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https://github.com/ROCm/composable_kernel.git
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7f21662089
* Implement layernorm kernel and deviceOp * verify gpu kernel with host code * 1. Separate gamma aand beta from affine 2. Check if argument is valid * clean * Sync the naming * Support sweep once mode if we can put k dimension data inside one block * [What] Get length from upper length. [Why] if we get length directly, we may get length after padding. * We only use one block in K dimension. Hence, we can simplify the indexing of global R/W. * Use 1d descriptor for gamma and beta * Add accElementwiseOp * Extract layernorm host code * Support different YVectorDim in GridwiseLayernorm * Rename XSrcVectorDim to XYSrcVectorDim. Because we use same parameter in deviceOp * Gamma and beta can share the VGPR. * Add test for fp32 and fp16 * Fix bug of concurrency and add test case which may fail orignally * Propagate NaN for layernorm Co-authored-by: Chao Liu <chao.liu2@amd.com>
Instructions for example_softmax_blockwise
Run example_softmax_blockwise
# -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>