8/32/40),
GQA ratios (1:1, 1:8), and
causal/non-causal modes. Verified all cases dispatch to the V3 pipeline
by enabling `F_is_v3_enabled` and confirming kernel names contain
`qr_async_trload_v3`.

  ## Test Result

176/176 tests passed with V3 enabled. All cases correctly dispatched to
V3 pipeline with `pertensor` quantization.

  ## Submission Checklist

- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
2026-04-07 14:20:43 +00:00

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Changelog for Composable Kernel

Documentation for Composable Kernel available at https://rocm.docs.amd.com/projects/composable_kernel/en/latest/.

(Unreleased) Composable Kernel 1.3.0

Added

  • Added overload of load_tile_transpose that takes reference to output tensor as output parameter
  • Use data type from LDS tensor view when determining tile distribution for transpose in the GEMM pipeline
  • Added eightwarps support for abquant mode in blockscale GEMM.
  • Added preshuffleB support for abquant mode in blockscale GEMM.
  • Added support for explicit GEMM in CK_TILE grouped convolution forward and backward weight.
  • Added TF32 convolution support on gfx942 and gfx950 in CK. It could be enabled/disabled via DTYPES of "tf32".
  • Added streamingllm sink support for FMHA FWD, include qr_ks_vs, qr_async and splitkv pipelines.
  • Added support for microscaling (MX) FP8/FP4 mixed data types to Flatmm pipeline.
  • Added support for fp8 dynamic tensor-wise quantization of fp8 fmha fwd kernel.
  • Added FP8 KV cache support for FMHA batch prefill.
  • Added support for gfx1153 target.
  • Added FMHA batch prefill kernel support for several KV cache layouts, flexible page sizes, and different lookup table configurations.
  • Added gpt-oss sink support for FMHA FWD, include qr_ks_vs, qr_async, qr_async_trload and splitkv pipelines.
  • Added persistent async input scheduler for CK Tile universal GEMM kernels to support asynchronous input streaming.
  • Added FP8 block scale quantization for FMHA forward kernel.
  • Added gfx11 support for FMHA.
  • Added microscaling (MX) FP8/FP4 support on gfx950 for FMHA forward kernel ("qr" pipeline only).
  • Added FP8 per-tensor quantization support for FMHA forward V3 pipeline on gfx950.

Changed

Upcoming changes

Composable Kernel 1.2.0 for ROCm 7.2.0

Added

  • Added tests for f8 x bf8 on CompV3, and f8 x bf8 with K_BlockSize 32 on CompV4
  • Added CK-Tile dispatcher - a unified kernel dispatch, code generation and architecture-based kernel filtering system with with C++ and Python frontends starting with GEMM support.
  • Added support for bf16 data type to grouped_gemm and grouped_gemm_preshuffle.
  • Added Col-Col-Row-Col layout support for aquant mode in blockscale GEMM.
  • Added support for mixed precision fp8 x bf8 universal GEMM and weight preshuffle GEMM.
  • Added a compute async pipeline in the CK Tile universal GEMM on gfx950.
  • Added support for B Tensor type pk_int4_t in the CK Tile weight preshuffle GEMM.
  • Added the new api to load different memory sizes to SGPR.
  • Added support for B Tensor preshuffle in CK Tile grouped GEMM.
  • Added a basic copy kernel example and supporting documentation for new CK Tile developers.
  • Added support for grouped GEMM kernels to perform Multi D elementwise operation.
  • Added support for multiple ABD GEMM.
  • Added benchmarking support for tile engine GEMM Multi D.
  • Added block scaling support in CK Tile GEMM, allowing flexible use of quantization matrices from either A or B operands.
  • Added the row-wise column-wise quantization for CK Tile GEMM and CK Tile grouped GEMM.
  • Added support for f32 to FMHA (forward and backward).
  • Added tensor-wise quantization for CK Tile GEMM.
  • Added support for batched contraction kernel.
  • Added WMMA (gfx12) support for FMHA.
  • Added pooling kernel in CK_TILE
  • Added top-k sigmoid kernel in CK_TILE
  • Added the blockscale 2D support for CK_TILE GEMM.
  • Added Flatmm pipeline for microscaling (MX) FP8/FP4 data types
  • Added reduce and multi reduction kernels

Changed

  • Removed BlockSize in make_kernel and CShuffleEpilogueProblem to support Wave32 in CK Tile (#2594)
  • Added an optional template parameter Arch (gfx9_t, gfx12_t etc.) to make_kernel to support linking multiple object files that have the same kernel compiled for different architectures.
  • FMHA examples and tests can be built for multiple architectures (gfx9, gfx950, gfx12) at the same time.

Upcoming changes

  • Composable Kernel will be adopting C++20 features in an upcoming ROCm release, updating the minimum compiler requirement to C++20. Ensure that your development environment complies with this requirement to facilitate a seamless transition.

Composable Kernel 1.1.0 for ROCm 7.1.1

Upcoming changes

  • Composable Kernel will be adopting C++20 features in an upcoming ROCm release, updating the minimum compiler requirement to C++20. Ensure that your development environment complies with this requirement to facilitate a seamless transition.

Composable Kernel 1.1.0 for ROCm 7.1.0

Added

  • Added support for hdim as a multiple of 32 for FMHA (fwd/fwd_splitkv/bwd)
  • Added support for elementwise kernel.

