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https://github.com/ROCm/composable_kernel.git
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36c7ce4e0e
* update MOCK_ID for moe-sorting * add moe-smoothquant * update a comment * fix format * hot fix * update topk in overflow case * update comments * update bf16 cvt --------- Co-authored-by: valarLip <340077269@qq.com>
265 lines
10 KiB
C++
265 lines
10 KiB
C++
#include "ck_tile/host.hpp"
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#include "moe_smoothquant.hpp"
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#include <cstring>
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#include <set>
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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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template <typename IndexType>
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void topid_unique_gen(
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std::vector<IndexType>& host_tensor, int tokens, int topk, int num_expert, int seed)
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{
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size_t total_size = topk * tokens;
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std::srand(seed);
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std::set<IndexType> unique_set;
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IndexType current_v;
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for(size_t i = 0; i < total_size; i++)
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{
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if(i % topk == 0)
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{
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unique_set.clear();
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}
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current_v = std::rand() % num_expert;
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while(unique_set.find(current_v) != unique_set.end())
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{
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current_v = std::rand() % num_expert;
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}
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unique_set.insert(current_v);
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host_tensor[i] = current_v;
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}
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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("t", "3328", "tokens dimension")
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.insert("h", "4096", "hidden_size dimension")
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.insert("e", "32", "experts")
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.insert("k", "5", "topk")
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.insert("stride", "-1", "stride per row, if -1 then equal to hidden_size")
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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 tokens = arg_parser.get_int("t");
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ck_tile::index_t hidden_size = arg_parser.get_int("h");
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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 = hidden_size;
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ck_tile::index_t experts = arg_parser.get_int("e");
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ck_tile::index_t topk = arg_parser.get_int("k");
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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 >= hidden_size);
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using TypeConfig = MoeSmoothquantTypeConfig<DataType>;
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using XDataType = typename TypeConfig::XDataType;
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using XScaleDataType = typename TypeConfig::XScaleDataType;
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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({tokens, hidden_size}, {stride, 1});
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ck_tile::HostTensor<XScaleDataType> xscale_host({experts * hidden_size});
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ck_tile::HostTensor<ck_tile::index_t> topk_ids_host({tokens, topk});
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ck_tile::HostTensor<YScaleDataType> yscale_host_ref({topk * tokens}, {1});
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ck_tile::HostTensor<YScaleDataType> yscale_host_dev({topk * tokens}, {1});
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ck_tile::HostTensor<QYDataType> qy_host_ref({topk * tokens, hidden_size}, {stride, 1});
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ck_tile::HostTensor<QYDataType> qy_host_dev({topk * tokens, hidden_size}, {stride, 1});
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topid_unique_gen<ck_tile::index_t>(topk_ids_host.mData, tokens, topk, experts, 11937);
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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 topk_ids_buf(topk_ids_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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topk_ids_buf.ToDevice(topk_ids_host.data());
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std::cout << "[" << data_type << "]"
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<< " tokens:" << tokens << ", hidden_size:" << hidden_size << ", stride:" << stride
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<< ", experts:" << experts << ", topk:" << topk << std::flush;
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moe_smoothquant_traits traits{data_type};
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moe_smoothquant_args args{x_buf.GetDeviceBuffer(),
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xscale_buf.GetDeviceBuffer(),
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topk_ids_buf.GetDeviceBuffer(),
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yscale_buf.GetDeviceBuffer(),
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qy_buf.GetDeviceBuffer(),
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tokens,
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hidden_size,
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experts,
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topk,
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stride,
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stride};
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float ave_time = moe_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 =
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sizeof(XDataType) * tokens * hidden_size + sizeof(XScaleDataType) * topk * hidden_size +
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sizeof(YScaleDataType) * topk * tokens + sizeof(QYDataType) * topk * tokens * hidden_size;
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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({topk * tokens, hidden_size}, {stride, 1});
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// smooth outlier
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{
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auto f = [&](auto i_token) {
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for(int i_topk = 0; i_topk < topk; i_topk++)
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{
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auto i_expert = topk_ids_host(i_token, i_topk);
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for(int i_h = 0; i_h < hidden_size; ++i_h)
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{
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auto v_xscale = ck_tile::type_convert<ComputeDataType>(
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xscale_host(i_expert * hidden_size + i_h));
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auto v_x = ck_tile::type_convert<ComputeDataType>(x_host(i_token, i_h));
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// y_host(i_token * topk + i_topk, i_h) = v_x * v_xscale;
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y_host(i_topk * tokens + i_token, i_h) = v_x * v_xscale;
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}
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}
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};
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ck_tile::make_ParallelTensorFunctor(f, tokens)(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({topk * tokens});
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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 == hidden_size)
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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 < topk * tokens; 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 +
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hidden_size);
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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 +
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hidden_size);
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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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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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