ROCm/composable_kernel
1
0
Fork 0
mirror of https://github.com/ROCm/composable_kernel.git synced 2026-04-20 06:49:15 +00:00
Code Issues Packages Projects Releases Wiki Activity

CK: removed the api reference (#3571)

Browse Source 
* removed the api reference

* updating to the latest rocm-docs-core min version

* fixed a formatting issue with buffer views

* removed reference links from code snippets

* removed reference links from code snippets

---------

Co-authored-by: John Afaganis <john.afaganis@amd.com>
This commit is contained in:
spolifroni-amd
2026-01-27 10:36:47 -05:00
committed by GitHub
parent b66597ed96
commit 0cc83cb8e8
25 changed files with 130 additions and 3160 deletions
Download Patch File Download Diff File Expand all files Collapse all files

14
docs/conceptual/ck_tile/convolution_example.rst
View File

@@ -59,10 +59,6 @@ The key insight is that convolution can be transformed from a complex nested loo
.. image:: diagrams/convolution_example.svg
:alt: Diagram
:align: center
.. image:: diagrams/convolution_example.svg
:alt: Diagram
:align: center
@@ -88,7 +84,6 @@ Non-overlapping tiles:
// Original matrix: shape=(6, 6), strides=(6, 1)
// Tiled view: shape=(3, 3, 2, 2), strides=(12, 2, 6, 1)
// See :ref:`ck_tile_descriptors` for descriptor details
using TileDescriptor = TensorDescriptor<
Sequence<kNumTiles, kNumTiles, kTileSize, kTileSize>,
Sequence<12, 2, 6, 1>
@@ -243,7 +238,6 @@ The im2col transformation converts the 4D windows tensor into a 2D matrix suitab
>;
// Step 2: Apply merge transforms to create 2D im2col layout
// See :ref:`ck_tile_transforms` for transform operations
using Im2colDescriptor = decltype(
transform_tensor_descriptor(
WindowsDescriptor{},
@@ -312,7 +306,6 @@ Combining all components into an optimized convolution implementation:
>;
// Tile distribution for matrix multiplication
// See :ref:`ck_tile_tile_distribution` for details
using ATileDist = TileDistribution<
Sequence<TileM, TileK>,
Sequence<BlockM, 1>
@@ -327,7 +320,6 @@ Combining all components into an optimized convolution implementation:
>;
// Thread-local accumulator
// See :ref:`ck_tile_static_distributed_tensor`
StaticDistributedTensor<DataType, CTileDist> c_accumulator;
// Initialize accumulator
@@ -339,7 +331,6 @@ Combining all components into an optimized convolution implementation:
// Main GEMM loop over K dimension
for (index_t k_tile = 0; k_tile < PatchSize; k_tile += TileK) {
// Create tile windows for im2col matrix and kernel
// See :ref:`ck_tile_tile_window` for window operations
auto a_window = make_tile_window<ATileDist>(
input, Im2colDesc{H, W, K},
{blockIdx.y * TileM, k_tile}
@@ -350,7 +341,7 @@ Combining all components into an optimized convolution implementation:
{k_tile, 0}
);
// Load tiles - see :ref:`ck_tile_load_store_traits` for optimization
// Load tiles
auto a_tile = a_window.load();
auto b_tile = b_window.load();
@@ -476,7 +467,6 @@ CK Tile enables several optimizations for convolution:
__shared__ float smem_b[TileK][TileN];
// Collaborative loading with proper bank conflict avoidance
// See :ref:`ck_tile_lds_bank_conflicts` for optimization
auto load_tile_to_smem = [&](auto& window, float smem[][TileK]) {
#pragma unroll
for (index_t i = threadIdx.y; i < TileM; i += blockDim.y) {
@@ -560,7 +550,7 @@ This example demonstrates how CK Tile transforms convolution from a memory-bound
- **Sliding windows** can be efficiently represented using tensor descriptors with appropriate strides
- **Im2col transformation** converts convolution to matrix multiplication without data copies
- **Tile distribution** enables optimal work distribution across GPU threads (see :ref:`ck_tile_tile_distribution`)
- **Tile distribution** enables optimal work distribution across GPU threads (see :ref:`ck_tile_distribution`)
- **Multi-channel support** extends naturally through higher-dimensional descriptors
- **Performance optimizations** like vectorization and shared memory are seamlessly integrated (see :ref:`ck_tile_gemm_optimization` for similar techniques)