mirror of
https://github.com/ROCm/composable_kernel.git
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0ffe956ab1
* [What] Rename the example [Why] Prepare to add unary reduction * Add global oparation to the parameter * Add atomicmax * Fix compile error * Support atomicMax (hip library) * Rename the reduction example * Fix target name * use p_d1_grid as the indicator directly * Prevent performance issue. Let passthrough handle it. * Implement the function template the specialize the float2 * No need to separate into two lines * Remove empty line * add comment * Fix compile error due to merge from develop * make the implementation of atomic_max / atomic_add explicit for each datatype * Refine typo * For future CI test * Fix compiler error in ckProfiler * Merge commit 'de2769e3a6695b38a20529261273ddc5cdaab2fe' * simply use remove_pointer * Rename type and var * Refine example * Modify reducemax example * Fix bug in reduction * Change initialize range * Implement F64 version of atomicMax * Move reduction code together * Add buffer atomic_max * Fix coding style by clang-format * Integrate new api of DeviceGemmReduce_Xdl_CShuffle * Integrate Batch gemm reduction * Fix example * fix example * clean up * Fix batch gemm tensor operation * Fix coding style * Fix template augument * Fix clang format * Keep flexible of different stride for each D tensor * Fix compile error for ckProfiler * Fix typo * [What] Fix naming [Why] Prepare to add out elementop * Add DoutElementOp Co-authored-by: Chao Liu <chao.liu2@amd.com> Co-authored-by: rocking <chunylai@amd.com>
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