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.
Tensor Permutation (Dimension Reordering)
Theory
This example demonstrates tensor permutation operations, which reorder the dimensions of tensors according to a specified permutation pattern. Permutation is fundamental for many machine learning operations, including tensor layout transformations, data format conversions, and implementing complex tensor operations.
Mathematical Formulation:
Given an input tensor X with shape [D_0, D_1, ..., D_{n-1}] and a permutation pattern P = [p_0, p_1, ..., p_{n-1}], the permutation operation produces an output tensor Y with shape [D_{p_0}, D_{p_1}, ..., D_{p_{n-1}}] such that:
Y_{i_{p_0}, i_{p_1}, ..., i_{p_{n-1}}} = X_{i_0, i_1, ..., i_{n-1}}
Algorithmic Background:
- Permutation is used for matrix transpose, NCHW/NHWC layout conversion, attention head reshaping, and more.
- Efficient permutation requires optimizing memory access patterns for coalescing and bandwidth.
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/39_permute
mkdir build && cd build
cmake -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc ..
make -j
# Example run (matrix transpose)
./permute_xdl --input_shape=4096,4096 --permutation=1,0 --verify=1 --time=1
# Example run (NCHW to NHWC)
./permute_xdl --input_shape=32,256,56,56 --permutation=0,2,3,1 --verify=1 --time=1
Source Code Structure
Directory Layout
example/39_permute/
├── permute_xdl.cpp # Main example: sets up, runs, and verifies tensor permutation
include/ck/tensor_operation/gpu/device/
│ └── device_permute.hpp # Device-level permutation API
include/ck/tensor_operation/gpu/grid/
│ └── gridwise_permute.hpp # Grid-level permutation kernel
Key Classes and Functions
- DevicePermute (in
device_permute.hpp):
Device API for tensor permutation. - gridwise_permute (in
gridwise_permute.hpp):
Implements the tiled/blocking permutation kernel.
This example demonstrates how Composable Kernel implements efficient tensor dimension reordering for layout transformations and deep learning operations.