mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-03 13:11:25 +00:00
ef6c28e989
* add aiter tests and move json_dump header * remove example/include path from cmake * extend time for aiter and pytorch stages
242 lines
8.9 KiB
C++
242 lines
8.9 KiB
C++
#include "ck_tile/host.hpp"
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#include "smoothquant.hpp"
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#include "ck_tile/utility/json_dump.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("x_stride", "-1", "input stride per row, if -1 then equal to n")
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.insert("y_stride", "-1", "output stride per row, if -1 then equal to n")
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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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.insert("json", "0", "0: No Json, 1: Dump Results in Json format")
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.insert("jsonfile", "smoothquant.json", "json file name to dump results");
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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 x_stride = arg_parser.get_int("x_stride");
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if(x_stride < 0)
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x_stride = n;
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ck_tile::index_t y_stride = arg_parser.get_int("y_stride");
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if(y_stride < 0)
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y_stride = n;
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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(x_stride >= n);
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using TypeConfig = SmoothquantTypeConfig<DataType>;
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using XDataType = typename TypeConfig::XDataType;
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using SmoothScaleDataType = typename TypeConfig::SmoothScaleDataType;
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using YScaleDataType = typename TypeConfig::YScaleDataType;
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using QYDataType = typename TypeConfig::QYDataType;
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using ComputeDataType = typename TypeConfig::ComputeDataType;
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// host verify
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ck_tile::HostTensor<XDataType> x_host({m, n}, {x_stride, 1});
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ck_tile::HostTensor<SmoothScaleDataType> smscale_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}, {y_stride, 1});
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ck_tile::HostTensor<QYDataType> qy_host_dev({m, n}, {y_stride, 1});
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ck_tile::FillUniformDistribution<XDataType>{-.5f, .5f}(x_host);
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ck_tile::FillUniformDistribution<SmoothScaleDataType>{1e-3, .5f}(smscale_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 smscale_buf(smscale_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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smscale_buf.ToDevice(smscale_host.data());
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std::cout << "[" << data_type << "]" << " m:" << m << ", n:" << n << ", x_stride:" << x_stride
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<< ", y_stride:" << y_stride << std::flush;
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smoothquant_traits traits{data_type};
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smoothquant_args args{x_buf.GetDeviceBuffer(),
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smscale_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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x_stride,
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y_stride};
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float ave_time = smoothquant(
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traits, args, ck_tile::stream_config{nullptr, true, kname ? 1 : 0, warmup, repeat});
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std::size_t num_byte = sizeof(XDataType) * m * n + sizeof(SmoothScaleDataType) * n +
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sizeof(YScaleDataType) * m + sizeof(QYDataType) * m * n;
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float gb_per_sec = num_byte / 1.E6 / ave_time;
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std::cout << ", " << ave_time * 1.E3 << " us, " << gb_per_sec << " GB/s" << std::flush;
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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}, {y_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_smscale = ck_tile::type_convert<ComputeDataType>(smscale_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_smscale;
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}
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};
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ck_tile::make_ParallelTensorFunctor(f, smscale_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(y_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 * y_stride,
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qy_host_dev.begin() + i_r * y_stride +
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n);
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std::vector<QYDataType> qy_host_ref_row(qy_host_ref.begin() + i_r * y_stride,
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qy_host_ref.begin() + i_r * y_stride +
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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 << ", valid:" << (pass ? "y" : "n") << std::flush << std::endl;
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}
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if(arg_parser.get_int("json") == 1)
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{
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dump_smoothquant_json(arg_parser.get_str("jsonfile"),
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data_type,
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m,
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n,
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x_stride,
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y_stride,
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ave_time,
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0,
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gb_per_sec,
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pass);
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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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