mirror of
https://github.com/ROCm/composable_kernel.git
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95f225d7bc
* refactor
* start
* add device gemm file
* add BatchStrideD0
* add stridd0
* add gridwise file
* add d0 parameters to gridwise gemm
* add c layout transformer
* add d0 threadwise copy
* init kernel
* init kernel
* regular code
* nm desc put to out
* kernel parameter can not use reference
* host add bias+gelu
* run right for bias+gelu
* change AddFastGelu into another file
* interface add d1 bias parameters
* add d1 parameter to argument
* add d1 parameter to gridwise
* first all code,not verify
* gelu change to relu and GetElementSpaceSize bug
* add instance
* start add to ckprofiler
* ckprofiler finish code
* change input parameter for ckProfiler
* fix host bias+gelu bug
* show help for ckProfiler
* fix bug for lunch kernel ignore parametes
* add pad and fix about bug
* mutiple d0
* add dynamic d0_element_op
* change profiler and instance to mutiple d0
* example have 2 d0
* remove some comments not using
* change 2 d0 have self parameters
* change d element_op name
* change class name(multiple_d)
* fix bug
* fix bug that don't find file
* update profiler
* refactor
* update profiler
* clean
* revert example change
* add gon layout
* optimize parameter for gno
* add gon to gemm+gemm
* change helping input parameters
* change to GemmPadder_v2
* using ForEach
* fix gb_per_sec
Co-authored-by: Chao Liu <lc.roy86@gmail.com>
Co-authored-by: ltqin <letaoqin@amd.com>
[ROCm/composable_kernel commit: 370efa6c08]
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