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[CK_TILE][GFX1250] Enable MX GEMM FLATMM with ASYNC ## Motivation Enables MX GEMM FLATMM pipeline on gfx1250. The pipeline uses an async load instruction for tensor A, which complements the existing MX GEMM FLATMM pipeline with TDM load. At this time, only FLATMM MX pipelines are enabled on gfx1250. ## Technical Details The existing gfx950 implementation was extended to support gfx1250 architecture. All three MX FP data types are supported across the two ASICs. It should be noted that while the TDM pipeline uses an emulated 32x32x128 warp-tile instruction, the present submission relies on the built-in 16x16x128 instruction, called 4 times per warp. ## Test Plan Existing `test/ck_tile/flatmm` tests were extended to cover new gfx1250 functionality. To help facilitate the testing in development, `example/ck_tile/18_flatmm/script/smoke_test_mx.sh` script was introduced to verify various combinations of supported data types and pipeline versions. ## Test Result The present submission is expected to work on both gfx950 and gfx1250 hardware for all reasonable sizes and all MX FP8/FP6/FP4 data types. ## Submission Checklist - [x] Look over the contributing guidelines at https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests. - [x] Relies on #6978 and should only be merged after the changes are merged to the `develop`.
FLATMM Matrix Multiplication with CK Tile
This example demonstrates FLATMM (flattened matrix multiplication) using the CK Tile programming model. FLATMM is a variant of GEMM optimized for certain memory layouts and batch processing patterns. 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.
Algorithm and Math
Given:
A:[\text{batch}, M, K]B:[\text{batch}, K, N]C:[\text{batch}, M, N]
For each batch b:
C^{(b)} = A^{(b)} \times B^{(b)}
- FLATMM: An alternative solution as the Preshuffled GEMM in /03_gemm
Build & Run
# 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
../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
Arguments
args:
-m m dimension (default:256)
-n n dimension (default:256)
-k k dimension (default:128)
-a_layout A tensor data layout - Row by default (default:R)
-b_layout B tensor data layout - Row by default (default:C)
-c_layout C tensor data layout - Row by default (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:1)
-prec data type. fp16/bf16/fp8/bf8 (default:fp16)
-warmup number of iterations before benchmark the kernel (default:50)
-repeat number of iterations to benchmark the kernel (default:100)
-timer gpu:gpu timer, cpu:cpu timer (default:gpu)
-split_k splitK value (default:1)
-init 0:random, 1:linear, 2:constant(1) (default:0)
-warp_tile 0: 16x16, 1: 32x32, 2: 16x16x128 (950 only), 3: 32x32x64 (950 only) (default:0)
-json 0: No Json, 1: Dump Results in Json format (default:0)
-jsonfile json file name to dump results (default:flatmm_basic.json)
Source Structure
- Kernel:
flatmm_basic.hpp(tile-programming kernel template) - Executable:
flatmm_basic.cpp - Build:
CMakeLists.txt,run_flatmm_example.inc,script/
Related CK Tile Examples
- 16_batched_gemm: Batched GEMM with tiles
- 03_gemm: Single GEMM with tiles
- 17_grouped_gemm: Grouped GEMM with tiles
For distribution, see include/ck_tile/tile_program/tile_distribution/.