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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]
Client Example: Grouped GEMM with bf16A/int8B and Fused Epilogues
Theory
This client example demonstrates grouped GEMM with mixed-precision input types (bf16 for A, int8 for B) and various fused epilogue operations (bias, FastGELU, multiply). Grouped GEMM performs multiple independent GEMM operations (with potentially different shapes) in a single kernel launch, and mixed-precision is used for efficient inference and training.
Mathematical Formulation:
For G groups, each with its own A_g, B_g:
- GEMM:
Y_g = A_g \times B_gA_g: bf16 (brain floating point)B_g: int8 (8-bit integer)
- Fused epilogues:
- Bias:
Z_g = Y_g + \text{bias}_g - FastGELU:
E_g = \text{FastGELU}(Z_g) - Multiply:
E_g = Z_g \odot D_{1,g}
- Bias:
Algorithmic Background:
- Each group can have different matrix sizes and strides.
- Mixed-precision computation reduces memory and compute requirements.
- Fused epilogues improve efficiency by combining bias, activation, and scaling in a single kernel.
How to Run
Prerequisites
Please follow the instructions in the main Build Guide section as a prerequisite to building and running this example.
cd composable_kernel/build
cmake -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc -D DTYPES="bf16;int8" ..
make -j
make install
Build and run
cd composable_kernel/client_example/31_grouped_gemm_bf16Aint8B
mkdir build && cd build
cmake -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc ..
make -j
# Example run (basic grouped GEMM)
./grouped_gemm_xdl_bf16_i8
# Example run (grouped GEMM + bias + FastGELU)
./grouped_gemm_bias_fastgelu_xdl_bf16_i8
# Example run (grouped GEMM + FastGELU)
./grouped_gemm_fastgelu_xdl_bf16_i8
# Example run (grouped GEMM + multiply)
./grouped_gemm_multiply_xdl_bf16_i8
# Example run (grouped GEMM + multiply + bias + FastGELU)
./grouped_gemm_multiply_bias_fastgelu_xdl_bf16_i8
Source Code Structure
Directory Layout
client_example/31_grouped_gemm_bf16Aint8B/
├── grouped_gemm_xdl_bf16_i8.cpp # Grouped GEMM (bf16A, int8B)
├── grouped_gemm_bias_fastgelu_xdl_bf16_i8.cpp # Grouped GEMM + bias + FastGELU
├── grouped_gemm_fastgelu_xdl_bf16_i8.cpp # Grouped GEMM + FastGELU
├── grouped_gemm_multiply_xdl_bf16_i8.cpp # Grouped GEMM + multiply
├── grouped_gemm_multiply_bias_fastgelu_xdl_bf16_i8.cpp # Grouped GEMM + multiply + bias + FastGELU
├── CMakeLists.txt # Build configuration for the example
Key Functions
- main() (in each
.cpp):
Sets up input matrices for each group, configures GEMM and epilogue parameters, launches the grouped kernel, and verifies the result. - Grouped GEMM kernel invocation:
Uses the Composable Kernel device API to launch grouped GEMM with various fused epilogues.
Additional Details
- Supports multiple groups with different matrix shapes and bf16/int8 input types.
- Example parameters can be adjusted in the source for different workloads.
Related Examples
- 30_gemm_bf16Aint8B: GEMM with bf16A/int8B and fused epilogues
- 15_grouped_gemm: Grouped GEMM in the main example directory