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https://github.com/ROCm/composable_kernel.git
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[Ck_tile] smoothquant (#1617)
* fix compile error * fix typo of padding * Add smoothquant op * Add smoothquant instance library * refine type * add test script * Re-generate smoothquant.hpp * Always use 'current year' in copyright * use Generic2dBlockShape instead * Add vector = 8 instance back * Find exe path automatically * Simplify the api condition * Remove debugging code * update year * Add blank line between function declaration * explicitly cast return value to dim3 * refine return value * Fix default warmup and repeat value * Add comment * refactor sommthquant cmake * Add README * Fix typo --------- Co-authored-by: Po Yen, Chen <PoYen.Chen@amd.com>
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237
example/ck_tile/12_smoothquant/example_smoothquant.cpp
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237
example/ck_tile/12_smoothquant/example_smoothquant.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/smoothquant.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-5;
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double atol = 1e-5;
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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-5;
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double atol = 1e-5;
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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::int8_t>()
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{
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// due to rounding, int8 quantization might have 1 abs error
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double rtol = 1;
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double atol = 1;
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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("prec", "fp16", "precision")
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.insert("warmup", "0", "cold iter")
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.insert("repeat", "1", "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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std::string data_type = arg_parser.get_str("prec");
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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 XScaleDataType = float;
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using YScaleDataType = float;
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using QYDataType = ck_tile::int8_t;
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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<XScaleDataType> xscale_host({n});
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ck_tile::HostTensor<YScaleDataType> yscale_host_ref({m}, {1});
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ck_tile::HostTensor<YScaleDataType> yscale_host_dev({m}, {1});
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ck_tile::HostTensor<QYDataType> qy_host_ref({m, n}, {stride, 1});
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ck_tile::HostTensor<QYDataType> qy_host_dev({m, n}, {stride, 1});
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ck_tile::FillUniformDistribution<XDataType>{-.5f, .5f}(x_host);
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ck_tile::FillUniformDistribution<XScaleDataType>{1e-3, .5f}(xscale_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 xscale_buf(xscale_host.get_element_space_size_in_bytes());
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ck_tile::DeviceMem yscale_buf(yscale_host_dev.get_element_space_size_in_bytes());
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ck_tile::DeviceMem qy_buf(qy_host_dev.get_element_space_size_in_bytes());
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x_buf.ToDevice(x_host.data());
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xscale_buf.ToDevice(xscale_host.data());
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constexpr bool kTwoPass = true;
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using BlockWarps = ck_tile::sequence<2, 2>;
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using BlockTile = ck_tile::sequence<2, 128>;
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using WarpTile = ck_tile::sequence<1, 64>;
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using Vector = ck_tile::sequence<1, 1>;
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using Shape = ck_tile::Generic2dBlockShape<BlockTile, BlockWarps, WarpTile, Vector>;
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using Problem = ck_tile::SmoothquantPipelineProblem<XDataType,
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XScaleDataType,
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ComputeDataType,
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YScaleDataType,
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QYDataType,
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Shape,
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true,
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kTwoPass>;
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using OnePassPipeline = ck_tile::SmoothquantPipelineOnePass<Problem>;
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using TwoPassPipeline = ck_tile::SmoothquantPipelineTwoPass<Problem>;
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using Pipeline = std::conditional_t<kTwoPass, TwoPassPipeline, OnePassPipeline>;
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using Kernel = ck_tile::Smoothquant<Pipeline>;
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ck_tile::SmoothquantHostArgs args{x_buf.GetDeviceBuffer(),
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xscale_buf.GetDeviceBuffer(),
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yscale_buf.GetDeviceBuffer(),
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qy_buf.GetDeviceBuffer(),
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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, 1, 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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using YDataType = ComputeDataType;
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ck_tile::HostTensor<ComputeDataType> y_host({m, n}, {stride, 1});
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// smooth outlier
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{
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auto f = [&](auto n_) {
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auto v_xscale = ck_tile::type_convert<ComputeDataType>(xscale_host(n_));
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for(int m_ = 0; m_ < m; ++m_)
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{
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auto v_x = ck_tile::type_convert<ComputeDataType>(x_host(m_, n_));
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y_host(m_, n_) = v_x * v_xscale;
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}
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};
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ck_tile::make_ParallelTensorFunctor(f, xscale_host.get_element_space_size())(
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std::thread::hardware_concurrency());
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}
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// yscale
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{
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ck_tile::HostTensor<YDataType> y_rowwise_amax_host({m});
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using ReduceAmax = ck_tile::ReduceOp::AbsMax;
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ck_tile::reference_reduce<ComputeDataType, ComputeDataType, YDataType>(
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y_host, y_rowwise_amax_host, ReduceAmax{});
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auto op = [](const auto& v0) {
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return v0 /
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ck_tile::type_convert<ComputeDataType>(ck_tile::numeric<QYDataType>::max());
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};
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ck_tile::reference_unary_elementwise<YDataType, YScaleDataType, ComputeDataType>(
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y_rowwise_amax_host, yscale_host_ref, op);
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yscale_buf.FromDevice(yscale_host_dev.mData.data());
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auto [rtol, atol] = get_elimit<YScaleDataType>();
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pass &= ck_tile::check_err(yscale_host_dev,
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yscale_host_ref,
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std::string("yscale Error: Incorrect results!"),
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rtol,
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atol);
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}
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// rowwise quantization
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{
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ck_tile::reference_rowwise_quantization2d<YDataType, YScaleDataType, QYDataType>(
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y_host, yscale_host_ref, qy_host_ref);
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qy_buf.FromDevice(qy_host_dev.data());
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auto [rtol, atol] = get_elimit<QYDataType>();
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if(stride == n)
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{
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pass = ck_tile::check_err(qy_host_dev,
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qy_host_ref,
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std::string("qy Error: Incorrect results!"),
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rtol,
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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<QYDataType> qy_host_dev_row(qy_host_dev.begin() + i_r * stride,
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qy_host_dev.begin() + i_r * stride + n);
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std::vector<QYDataType> qy_host_ref_row(qy_host_ref.begin() + i_r * stride,
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qy_host_ref.begin() + i_r * stride + n);
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pass &= ck_tile::check_err(qy_host_dev_row,
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qy_host_ref_row,
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std::string("qy[") + 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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}
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std::cout << "[" << data_type << "]"
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<< " m:" << m << ", n:" << n << ", stride:" << stride
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<< ", 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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/*else if(data_type == "bf16")
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{
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return run<ck_tile::bf16_t>(arg_parser) ? 0 : -2;
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}*/
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return -3;
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}
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