mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-14 18:17:44 +00:00
ad57f6ef0b
* Wrap ck host utitlies in CK namespace. The CK and CK-Tile source code bases are incompatible because CK is not properly using namespaces everywhere. In particular, we need to put hip_check_error in the ck namespace. Move all functions in include/ck_/host_utility that were in global namespace into the ck namespace. There may be additional namespace problems like this, and it's possible we'll have namespace clashes. But it is good design to properly guard our to code bases (CK and CKTile) so that they can both coexist. Moreover, estabilishing this compatiblity is essential if we are going to allow the builder to instantiate kernels from either template library. * Add using declarations to test code. After moving some of the untils into the ck namespace, most examples and a few tests had to be updated to recognize the new namespace declarations. We add using declarations to individual compute units for functions that were previously in the global namespace. * Add using declarations to client examples.
Grouped GEMM
Theory
This example demonstrates grouped GEMM: performing multiple independent GEMM operations (with potentially different shapes) in a single kernel launch. Grouped GEMM is used in transformer models (e.g., multi-head attention), mixture-of-experts, and other architectures requiring heterogeneous batched matrix multiplications.
Mathematical Formulation:
For G groups, each with its own A_g, B_g, C_g:
C_g = A_g \times B_g \quad \text{for} \quad g = 1, 2, ..., G
A_g: [M_g, K_g] input matrix for groupgB_g: [K_g, N_g] weight matrix for groupgC_g: [M_g, N_g] output matrix for groupg
Algorithmic Background:
- Each group can have different matrix sizes and strides.
- The kernel launches a grid covering all groups, with each block assigned to a group.
- Useful for variable-length sequences, multi-head attention, and expert routing.
How to Run
Prerequisites
Please follow the instructions in the main Build Guide section as a prerequisite to building and running this example.
Build and run
cd composable_kernel/example/15_grouped_gemm
mkdir build && cd build
cmake -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc ..
make -j
Run example_grouped_gemm_xdl
#arg1: verification (0=no, 1=yes)
#arg2: initialization (0=no init, 1=integer value, 2=decimal value)
#arg3: run kernel # of times (>1)
./bin/example_grouped_gemm_xdl_fp16 0 1 5
Source Code Structure
Directory Layout
example/15_grouped_gemm/
├── grouped_gemm_xdl.cpp # Main example: sets up, runs, and verifies grouped GEMM
include/ck/tensor_operation/gpu/device/
│ └── device_grouped_gemm_xdl.hpp # Device-level grouped GEMM API
include/ck/tensor_operation/gpu/grid/
│ └── gridwise_grouped_gemm_xdl.hpp # Grid-level grouped GEMM kernel
Key Classes and Functions
- DeviceGroupedGemmXdl (in
device_grouped_gemm_xdl.hpp):
Device API for grouped GEMM. - gridwise_grouped_gemm_xdl (in
gridwise_grouped_gemm_xdl.hpp):
Implements the tiled/blocking grouped GEMM kernel.
This example demonstrates how Composable Kernel supports efficient heterogeneous batched matrix multiplication for advanced AI/ML workloads.