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* 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.
Parallel Reduction Operations
Theory
This example demonstrates parallel reduction operations (e.g., sum, max, min, mean) over tensors. Reduction is a fundamental operation in deep learning for computing statistics (such as batch mean/variance), loss aggregation, and normalization.
Mathematical Formulation:
Given a tensor X and a reduction axis a:
Y = \text{reduce}_{a}(X)
- For sum:
Y = \sum_{i \in a} X_i - For max:
Y = \max_{i \in a} X_i - For mean:
Y = \frac{1}{|a|} \sum_{i \in a} X_i
Algorithmic Background:
- Reductions are implemented using parallel tree reduction or segmented reduction algorithms.
- Efficient reductions require careful memory access, synchronization, and sometimes numerically stable algorithms (e.g., Welford's for variance).
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/12_reduce
mkdir build && cd build
cmake -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc ..
make -j
Run example_reduce_blockwise
# -D <xxx> : input 3D/4D/5D tensor lengths
# -R <xxx> : reduce dimension ids
# -v <x> : verification (0=no, 1=yes)
#arg1: data type (0: fp16, 1: fp32, 3: int8, 5: bp16, 6: fp64, 7: int4)
#arg2: initialization (0=no init, 1=single integer value, 2=scope integer value, 3=decimal value)
#arg3: time kernel (0=no, 1=yes)
./bin/example_reduce_blockwise -D 16,64,32,960 -v 1 0 2 1
Expected Result:
./bin/example_reduce_blockwise -D 16,64,32,960 -v 1 0 2 1
launch_and_time_kernel: grid_dim {240, 1, 1}, block_dim {256, 1, 1}
Warm up 1 time
Start running 10 times...
Perf: 0.238063 ms, 264.285 GB/s, DeviceReduceBlockWise<256,M_C4_S1,K_C64_S1,InSrcVectorDim_0_InSrcVectorSize_1_OutDstVectorSize_1>
Run example_reduce_multiblock_atomic_add
# -D <xxx> : input 3D/4D/5D tensor lengths
# -R <xxx> : reduce dimension ids
# -v <x> : verification (0=no, 1=yes)
#arg1: data type (0: fp32, 1: fp64)
#arg2: initialization (0=no init, 1=single integer value, 2=scope integer value, 3=decimal value)
#arg3: time kernel (0=no, 1=yes)
./bin/example_reduce_multiblock_atomic_add -D 16,64,32,960 -v 1 0 2 0
Expected Result
./bin/example_reduce_multiblock_atomic_add -D 16,64,32,960 -v 1 0 2 0
Perf: 0 ms, inf GB/s, DeviceReduceMultiBlock<256,M_C4_S1,K_C64_S1,InSrcVectorDim_0_InSrcVectorSize_1_OutDstVectorSize_1>
echo $?
0
Instructions for example_reduce_blockwise_two_call
Run example_reduce_blockwise_two_call
#arg1: verification (0=no, 1=yes(
#arg2: initialization (0=no init, 1=single integer value, 2=scope integer value, 3=decimal value)
#arg3: time kernel (0=no, 1=yes)
./bin/example_reduce_blockwise_two_call 1 2 1
Expected Result:
./bin/example_reduce_blockwise_two_call 1 2 1
launch_and_time_kernel: grid_dim {204800, 1, 1}, block_dim {256, 1, 1}
Warm up 1 time
Start running 10 times...
launch_and_time_kernel: grid_dim {6400, 1, 1}, block_dim {256, 1, 1}
Warm up 1 time
Start running 10 times...
Perf: 2.1791 ms, 771.42 GB/s, DeviceReduceBlockWise<256,M_C32_S1,K_C8_S1,InSrcVectorDim_1_InSrcVectorSize_1_OutDstVectorSize_1> => DeviceReduceBlockWise<256,M_C256_S1,K_C1_S1,InSrcVectorDim_1_InSrcVectorSize_1_OutDstVectorSize_1>
Source Code Structure
Directory Layout
example/12_reduce/
├── reduce_xdl.cpp # Main example: sets up, runs, and verifies reduction
include/ck/tensor_operation/gpu/device/
│ └── device_reduce.hpp # Device-level reduction API
include/ck/tensor_operation/gpu/device/impl/
│ └── device_reduce_impl.hpp # Implementation
include/ck/tensor_operation/gpu/grid/
│ └── gridwise_reduce.hpp # Grid-level reduction kernel
include/ck/tensor_operation/gpu/block/
└── blockwise_reduce.hpp # Block-level reduction
Key Classes and Functions
- DeviceReduce (in
device_reduce.hpp):
Device API for reductions. - gridwise_reduce (in
gridwise_reduce.hpp):
Implements the tiled/blocking reduction kernel. - blockwise_reduce (in
blockwise_reduce.hpp):
Handles block-level reduction and shared memory.
This example demonstrates how Composable Kernel implements efficient parallel reductions for deep learning and scientific computing.