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* 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.
[ROCm/composable_kernel commit: ad57f6ef0b]
GEMM with Multiple D and Multiple Reductions
Theory
This example demonstrates GEMM with multiple auxiliary tensors (D) and multiple reduction operations. This pattern is used in advanced neural network layers that require additional outputs or statistics (such as sums, means, or other reductions) alongside the main GEMM result.
Mathematical Formulation:
- For each GEMM:
C = A \times B - Auxiliary tensors:
D_0, D_1, ...(various shapes) - Reductions: e.g., sum, mean, max over specified axes or outputs
The kernel computes the main GEMM output and additional reductions or statistics in a single pass.
Algorithmic Background:
- The GEMM result is kept in registers, auxiliary tensors are fused in the epilogue, and reductions are computed as part of the output.
- Useful for multi-task learning, attention statistics, and custom neural network layers.
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/16_gemm_multi_d_multi_reduces
mkdir build && cd build
cmake -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc ..
make -j
# Example run
./gemm_multi_d_multi_reduces_xdl --verify=1 --time=1
Source Code Structure
Directory Layout
example/16_gemm_multi_d_multi_reduces/
├── gemm_multi_d_multi_reduces_xdl.cpp # Main example: sets up, runs, and verifies GEMM with multi-D/multi-reduce
include/ck/tensor_operation/gpu/device/
│ └── device_gemm_multi_d_multi_reduces.hpp # Device-level API for multi-D/multi-reduce GEMM
include/ck/tensor_operation/gpu/device/impl/
│ └── device_gemm_multi_d_multi_reduces_impl.hpp # Implementation
include/ck/tensor_operation/gpu/grid/
└── gridwise_gemm_multi_d_multi_reduces.hpp # Grid-level kernel
Key Classes and Functions
- DeviceGemmMultiDMultiReduces (in
device_gemm_multi_d_multi_reduces.hpp):
Device API for GEMM with multiple outputs and reductions. - gridwise_gemm_multi_d_multi_reduces (in
gridwise_gemm_multi_d_multi_reduces.hpp):
Implements the tiled/blocking GEMM kernel with multi-output/reduce epilogue.
This example demonstrates how Composable Kernel supports advanced GEMM patterns with multiple outputs and reductions in a single efficient kernel.