[CK_TILE]Moe update index (#1672)

* 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>

example/ck_tile/14_moe_smoothquant/instances/moe_smoothquant_fp16_n64_n128_instance.cpp

+
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "moe_smoothquant_instance_common.hpp"
+
+// clang-format off
+//                                                  rm  rn  tm  tn  vn  pd      2p
+template float moe_smoothquant_<trait_<ck_tile::fp16_t, 1,  1,  4,  64, 1,  true, false>>(const S&, A);
+template float moe_smoothquant_<trait_<ck_tile::fp16_t, 1,  1,  4,  64, 2,  true, false>>(const S&, A);
+template float moe_smoothquant_<trait_<ck_tile::fp16_t, 1,  2,  4,  64, 1,  true, false>>(const S&, A);
+// clang-format on

example/ck_tile/14_moe_smoothquant/instances/moe_smoothquant_fp16_n768_instance.cpp

+
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "moe_smoothquant_instance_common.hpp"
+
+// clang-format off
+//                                                  rm  rn  tm  tn  vn  pd       2p
+template float moe_smoothquant_<trait_<ck_tile::fp16_t, 1,  3,  4,  64, 4,  true , false>>(const S&, A);
+template float moe_smoothquant_<trait_<ck_tile::fp16_t, 1,  6,  4,  64, 2,  true , false>>(const S&, A);
+template float moe_smoothquant_<trait_<ck_tile::fp16_t, 1, 12,  4,  64, 1,  true , false>>(const S&, A);
+// clang-format on

