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Add rmsnorm small example
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rocking
185
example/ck_tile/06_rmsnorm2d/example_rmsnorm2d_fwd.cpp
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185
example/ck_tile/06_rmsnorm2d/example_rmsnorm2d_fwd.cpp
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#include "ck_tile/host.hpp"
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#include "ck_tile/core.hpp"
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#include "ck_tile/host/kernel_launch.hpp"
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#include "ck_tile/ops/rmsnorm2d.hpp"
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#include <cstring>
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// different threshold for different dtype
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template <typename DataType>
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auto get_elimit()
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{
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double rtol = 1e-2;
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double atol = 1e-2;
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return ck_tile::make_tuple(rtol, atol);
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}
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template <>
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auto get_elimit<ck_tile::bf16_t>()
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{
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double rtol = 1e-2;
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double atol = 1e-2;
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return ck_tile::make_tuple(rtol, atol);
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}
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auto create_args(int argc, char* argv[])
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{
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ck_tile::ArgParser arg_parser;
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arg_parser.insert("m", "3328", "m dimension")
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.insert("n", "4096", "n dimension")
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.insert("stride", "-1", "stride per row, if -1 then equal to n")
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.insert("e", "1e-5", "epsilon")
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.insert("v", "1", "cpu validation or not")
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.insert("kname", "1", "print kernel name or not")
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.insert("prec", "fp16", "precision")
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.insert("warmup", "5", "cold iter")
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.insert("repeat", "20", "hot iter");
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bool result = arg_parser.parse(argc, argv);
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return std::make_tuple(result, arg_parser);
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}
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template <typename DataType>
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bool run(const ck_tile::ArgParser& arg_parser)
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{
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ck_tile::index_t m = arg_parser.get_int("m");
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ck_tile::index_t n = arg_parser.get_int("n");
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ck_tile::index_t stride = arg_parser.get_int("stride");
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if(stride < 0)
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stride = n;
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float epsilon = arg_parser.get_float("e");
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std::string data_type = arg_parser.get_str("prec");
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int kname = arg_parser.get_int("kname");
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int do_validation = arg_parser.get_int("v");
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int warmup = arg_parser.get_int("warmup");
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int repeat = arg_parser.get_int("repeat");
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assert(stride >= n);
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using XDataType = DataType;
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using YDataType = DataType;
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using GammaDataType = DataType;
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using InvRmsDataType = ck_tile::null_type;
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using ComputeDataType = float;
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// host verify
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ck_tile::HostTensor<XDataType> x_host({m, n}, {stride, 1});
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ck_tile::HostTensor<GammaDataType> gamma_host({n});
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ck_tile::HostTensor<YDataType> y_host_ref({m, n}, {stride, 1});
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ck_tile::HostTensor<YDataType> y_host_dev({m, n}, {stride, 1});
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ck_tile::HostTensor<InvRmsDataType> invRms_host_ref({m});
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ck_tile::FillUniformDistribution<XDataType>{-.5f, .5f}(x_host);
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ck_tile::FillUniformDistribution<GammaDataType>{-.5f, .5f}(gamma_host);
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ck_tile::DeviceMem x_buf(x_host.get_element_space_size_in_bytes());
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ck_tile::DeviceMem gamma_buf(gamma_host.get_element_space_size_in_bytes());
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ck_tile::DeviceMem y_buf(y_host_dev.get_element_space_size_in_bytes());
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x_buf.ToDevice(x_host.data());
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gamma_buf.ToDevice(gamma_host.data());
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std::cout << "[" << data_type << "]"
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<< " m:" << m << ", n:" << n << ", stride:" << stride << std::flush;
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constexpr bool kTwoPass = true;
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using BlockWarps = ck_tile::sequence<4, 1>;
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using BlockTile = ck_tile::sequence<8, 64>;
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using WarpTile = ck_tile::sequence<2, 64>;
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using Vector = ck_tile::sequence<1, 2>;
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using Shape = ck_tile::Rmsnorm2dShape<BlockTile, BlockWarps, WarpTile, Vector>;
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using Problem = ck_tile::Rmsnorm2dFwdPipelineProblem<XDataType,
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GammaDataType,
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ComputeDataType,
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YDataType,
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InvRmsDataType,
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Shape,
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false, // kPadN
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false, // kSaveInvRms
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kTwoPass>;
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using OnePassPipeline = ck_tile::Rmsnorm2dFwdPipelineOnePass<Problem>;
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using TwoPassPipeline = ck_tile::Rmsnorm2dFwdPipelineTwoPass<Problem>;
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using Pipeline = std::conditional_t<kTwoPass, TwoPassPipeline, OnePassPipeline>;
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using Kernel = ck_tile::Rmsnorm2dFwd<Pipeline>;
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ck_tile::Rmsnorm2dFwdHostArgs args{x_buf.GetDeviceBuffer(),
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gamma_buf.GetDeviceBuffer(),
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y_buf.GetDeviceBuffer(),
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nullptr,
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epsilon,
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m,
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n,
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stride};
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auto kargs = Kernel::MakeKargs(args);
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const dim3 grids = Kernel::GridSize(args);
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constexpr dim3 blocks = Kernel::BlockSize();
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constexpr ck_tile::index_t kBlockPerCu = 1;
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auto s = ck_tile::stream_config{nullptr, true, kname ? 1 : 0, warmup, repeat};
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ck_tile::launch_kernel(
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s, ck_tile::make_kernel<blocks.x, kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
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bool pass = true;
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if(do_validation)
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{
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// reference
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ck_tile::reference_rmsnorm2d_fwd<XDataType,
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GammaDataType,
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ComputeDataType,
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YDataType,
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InvRmsDataType>(
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x_host, gamma_host, y_host_ref, invRms_host_ref, epsilon);
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y_buf.FromDevice(y_host_dev.data());
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auto [rtol, atol] = get_elimit<DataType>();
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if(stride == n)
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{
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pass = ck_tile::check_err(
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y_host_dev, y_host_ref, std::string("OUT Error: Incorrect results!"), rtol, atol);
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}
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else
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{
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for(int i_r = 0; i_r < m; i_r++)
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{
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std::vector<YDataType> y_host_dev_row(y_host_dev.begin() + i_r * stride,
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y_host_dev.begin() + i_r * stride + n);
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std::vector<YDataType> y_host_ref_row(y_host_ref.begin() + i_r * stride,
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y_host_ref.begin() + i_r * stride + n);
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pass &= ck_tile::check_err(y_host_dev_row,
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y_host_ref_row,
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std::string("OUT[") + std::to_string(i_r) +
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std::string("] Error: Incorrect results!"),
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rtol,
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atol);
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}
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}
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std::cout << ", valid:" << (pass ? "y" : "n") << std::flush << std::endl;
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}
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return pass;
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}
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int main(int argc, char* argv[])
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{
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auto [result, arg_parser] = create_args(argc, argv);
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if(!result)
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return -1;
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const std::string data_type = arg_parser.get_str("prec");
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if(data_type == "fp16")
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{
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return run<ck_tile::half_t>(arg_parser) ? 0 : -2;
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}
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return -3;
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}
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