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https://github.com/ROCm/composable_kernel.git
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ba4d3575b2
* add fused addition lyernorm
* add fused addition lyernorm
* changed CMakelist
* removed annotates
* modified descriptor of C
* fixed bug in gridwise add layernorm
* format the files
* modified name from add&layernorm into elementwise&layernorm
* created fused elementwise layernorm branch
* change input into tuple type
* add sweep once to reduce load & read of C from global memory
* modified Argument api
* modified way to malloc c in global memory
* changed gamma and beta to m_k_desc
* fixed bug when sweep once and move CDataType when define device level struct
* add src dim for gamma and beta
* implement optimization for coalesced
* delete a annotation line
* fixed some bug to meet the requirements of ck
* add bandwidth computing in example, and fixed the time unit
* move device_elementwise_layernorm_impl.hpp into device/impl
* fixed bug in device_elementwise_layernorm_impl.hpp
* changed name from layernorm into normalization
* clang-format the changed files
* changed the names
* moved immidiate results into lds, it become faster in non-sweeponce cases
* changed naming of C into X to make the defination more clear
* changed naming in example
* add tests for elementwise normalization
* move example_elementwise_layernorm_blockwise into folder 44_elementwise_normalization
* move test_elementwise_layernorm_fp16 into new folder
* move elementwise_normalization_instances into a new folder
* add more tests in test_elementwise_layernorm_fp16.cpp
* added some corner cases in test
* fixed method to compute lds size for matrix X
* changed name of 44_elementwise_normalization into 45_elementwise_normalization
* modified some comments
* modified some other confused comments
* reduce redundant tests in test_elementwise_layernorm_fp16.cpp
[ROCm/composable_kernel commit: efbcc6eddc]
Profile GEMM kernels
#arg1: tensor operation (gemm=GEMM)
#arg2: data type (0=fp32, 1=fp16)
#arg3: matrix layout (0=NN, 1=NT, 2=TN, 3=TT)
#arg4: verification (0=no, 1=yes)
#arg5: initialization (0=no init, 1=integer value, 2=decimal value)
#arg6: print matrix value (0=no, 1=yes)
#arg7: run kernel # of times (>1)
#arg8 to 13: M, N, K, StrideA, StrideB, StrideC
################ op datatype layout verify init log repeat M___ N___ K___ StrideA StrideB StrideC
./bin/ckProfiler gemm 1 1 1 1 0 5 3840 4096 4096 4096 4096 4096
Result (MI100 @ 1087Mhz, 133.5TFlops peak FP16)
a_m_k: dim 2, lengths {3840, 4096}, strides {4096, 1}
b_k_n: dim 2, lengths {4096, 4096}, strides {1, 4096}
c_m_n: dim 2, lengths {3840, 4096}, strides {4096, 1}
....
Best Perf: 1.1933 ms, 107.977 TFlops, 79.0848 GB/s
Profile 2d forward convolution kernels
#arg1: tensor operation (conv=Convolution)
#arg2: data type (0=fp32, 1=fp16)
#arg3: input tensor layout (0=NCHW, 1=NHWC)
#arg4: weight tensor layout (0=KCYX, 1=KYXC)
#arg5: output tensor layout (0=NKHW, 1=NHWK)
#arg6: verification (0=no, 1=yes)
#arg7: initialization (0=no init, 1=integer value, 2=decimal value)
#arg8: print matrix value (0=no, 1=yes)
#arg9: run kernel # of times (>1)
#arg10 to 24: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, RightPx
################ op datatype in_layout wei_layout out_layout verify init log repeat N__ K___ C___ Y X Hi__ Wi__ Strides Dilations LeftPads RightPads
./bin/ckProfiler conv2d_fwd 1 1 1 1 1 1 0 5 128 256 192 3 3 71 71 2 2 1 1 1 1 1 1
Result (MI100 @ 1087Mhz, 133.5TFlops peak FP16)
in_n_c_hi_wi: dim 4, lengths {128, 192, 71, 71}, strides {967872, 1, 13632, 192}
wei_k_c_y_x: dim 4, lengths {256, 192, 3, 3}, strides {1728, 1, 576, 192}
out_n_k_ho_wo: dim 4, lengths {128, 256, 36, 36}, strides {331776, 1, 9216, 256}
....
Best Perf: 1.42509 ms, 102.988 TFlops, 234.086 GB/s