example/ck_tile/14_moe_smoothquant/instances/moe_smoothquant_fwd_api.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <ck_tile/core.hpp>
+#include "moe_smoothquant.hpp"
+
+template <typename DataType_,
+          ck_tile::index_t Repeat_M_,         // each thread repeat along M
+          ck_tile::index_t Repeat_N_,         // each thread repeat along N
+          ck_tile::index_t ThreadPerBlock_M_, // num threads along M
+          ck_tile::index_t ThreadPerBlock_N_, // num threads along N
+          ck_tile::index_t Vector_N_,         // vector size along N
+          bool kPadN_,
+          bool kTwoPass_>
+using trait_ = moe_smoothquant_traits_<DataType_,
+                                       Repeat_M_,
+                                       Repeat_N_,
+                                       ThreadPerBlock_M_,
+                                       ThreadPerBlock_N_,
+                                       Vector_N_,
+                                       kPadN_,
+                                       kTwoPass_>;
+
+template <typename data_type>
+float moe_smoothquant_dispatch(moe_smoothquant_traits /*t*/,
+                               moe_smoothquant_args a,
+                               const ck_tile::stream_config& s)
+{
+    float r = -1;
+    // clang-format off
+    //                                         rm  rn  tm  tn  vn   pd    2p
+    if(a.hidden_size <= 64) {
+            r = moe_smoothquant_<trait_<data_type, 1,  1,  4,  64, 1,  true, false>>(s, a);
+    }
+    else if(a.hidden_size <= 128) {
+        if (a.hidden_size % 2 == 0)
+            r = moe_smoothquant_<trait_<data_type, 1,  1,  4,  64, 2,  true, false>>(s, a);
+        else
+            r = moe_smoothquant_<trait_<data_type, 1,  2,  4,  64, 1,  true, false>>(s, a);
+    }
+    else if(a.hidden_size <= 256) {
+        if (a.hidden_size % 4 == 0)
+            r = moe_smoothquant_<trait_<data_type,  1, 1,  4,  64, 4,  true, false>>(s, a);
+        else if (a.hidden_size % 2 == 0)
+            r = moe_smoothquant_<trait_<data_type,  1, 2,  4,  64, 2,  true, false>>(s, a);
+        else
+            r = moe_smoothquant_<trait_<data_type,  1, 4,  4,  64, 1,  true, false>>(s, a);
+    }
+    else if(a.hidden_size <= 512) {
+        if (a.hidden_size % 8 == 0)
+            r = moe_smoothquant_<trait_<data_type,  1, 1,  4,  64, 8,  true, false>>(s, a);
+        else if (a.hidden_size % 4 == 0)
+            r = moe_smoothquant_<trait_<data_type,  1, 2,  4,  64, 4,  true, false>>(s, a);
+        else if (a.hidden_size % 2 == 0)
+            r = moe_smoothquant_<trait_<data_type,  1, 4,  4,  64, 2,  true, false>>(s, a);
+        else
+            r = moe_smoothquant_<trait_<data_type,  1, 8,  4,  64, 1,  true, false>>(s, a);
+    }
+    else if(a.hidden_size <= 768) {
+        if (a.hidden_size % 4 == 0)
+            r = moe_smoothquant_<trait_<data_type,  1, 3,  4,  64, 4,  true, false>>(s, a);
+        else if (a.hidden_size % 2 == 0)
+            r = moe_smoothquant_<trait_<data_type,  1, 6,  4,  64, 2,  true, false>>(s, a);
+        else
+            r = moe_smoothquant_<trait_<data_type,  1,12,  4,  64, 1,  true, false>>(s, a);
+    }
+    else if(a.hidden_size <= 1024) {
+        if (a.hidden_size % 8 == 0)
+            r = moe_smoothquant_<trait_<data_type,  1, 1, 2,  128, 8,  true, false>>(s, a);
+        else if (a.hidden_size % 4 == 0)
+            r = moe_smoothquant_<trait_<data_type,  1, 2, 2,  128, 4,  true, false>>(s, a);
+        else if (a.hidden_size % 2 == 0)
+            r = moe_smoothquant_<trait_<data_type,  1, 4, 2,  128, 2,  true, false>>(s, a);
+        else
+            r = moe_smoothquant_<trait_<data_type,  1, 4, 1,  256, 1,  true, false>>(s, a);
+    }
+    else if(a.hidden_size <= 1536) {
+        if (a.hidden_size % 8 == 0)
+            r = moe_smoothquant_<trait_<data_type,  1, 3, 4,   64, 8,  true, false>>(s, a);
+        else if (a.hidden_size % 4 == 0)
+            r = moe_smoothquant_<trait_<data_type,  1, 3, 2,  128, 4,  true, false>>(s, a);
+        else if (a.hidden_size % 2 == 0)
+            r = moe_smoothquant_<trait_<data_type,  1, 3, 1,  256, 2,  true, false>>(s, a);
+        else
+            r = moe_smoothquant_<trait_<data_type,  1, 6, 1,  256, 1,  true, false>>(s, a);
+    }
+    else if(a.hidden_size <= 2048) {
+        if (a.hidden_size % 8 == 0)
+            r = moe_smoothquant_<trait_<data_type,  1, 1, 1,  256, 8,  true, false>>(s, a);
+        else if (a.hidden_size % 4 == 0)
+            r = moe_smoothquant_<trait_<data_type,  1, 2, 1,  256, 4,  true, false>>(s, a);
+        else if (a.hidden_size % 2 == 0)
+            r = moe_smoothquant_<trait_<data_type,  1, 4, 1,  256, 2,  true, false>>(s, a);
+        else
+            r = moe_smoothquant_<trait_<data_type,  1, 8, 1,  256, 1,  true, false>>(s, a);
+    }
+    else if(a.hidden_size <= 3072) {
+        if (a.hidden_size % 8 == 0)
+            r = moe_smoothquant_<trait_<data_type,  1, 3, 1,  128, 8,  true, false>>(s, a);
+        else if (a.hidden_size % 4 == 0)
+            r = moe_smoothquant_<trait_<data_type,  1, 3, 1,  256, 4,  true, false>>(s, a);
+        else if (a.hidden_size % 2 == 0)
+            r = moe_smoothquant_<trait_<data_type,  1, 6, 1,  256, 2,  true, false>>(s, a);
+        else
+            r = moe_smoothquant_<trait_<data_type,  1, 3, 1, 1024, 1,  true, false>>(s, a);
+    }
+    else if(a.hidden_size <= 4096) {
+        if (a.hidden_size % 8 == 0)
+            r = moe_smoothquant_<trait_<data_type,  1, 2, 1,  256, 8,  true, false>>(s, a);
+        else if (a.hidden_size % 4 == 0)
+            r = moe_smoothquant_<trait_<data_type,  1, 4, 1,  256, 4,  true, false>>(s, a);
+        else if (a.hidden_size % 2 == 0)
+            r = moe_smoothquant_<trait_<data_type,  1, 2, 1, 1024, 2,  true, false>>(s, a);
+        else
+            r = moe_smoothquant_<trait_<data_type,  1, 4, 1, 1024, 1,  true, false>>(s, a);
+    }
+    else if(a.hidden_size > 4096) {
+        if (a.hidden_size % 8 == 0)
+            r = moe_smoothquant_<trait_<data_type,  1, 2, 1,  256, 8,  true, true>>(s, a);
+        else if (a.hidden_size % 4 == 0)
+            r = moe_smoothquant_<trait_<data_type,  1, 4, 1,  256, 4,  true, true>>(s, a);
+        else if (a.hidden_size % 2 == 0)
+            r = moe_smoothquant_<trait_<data_type,  1, 2, 1, 1024, 2,  true, true>>(s, a);
+        else
+            r = moe_smoothquant_<trait_<data_type,  1, 4, 1, 1024, 1,  true, true>>(s, a);
+    }
+    return r;
+    // clang-format on
+}
+
+float moe_smoothquant(moe_smoothquant_traits t,
+                      moe_smoothquant_args a,
+                      const ck_tile::stream_config& s)
+{
+    if(t.data_type.compare("fp16") == 0)
+    {
+        return moe_smoothquant_dispatch<ck_tile::fp16_t>(t, a, s);
+    }
+    else if(t.data_type.compare("bf16") == 0)
+    {
+        return moe_smoothquant_dispatch<ck_tile::bf16_t>(t, a, s);
+    }
+    else
+        throw std::runtime_error("Without supported instances!");
+}

