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abcca1dd45
* UniforFill with integer values. * Log tested instance type string. * Add UT for all convolution specializations. * debugging conv * Fix dangling reference bug. * Small refinements. * Fix call to error checking function. * Small refinements to tests. * Configure error tolerance * Change problem size. * Remove OddC case from types that do not support it. * Add helper traits for AccumulatorDataType. * Print first 5 errs in check_err for integral types. * Rename FillUniform to FillUniformDistribution * Refactor * Do not use typed tests. * Instead use plain fixture class with templatized member functions. * Initialize tensors with integer values. * Refine test instances. * Properly set accumulator data type. * Add another "big" instance. * Refactor convolution tests. * Revert "debugging conv" This reverts commitb109516455. * Add pragma once + format + small refinement. * Fix some unwanted changes. * Clang-format * Fix profile_convnd to use renamed tensor initializer. * Add instances for ConvFWDND kernel case 2D * Helpers to get ConvNDFwd 2D instances. * Refactoring. * Remove "small block" instance as it was generating compiler errors. * Remove default template parameters values. * Refine and fix test. * Fix problem with default template parameter types. * Adjust error thresholds for floating point values test. * Use integer values initialization for instances test. * Add tests for ConvNDFwd 2D case. * Remove AccumulatorDataType type trait. * Update unit-tests. * Remove operator<< overload. * Unlock conv1d/3d nd fwd instances. * Enable skipping calculating reference using flag. * Fix number of channels for first ResNet50 layer. * Clang-format. Co-authored-by: Adam Osewski <aosewski@amd.com> Co-authored-by: Chao Liu <chao.liu2@amd.com> [ROCm/composable_kernel commit:a2edd7d802]
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