Files
composable_kernel/example/ck_tile/38_block_scale_gemm
Thomas Ning 5cb8109535 [rocm-libraries] ROCm/rocm-libraries#4640 (commit 37b8c81)
Fix the Composable Kernel CI and versions incompatibility
 (#4640)

## Motivation

This PR has 4 patches:
1. Fix the CI error of grouped gemm.
2. Fix the incompatibility of old linux version.
3. Fix the potential errors of flatmm.
4. Address the previous comments of abquant eight warps pipeline
solution.
2026-02-18 15:00:26 +00:00
..
2025-12-11 07:20:29 -08:00

Quant GEMM Matrix Multiplication

This folder contains examples of quant GEMMs using the ck_tile tile-programming implementation.

  • AQuant kernel with blocks of A matrix sharing scales: custom GEMM pipeline
  • BQuant kernel with blocks of B matrix sharing scales: custom GEMM pipeline
  • Row and Column-wise scaled: All of the row-wise elements in A Matrix and column-wise elements in B Matrix will share the same quantization element and the element-wise operation will complete in epilogue.
  • Tensor-wise scaled: Share the same scalar scale across the whole tensor of A or B

Quantization Mode Comparison

Quant Mode A Matrix Organization A Scale Shape B Matrix Organization B Scale Shape
AQuant Blocks along K dimension
Each M×GroupSize block shares one scale
[M, K/GroupSize] Not quantized N/A
BQuant Not quantized N/A Blocks along K dimension
Each GroupSize×N block shares one scale
[K/GroupSize, N]
RowColQuant Per-row quantization
All K elements in each row share one scale
[M, 1] Per-column quantization
All K elements in each column share one scale
[1, N]
TensorQuant Tensor-wise quantization
All M×K elements share one scale
[1] Tensor-wise quantization
All K×N elements share one scale
[1]

Features

  • Preshuffled GEMM: Shuffle the GEMM of B (weight) matrix in the warp layout and bypass the shared memory to do the GEMM calculation. Best performance solution for GEMM.
  • TransposeC: Transpose the C Matrix Output layout to have the best coalesced scale reading
  • Preshuffled Quant: Preshuffle the input matrix to load multiple Quant warp blocks along the selected dimension.
  • Precision: Supports fp16, bf16, fp8, bf8, int4 (for B Matrix), uint8 (split into two fp4 in the pipeline (for B Matrix)).
  • Validation: CPU/GPU validation and error tolerance options.

build

# in the root of ck_tile
mkdir build && cd build
# you can replace <arch> with the appropriate architecture (for example gfx942) or leave it blank
../script/cmake-ck-dev.sh  ../ <arch>
# Compile the quant kernels
make tile_example_gemm_quant -j

This will result in an executable build/bin/tile_example_gemm_quant

example

args:
               -h    Print help message (default:false)
               -m    m dimension (default:3840)
               -n    n dimension (default:4096)
               -k    k dimension (default:2048)
        -a_layout    A tensor data layout - R for Row or C for Column (default:R)
        -b_layout    B tensor data layout - R for Row or C for Column (default:C)
       -bq_layout    Bq tensor data layout - R for Row or C for Column (default:C)
        -c_layout    C tensor data layout - R for Row or C for Column (default:R)
        -stride_a    Tensor A stride (default:0)
        -stride_q    Tensor AQ stride (default:0)
        -stride_b    Tensor B stride (default:0)
        -stride_c    Tensor C stride (default:0)
               -v    0: No validation, 1: Validation on CPU, 2: Validation on GPU (default:1)
            -prec    Data type. For AQuant: fp8, bf8, i4fp8, or i4bf8;  for Bquant: fp8, bf8, fp8i4, bf8i4, or bf16fp4 (default for both AQuant and Bquant: fp8)
          -warmup    Number of iterations before benchmarking the kernel (default:50)
          -repeat    Number of iterations to benchmark the kernel (default:1000)
           -timer    gpu:gpu timer, cpu:cpu timer (default:gpu)
         -split_k    SplitK value (default:1)
          -device    Device id that will be used to run the kernel (default:0)
            -init    0:random, 1:linear, 2:constant(1) (default:0)
     -flush_cache    Flush cache before running the kernel (default:true)
  -rotating_count    Rotating count (default:1000)
      -quant_mode    Choose aquant, bquant, tensor or rowcol (default:bquant)
     -preshuffleb    Enable preshuffle of tensor B (default:false)
 -preshufflequant   Enable preshuffle of quant tensor (default:false)
      -group_size    Quantization group size as MxNxK, e.g., 1x1x128, 1x32x128, 1x64x128 (default:1x1x128)

User need to select correct mapping of config for each quant mode:

quant_mode as runtime argument Corresponding cpp file GemmConfig at the top of cpp file
For selecting AQuant aquant gemm_aquant_quantgrouped.cpp GemmConfigQuantDecode
For selecting AQuant with Preshuffle quant aquant gemm_aquant_quantgrouped_preshufflequant.cpp GemmConfigPreshuffleQuantDecode
For selecting BQuant bquant gemm_bquant_quantgrouped_<prec_type>.cpp GemmConfigQuantDecode (or) GemmConfigQuantPrefill
For selecting BQuant with Preshuffle quant bquant gemm_bquant_quantgrouped_preshufflequant.cpp GemmConfigPreshuffleQuantDecode (or) GemmConfigPreshuffleBQuantPrefill
For selecting PreShuffle B with BQuant bquant gemm_bquant_quantgrouped_preshuffleb.cpp GemmConfigPreshuffleB_BQuant_Decode (or) GemmConfigPreshuffleB_BQuant_Prefill
For selecting PreShuffle B with preshuffle BQuant bquant gemm_bquant_quantgrouped_preshuffleb_preshufflequant.cpp GemmConfigPreshuffleB_PreshuffleBQuant_Decode (or) GemmConfigPreshuffleB_PreshuffleBQuant_Prefill
For selecting RowCol quant rowcolquant gemm_quant_rowcol GemmConfigRowColQuant