example/ck_tile/14_moe_smoothquant/instances/moe_smoothquant_instance_common.hpp

+
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <ck_tile/core.hpp>
+#include "moe_smoothquant.hpp"
+#include <iostream>
+
+#pragma once
+
+using S = ck_tile::stream_config;
+using A = moe_smoothquant_args;
+
+template <typename DataType_,
+          ck_tile::index_t Repeat_M_,         // each thread repeat along M
+          ck_tile::index_t Repeat_N_,         // each thread repeat along N
+          ck_tile::index_t ThreadPerBlock_M_, // num threads along M
+          ck_tile::index_t ThreadPerBlock_N_, // num threads along N
+          ck_tile::index_t Vector_N_,         // vector size along N
+          bool kPadN_,
+          bool kTwoPass_>
+using trait_ = moe_smoothquant_traits_<DataType_,
+                                       Repeat_M_,
+                                       Repeat_N_,
+                                       ThreadPerBlock_M_,
+                                       ThreadPerBlock_N_,
+                                       Vector_N_,
+                                       kPadN_,
+                                       kTwoPass_>;
+
+template <typename Traits_>
+float moe_smoothquant_(const S& s, A a)
+{
+    using DataType = typename Traits_::DataType;
+
+    using PipelineProblem = ck_tile::SmoothquantPipelineProblem<
+        typename MoeSmoothquantTypeConfig<DataType>::XDataType,
+        typename MoeSmoothquantTypeConfig<DataType>::XScaleDataType,
+        typename MoeSmoothquantTypeConfig<DataType>::ComputeDataType,
+        typename MoeSmoothquantTypeConfig<DataType>::YScaleDataType,
+        typename MoeSmoothquantTypeConfig<DataType>::QYDataType,
+        typename Traits_::Shape,
+        Traits_::kPadN,
+        Traits_::kTwoPass>;
+
+    using OnePassPipeline = ck_tile::SmoothquantPipelineOnePass<PipelineProblem>;
+    using TwoPassPipeline = ck_tile::SmoothquantPipelineTwoPass<PipelineProblem>;
+    using Pipeline        = std::conditional_t<Traits_::kTwoPass, TwoPassPipeline, OnePassPipeline>;
+
+    using Kernel = ck_tile::MoeSmoothquant<Pipeline>;
+
+    const dim3 grids                       = Kernel::GridSize(a);
+    constexpr dim3 blocks                  = Kernel::BlockSize();
+    constexpr ck_tile::index_t kBlockPerCu = 1;
+
+    auto kargs = Kernel::MakeKargs(a);
+    if(s.log_level_ > 0)
+        std::cout << ", " << Kernel::GetName() << std::flush;
+
+    return ck_tile::launch_kernel(
+        s, ck_tile::make_kernel<blocks.x, kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
+}

