mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-14 10:09:41 +00:00
b5e2f26808
* 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 Microscaling
This example demonstrates a GEMM operation with microscaling, an advanced quantization technique that applies fine-grained scaling to small blocks of data. Microscaling enables more precise quantization than traditional methods by using different scale factors for small groups of elements, leading to better accuracy preservation in quantized neural network inference.
Source Code Organization
gemm_microscaling_xdl.cpp: The main example file. It sets up microscaled matrices with quantized data and scale factors, and instantiates theDeviceGemmMicroscalingoperation.../../include/ck/tensor_operation/gpu/device/device_gemm_microscaling.hpp: The device interface for GEMM with microscaling support.- The underlying kernel implements sophisticated block-wise dequantization integrated into the GEMM computation pipeline.
Build and Run
example_gemm_mx_fp8
Custom verification parameters:
# arg1: verification (0=no, 1=CPU)
# arg2: initialization (0=constant values, 1=integer values, 2=decimal values)
# arg3: time kernel (0=no, 1=yes)
# arg4: verbosity (0=no info, 1=verbose info)
# arg5 to 10: M(256x), N(256x), K(512x), StrideA, StrideB, StrideC
# arg11: KBatch
# arg12: warmup runs pre-timing
# arg13: repeat run count for timing
./bin/example_gemm_mx_fp8 1 1 0 1
Custom tensor shapes:
./bin/example_gemm_mx_fp8 1 2 1 0 256 256 512 -1 -1 -1 1 10 10
Run the Example
Custom verification parameters:
# arg1: verification (0=no, 1=CPU)
# arg2: initialization (0=constant values, 1=integer values, 2=decimal values)
# arg3: time kernel (0=no, 1=yes)
# arg4: verbosity (0=no info, 1=verbose info)
# arg5 to 10: M(128x), N(128x), K(64x), StrideA, StrideB, StrideC
# arg11: KBatch
./bin/example_gemm_mx_fp8 1 1 0 1
Custom tensor shapes:
./bin/example_gemm_mx_fp8 1 2 1 0 128 128 256 -1 -1 -1 1
Default invocation:
# Implies: ./bin/example_gemm_mx_fp8 1 2 0 0
./bin/example_gemm_mx_fp8