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John Shumway ad57f6ef0b [CK_BUILDER] Put global CK functions in an the CK namespace (#3232 )

* 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.

2025-11-19 11:23:02 +01:00

CMakeLists.txt

Standalone softmax kernel (#284 )

2022-06-21 14:59:19 -05:00

README.md

[DOCS] Documentation Addition (Readme updates) (#2495 )

2025-10-16 03:10:57 -07:00

softmax_blockwise.cpp

[CK_BUILDER] Put global CK functions in an the CK namespace (#3232 )

2025-11-19 11:23:02 +01:00

README.md

Parallel Softmax

Theory

This example demonstrates parallel softmax computation over tensors. Softmax is a key operation in deep learning, especially in attention mechanisms and classification, converting logits into normalized probabilities.

Mathematical Formulation: Given input X and axis a:


\text{softmax}(X)_i = \frac{\exp(X_i)}{\sum_j \exp(X_j)}

Algorithmic Background:

Softmax is implemented using a numerically stable algorithm:
1. Subtract the maximum value for numerical stability.
2. Exponentiate and sum.
3. Normalize by the sum.
Efficient parallel softmax requires careful reduction and memory access patterns.

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/23_softmax
mkdir build && cd build
cmake -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc ..
make -j

# -D <xxx> : input 3-d tensor lengths
# -v <x> :   verification (0=no, 1=yes)
#arg1: initialization (0=no init, 1=single integer value, 2=scope integer value, 3=decimal value)
#arg2: time kernel (0=no, 1=yes)
example_softmax_blockwise -D 4,128,2048 -v 1 1 1

Result

launch_and_time_kernel: grid_dim {64, 1, 1}, block_dim {256, 1, 1}
Warm up 1 time
Start running 10 times...
Perf: 0.0242877 ms, 259.039 GB/s, DeviceReduceSoftmax<256,M_C8_S1,K_C32_S8,InSrcVectorDim_1_InSrcVectorSize_8_OutDstVectorSize_8>

Source Code Structure

Directory Layout

example/23_softmax/
├── softmax_xdl.cpp         # Main example: sets up, runs, and verifies softmax
include/ck/tensor_operation/gpu/device/
│   └── device_softmax.hpp       # Device-level softmax API
include/ck/tensor_operation/gpu/device/impl/
│   └── device_softmax_impl.hpp  # Implementation
include/ck/tensor_operation/gpu/grid/
│   └── gridwise_softmax.hpp     # Grid-level softmax kernel
include/ck/tensor_operation/gpu/block/
    └── blockwise_softmax.hpp    # Block-level softmax

Key Classes and Functions

DeviceSoftmax (in device_softmax.hpp):
Device API for softmax.
gridwise_softmax (in gridwise_softmax.hpp):
Implements the tiled/blocking softmax kernel.
blockwise_softmax (in blockwise_softmax.hpp):
Handles block-level softmax and shared memory.

This example demonstrates how Composable Kernel implements efficient, numerically stable softmax for deep learning models.