mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-04 21:51:28 +00:00
37e1a27537
* [CK_TILE] Refine fp8 in flatmm 1. Replace USING_MFMA_16x16x32 & USING_MFMA_16x16x32 with constexpr 2. Add an additional const check to avoid build error in HotLoopScheduler 3. Refine shuffleb to support both tile 32x32 and 16x16 4. Support command option -init 5. Move Gemm warp defintion to a separate struct * fix clang format * fix clang format * keep default bhavior unchanged (warp tile = 16x16) * fix tile engine build error * fix a typo in codegen_utils.py * address review comments * address review comments --------- Co-authored-by: Thomas Ning <Thomas.Ning@amd.com>
FLATMM Matrix Multiplication
This folder contains example for FLATMM using ck_tile tile-programming implementation. Currently, it only supports the basic feature of the CK Tile FLATMM, but creates the placeholders for the future support on different FLATMM pipeline and different FLATMM modules. In the near future, we will gradually migrate all the FLATMM features from old CK to CK Tile.
build
# in the root of ck_tile
mkdir build && cd build
# you can replace <arch> with the appropriate architecture (for example gfx90a or gfx942) or leave it blank
sh ../script/cmake-ck-dev.sh ../ <arch>
# The basic pipeline method on the flatmm calculation
make tile_example_flatmm_basic -j
This will result in an executable build/bin/tile_example_flatmm_basic
example
args:
-b batch size (default:1)
-m m dimension (default:1024)
-n n dimension (default:2048)
-k k dimension (default:64)
-a_layout Tensor A data layout (default: R)
-b_layout Tensor B data layout (default: R)
-c_layout Tensor C data layout (default: R)
-stride_a Tensor A 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:2)
-e Absolute error tolerance (default:1e-5)
-prec data type. fp16/bf16/fp8/bf8 (default:fp16)
-warmup number of iterations before benchmark the kernel (default:10)
-repeat number of iterations to benchmark the kernel (default:100)
-timer gpu:gpu timer, cpu:cpu timer (default:gpu)