Upcoming changes

  • Non-grouped convolutions are deprecated. Their functionality is supported by grouped convolution.

Composable Kernel 1.1.0 for ROCm 7.0.0

Added

  • Added support for bf16, f32, and f16 for 2D and 3D NGCHW grouped convolution backward data
  • Added a fully asynchronous HOST (CPU) arguments copy flow for CK grouped GEMM kernels.
  • Added support GKCYX layout for grouped convolution forward (NGCHW/GKCYX/NGKHW, number of instances in instance factory for NGCHW/GKYXC/NGKHW has been reduced).
  • Added support for GKCYX layout for grouped convolution forward (NGCHW/GKCYX/NGKHW).
  • Added support for GKCYX layout for grouped convolution backward weight (NGCHW/GKCYX/NGKHW).
  • Added support for GKCYX layout for grouped convolution backward data (NGCHW/GKCYX/NGKHW).
  • Added support for Stream-K version of mixed fp8/bf16 GEMM
  • Added support for Multiple D GEMM
  • Added GEMM pipeline for microscaling (MX) FP8/FP6/FP4 data types
  • Added support for FP16 2:4 structured sparsity to universal GEMM.
  • Added support for Split K for grouped convolution backward data.
  • Added logit soft-capping support for fMHA forward kernels.
  • Added support for hdim as a multiple of 32 for FMHA (fwd/fwd_splitkv)
  • Added benchmarking support for tile engine GEMM.
  • Added Ping-pong scheduler support for GEMM operation along the K dimension.
  • Added rotating buffer feature for CK_Tile GEMM.
  • Added int8 support for CK_TILE GEMM.
  • Added CK Tile Epilogue Chainer framework for composable epilogue sequences in GEMM operations

Optimized

  • Optimize the gemm multiply multiply preshuffle & lds bypass with Pack of KGroup and better instruction layout.
  • Added Vectorize Transpose optimization for CK Tile
  • Added the asynchronous copy for gfx950

Changed

  • Removed support for gfx940 and gfx941 targets (#1944)
  • Replaced the raw buffer load/store intrinsics with Clang20 built-ins (#1876)
  • DL and DPP kernels are now enabled by default.
  • Number of instances in instance factory for grouped convolution forward NGCHW/GKYXC/NGKHW has been reduced.
  • Number of instances in instance factory for grouped convolution backward weight NGCHW/GKYXC/NGKHW has been reduced.
  • Number of instances in instance factory for grouped convolution backward data NGCHW/GKYXC/NGKHW has been reduced.

Composable Kernel 1.1.0 for ROCm 6.1.0

Additions

  • Added generic instances for GEMM XDL operations (#1161)
  • Added gamma and beta parameters for the layernorm and groupnorm bwd operations (#1133)
  • Introduced wrapper sublibrary (limited functionality). (#1071, #1098, #1108, #1126)
  • Added an option to vary the number of warm-up cycles and iterations for ckProfiler (#1124)

Optimizations

  • New performance optimizations for GEMM operations on MI200 and MI300 architectures (#1135)

Fixes

  • Reduced the build time for most GPU architectures (#1084)
  • Fixed some conversion issues for fp8 data type (#1099)

Changes

None

Known issues

None

Composable Kernel 1.1.0 for ROCm 6.0.0

Fixes

  • Fixed a hazard associated with inline v_dot (#808)
  • Fixed two bugs in grouped convolution backward data without K padding (#848 #876)

Optimizations

None

Additions

  • Added an image to a column kernel (#867)
  • Added a column to an image kernel (#930)
  • Support for 3D grouped convolution on RDNA 3 GPUs (#935, #950, #985)
  • Grouped convolution support for small K and C (#822 #879 #897)
  • Support for NHWGC (2D and 3D) grouped convolution backward weight (#769 #804)
  • Support for bf16/f32/f16 and NHWGC (2D and 3D) grouped convolution backward data (#757 #799)
  • Support for Batched GEMM DL (#732)

Changes

  • Changed the grouped convolution API to maintain consistency with other convolution kernels (#817)

Composable Kernel 0.2.0 for ROCm 5.7.0

Fixes

  • Fixed a bug in 6-dimensional kernels (#555)
  • Fixed a test case failure with grouped convolution backward weight (#524)

Optimizations

  • Improved the performance of the normalization kernel

Additions

  • New CMake flags:
    • "DL_KERNELS"-* Must be set to "ON" in order to build the GEMM DL and batched_gemm_multi_d_dl instances
    • "DTYPES" -- Can be set to any subset of "fp64;fp32;fp16;fp8;bf16;int8" to build an instance of the specified data types
    • "INSTANCES_ONLY" -- Only builds CK library and instances without tests, examples, or profiler
  • New feature: if GPU_TARGETS is not set in the CMake command line, CK will be built for all targets supported by the compiler
  • Support for MI300A/MI300X
  • Support for AMD RDNA 3
  • New user tutorial (#563)
  • Additional instances for irregular GEMM sizes (#560)
  • New inter-wave consumer-producer programming model for GEMM kernels (#310)
  • GEMM with support multiple elementwise fusions (multi-D) (#534)
  • Multi-embeddings support (#542)
  • AMD RDNA 3 blockwise GEMM and real GEMM support (#541)
  • AMD RDNA grouped convolution backward weight support (#505)
  • MaxPool and AvgPool forward (#815); MaxPool backward (#750)

Changes

None

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