ff4df5c158
* Adding RapidJson Library
* Adding Json Dumps in all CK_Tile Examples
Not verified yet
* Adding json to cktile Batched Transpose
* adding json dumps to layernorm2d_fwd
* Adding json dump to flatmm_basic
* Adding RapidJson Library
* Adding Json Dumps in all CK_Tile Examples
Not verified yet
* Adding json to cktile Batched Transpose
* adding json dumps to layernorm2d_fwd
* Adding json dump to flatmm_basic
* Adding json in 03_gemm
* Add json dump to 16_batched_gemm
* Add json dump to gemm_multi_d_fp16
* Add json dump to grouped_gemm
* fix fmha_bwd/fwd
* Fix clang-format errors
exclude include/rapidjson in jenkins as its a third-party library
* Saparating function and defination.
* Update Documentation of 03_gemm
* Refactoring as per code review
* Disable fp8 instances on unsupported targets (#2592)
* Restrict building of gemm_universal_preshuffle_f8 instances to specific targets in CMakeLists.txt
* Add condition to skip gemm_xdl_universal_preshuffle_f8 instances for unsupported targets in CMakeLists.txt
* Add conditions to skip unsupported targets for gemm_universal_preshuffle_f8 and gemm_xdl_universal_preshuffle_f8 instances in CMakeLists.txt
* Refine conditions to exclude gemm_universal_preshuffle_f8 instances for unsupported targets in CMakeLists.txt
---------
Co-authored-by: AviralGoelAMD <aviralgoel@amd.com>
* fix clang format
* remove duplicate lines of code from library/src/tensor_operation_instance/gpu/CMakeLists.txt
* Fixing Readme and unifying jsondumps
* adding moe_smoothquant
* adding fused_moe
* Fixing Readme for batched_gemm
* Fixing Readme for grouped_gemm
* adding flatmm
* adding gemm_multi_d_fp16
* adding elementwise
* adding File name when json is dumped
* Fixing Reduce after merge
* adding batched_transpose
* Adding Warptile in Gemm
* Fixing Clang Format
---------
Co-authored-by: Aviral Goel <aviral.goel@amd.com>
Co-authored-by: AviralGoelAMD <aviralgoel@amd.com>
Co-authored-by: illsilin_amdeng <Illia.Silin@amd.com>
[ROCm/composable_kernel commit: 4d041837ad]
permute
This folder contains example for permute kernel, which is similiar to torch.permute (combined with torch.contiguous). Currently we implement a generic permute kernel that support up to rank 8 arbitrary permutation with a single kernel instance. Performance is not the first consideration, we prefer a simple and general kernel implementation using ck_tile in this example.
args:
-v weather do CPU validation or not (default:1)
-prec data type. fp16/bf16/fp32 (default:fp16)
-shape the shape of the input tensor (default:2,3,4)
-perm permute perm (default:2,1,0)
build
# in the root of ck_tile
mkdir build && cd build
../script/cmake-ck-dev.sh ../ <arch> # you can replace this <arch> to gfx90a, gfx942...
make tile_example_permute -j
This will result in an executable build/bin/tile_example_permute
some examples
# torch
x=torch.randn(2,3,4,6)
y=x.permute(0,3,2,1).contiguous()
# ck_tile
./build/bin/tile_example_permute -shape=2,3,4,6 -perm=0,3,2,1
or you can try the smoke_test
# in the root of ck_tile, after you build this example
sh example/ck_tile/06_permute/script/smoke_test.sh
alternative implementation
we have an alternative implementation under alternative_impl/ folder, that can swizzle the tensor to be more friendly for data loading for matrix core layout. This can be enabled when dealing with a rank-7 tensor, with a fixed pattern of either 0,1,4,2,5,3,6 or 0,1,2,4,5,3,6. There are other shape limitation of this implementation, check the source code of permute.cpp for detail.
# example
./build/bin/tile_example_permute -shape=3,6,4,32,16,2,8 -perm=0,1,4,2,5,3,6 # b_n0_k0_n1_k1_n2_k2
./build/bin/tile_example_permute -shape=3,8,4,16,16,4,8 -perm=0,1,2,4,5,3,6 # b_n0_n1_k0_k1_n2_k2