example/ck_tile/14_moe_smoothquant/moe_smoothquant.cpp

+#include "ck_tile/host.hpp"
+#include "moe_smoothquant.hpp"
+#include <cstring>
+#include <set>
+
+// different threshold for different dtype
+template <typename DataType>
+auto get_elimit()
+{
+    double rtol = 1e-5;
+    double atol = 1e-5;
+    return ck_tile::make_tuple(rtol, atol);
+}
+
+template <>
+auto get_elimit<ck_tile::bf16_t>()
+{
+    double rtol = 1e-5;
+    double atol = 1e-5;
+    return ck_tile::make_tuple(rtol, atol);
+}
+
+template <>
+auto get_elimit<ck_tile::int8_t>()
+{
+    // due to rounding, int8 quantization might have 1 abs error
+    double rtol = 1;
+    double atol = 1;
+    return ck_tile::make_tuple(rtol, atol);
+}
+
+template <typename IndexType>
+void topid_unique_gen(
+    std::vector<IndexType>& host_tensor, int tokens, int topk, int num_expert, int seed)
+{
+    size_t total_size = topk * tokens;
+    std::srand(seed);
+    std::set<IndexType> unique_set;
+    IndexType current_v;
+    for(size_t i = 0; i < total_size; i++)
+    {
+        if(i % topk == 0)
+        {
+            unique_set.clear();
+        }
+        current_v = std::rand() % num_expert;
+        while(unique_set.find(current_v) != unique_set.end())
+        {
+            current_v = std::rand() % num_expert;
+        }
+        unique_set.insert(current_v);
+        host_tensor[i] = current_v;
+    }
+}
+
+auto create_args(int argc, char* argv[])
+{
+    ck_tile::ArgParser arg_parser;
+    arg_parser.insert("t", "3328", "tokens dimension")
+        .insert("h", "4096", "hidden_size dimension")
+        .insert("e", "32", "experts")
+        .insert("k", "5", "topk")
+        .insert("stride", "-1", "stride per row, if -1 then equal to hidden_size")
+        .insert("v", "1", "cpu validation or not")
+        .insert("kname", "1", "print kernel name or not")
+        .insert("prec", "fp16", "precision")
+        .insert("warmup", "5", "cold iter")
+        .insert("repeat", "20", "hot iter");
+
+    bool result = arg_parser.parse(argc, argv);
+    return std::make_tuple(result, arg_parser);
+}
+
+template <typename DataType>
+bool run(const ck_tile::ArgParser& arg_parser)
+{
+    ck_tile::index_t tokens      = arg_parser.get_int("t");
+    ck_tile::index_t hidden_size = arg_parser.get_int("h");
+    ck_tile::index_t stride      = arg_parser.get_int("stride");
+    if(stride < 0)
+        stride = hidden_size;
+    ck_tile::index_t experts = arg_parser.get_int("e");
+    ck_tile::index_t topk    = arg_parser.get_int("k");
+    std::string data_type    = arg_parser.get_str("prec");
+    int kname                = arg_parser.get_int("kname");
+    int do_validation        = arg_parser.get_int("v");
+    int warmup               = arg_parser.get_int("warmup");
+    int repeat               = arg_parser.get_int("repeat");
+
+    assert(stride >= hidden_size);
+
+    using TypeConfig = MoeSmoothquantTypeConfig<DataType>;
+
+    using XDataType       = typename TypeConfig::XDataType;
+    using XScaleDataType  = typename TypeConfig::XScaleDataType;
+    using YScaleDataType  = typename TypeConfig::YScaleDataType;
+    using QYDataType      = typename TypeConfig::QYDataType;
+    using ComputeDataType = typename TypeConfig::ComputeDataType;
+
+    // host verify
+    ck_tile::HostTensor<XDataType> x_host({tokens, hidden_size}, {stride, 1});
+    ck_tile::HostTensor<XScaleDataType> xscale_host({experts * hidden_size});
+    ck_tile::HostTensor<ck_tile::index_t> topk_ids_host({tokens, topk});
+
+    ck_tile::HostTensor<YScaleDataType> yscale_host_ref({topk * tokens}, {1});
+    ck_tile::HostTensor<YScaleDataType> yscale_host_dev({topk * tokens}, {1});
+
+    ck_tile::HostTensor<QYDataType> qy_host_ref({topk * tokens, hidden_size}, {stride, 1});
