mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-12 09:16:52 +00:00
7fa892e63e
* Update to the batchnorm-forward API and base class * Fix leeked header including in gridwise_set_buffer_value.hpp * Add kernels and device file for batchnorm-forward welford supporting both blockwise and multi-block reduction * Update to the batchnorm-forward example to use the new batchnorm-forward device interface * Change the batchnorm-forward reference to use sequential welford method * Change to assign the workspace into four buffers in the host layer * Use GetReduceCountPerThread functor to replace the initial count for Blockwise and Multiblock welford * Tiny correction and remove un-used file under example/34_batchnorm * Renaming in the kernel arguments * Explicitly use ck::math::sqrt in batchnorm-forward kernels * Add some comments to some kernels * Tiny fix * Generalize the data types in reference_batchnorm_forward_nhwc_c * Use ck::ignore to mark un-used parameters * Move GetReduceCountPerThread functor codes from kernel to device * Remove some un-used codes in device_batchnorm_forward_impl.hpp * Tiny fix in batchnorm_forward example * Move GetReduceCountPerThread() to welford_helper.hpp * Use seperate data type for Scale and Bias * Renaming in device Op * Tiny fix in forward example * Updata to batchnorm-infer (type spliting, renaming) * Add time and bandwidth measurement to the batchnorm-forward example * Add support of elementwise operation for batchnorm forward output * Reduce object copying by passing object as reference type * Tiny change for performance * Updates for performance again * Some Renamings * Add GetActualVariance template parameter for ThreadwiseWelfordMerge * Tiny update in reference batchnorm forward nhwc/c * Move batchnorm multiblock kernel files to grid/batchnorm_multiblock sub-directory * Fuse mean and bias in the normalization calculation Co-authored-by: root <root@dc-smc-18.amd.com> Co-authored-by: rocking5566 <ChunYu.Lai@amd.com>
Instructions for batchnorm nhwc Example
Run batchnorm forward nhwc
# -D <xxx> : input 4-d tensor lengths
# -v <x> : verification (0=no, 1=yes)
#arg1: data type (0: fp16, 1: fp32, 3: int8, 5: bp16, 6: fp64)
#arg2: 1/0 to indicate whether to update the moving average and variance (0=no, 1=yes)
#arg3: 1/0 to indicate whether to save result mean/invVariance (0=no, 1=yes)
#arg4: initialization (0=no init, 1=single integer value, 2=scope integer value, 3=decimal value)
#arg5: time kernel (0=no, 1=yes)
./bin/example_batchnorm_forward -D 128,16,16,1024 -v 1 0 0 1 2 1
Result
./bin/example_batchnorm_forward -D 128,16,16,1024 -v 1 0 0 1 2 1
launch_and_time_kernel: grid_dim {64, 1, 1}, block_dim {256, 1, 1}
Warm up 1 time
Start running 10 times...
launch_and_time_kernel: grid_dim {120, 1, 1}, block_dim {256, 1, 1}
Warm up 1 time
Start running 10 times...
launch_and_time_kernel: grid_dim {120, 1, 1}, block_dim {256, 1, 1}
Warm up 1 time
Start running 10 times...
Perf: 2.08231 ms, 354.519 GB/s
Result
./bin/example_batchnorm_forward -D 128,16,16,1024 -v 1 0 1 0 2 0
echo $?
0
Run batchnorm infer nhwc
# -D <xxx> : input 4-d tensor lengths
# -v <x> : verification (0=no, 1=yes)
#arg1: data type (0: fp16, 1: fp32, 3: int8, 5: bp16, 6: 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_batchnorm_infer -D 128,16,16,1024 -v 1 0 2 1
Result
./bin/example_batchnorm_infer -D 128,16,16,1024 -v 1 0 2 1
launch_and_time_kernel: grid_dim {120, 1, 1}, block_dim {256, 1, 1}
Warm up 1 time
Start running 10 times...
Perf: 1.28235 ms, 523.329 GB/s