c24e528481
[CK] suppress compiler warnings while building pytorch. (#7760) ## Motivation Recently added compiler flags that are required to suppress false warnings by latest staging compiler are not recognized by older compiler versions and are triggering an avalanche of warnings. Previous attempt to suppress them by using -Wno-unknown-warning-option flag didn't help, because that flag wasn't recognized either and just added more warnings. I've verified that current approach by checking the clang version actually works as intended and makes the warnings go away. ## Technical Details <!-- Explain the changes along with any relevant GitHub links. --> ## Test Plan <!-- Explain any relevant testing done to verify this PR. --> ## Test Result <!-- Briefly summarize test outcomes. --> ## Submission Checklist - [ ] Look over the contributing guidelines at https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
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.