+    ck_tile::HostTensor<QYDataType> qy_host_dev({topk * tokens, hidden_size}, {stride, 1});
+
+    topid_unique_gen<ck_tile::index_t>(topk_ids_host.mData, tokens, topk, experts, 11937);
+    ck_tile::FillUniformDistribution<XDataType>{-.5f, .5f}(x_host);
+    ck_tile::FillUniformDistribution<XScaleDataType>{1e-3, .5f}(xscale_host);
+
+    ck_tile::DeviceMem x_buf(x_host.get_element_space_size_in_bytes());
+    ck_tile::DeviceMem xscale_buf(xscale_host.get_element_space_size_in_bytes());
+    ck_tile::DeviceMem topk_ids_buf(topk_ids_host.get_element_space_size_in_bytes());
+    ck_tile::DeviceMem yscale_buf(yscale_host_dev.get_element_space_size_in_bytes());
+    ck_tile::DeviceMem qy_buf(qy_host_dev.get_element_space_size_in_bytes());
+
+    x_buf.ToDevice(x_host.data());
+    xscale_buf.ToDevice(xscale_host.data());
+    topk_ids_buf.ToDevice(topk_ids_host.data());
+
+    std::cout << "[" << data_type << "]"
+              << " tokens:" << tokens << ", hidden_size:" << hidden_size << ", stride:" << stride
+              << ", experts:" << experts << ", topk:" << topk << std::flush;
+
+    moe_smoothquant_traits traits{data_type};
+
+    moe_smoothquant_args args{x_buf.GetDeviceBuffer(),
+                              xscale_buf.GetDeviceBuffer(),
+                              topk_ids_buf.GetDeviceBuffer(),
+                              yscale_buf.GetDeviceBuffer(),
+                              qy_buf.GetDeviceBuffer(),
+                              tokens,
+                              hidden_size,
+                              experts,
+                              topk,
+                              stride,
+                              stride};
+
+    float ave_time = moe_smoothquant(
+        traits, args, ck_tile::stream_config{nullptr, true, kname ? 1 : 0, warmup, repeat});
+
+    std::size_t num_byte =
+        sizeof(XDataType) * tokens * hidden_size + sizeof(XScaleDataType) * topk * hidden_size +
+        sizeof(YScaleDataType) * topk * tokens + sizeof(QYDataType) * topk * tokens * hidden_size;
+
+    float gb_per_sec = num_byte / 1.E6 / ave_time;
+    std::cout << ", " << ave_time * 1.E3 << " us, " << gb_per_sec << " GB/s" << std::flush;
+
+    bool pass = true;
+
+    if(do_validation)
+    {
+        using YDataType = ComputeDataType;
+        ck_tile::HostTensor<ComputeDataType> y_host({topk * tokens, hidden_size}, {stride, 1});
+        // smooth outlier
+        {
+            auto f = [&](auto i_token) {
+                for(int i_topk = 0; i_topk < topk; i_topk++)
+                {
+                    auto i_expert = topk_ids_host(i_token, i_topk);
+
+                    for(int i_h = 0; i_h < hidden_size; ++i_h)
+                    {
+                        auto v_xscale = ck_tile::type_convert<ComputeDataType>(
+                            xscale_host(i_expert * hidden_size + i_h));
+                        auto v_x = ck_tile::type_convert<ComputeDataType>(x_host(i_token, i_h));
+                        // y_host(i_token * topk + i_topk, i_h) = v_x * v_xscale;
+                        y_host(i_topk * tokens + i_token, i_h) = v_x * v_xscale;
+                    }
+                }
+            };
+
+            ck_tile::make_ParallelTensorFunctor(f, tokens)(std::thread::hardware_concurrency());
+        }
+
+        // yscale
+        {
+            ck_tile::HostTensor<YDataType> y_rowwise_amax_host({topk * tokens});
+
+            using ReduceAmax = ck_tile::ReduceOp::AbsMax;
+            ck_tile::reference_reduce<ComputeDataType, ComputeDataType, YDataType>(
+                y_host, y_rowwise_amax_host, ReduceAmax{});
+
+            auto op = [](const auto& v0) {
+                return v0 /
+                       ck_tile::type_convert<ComputeDataType>(ck_tile::numeric<QYDataType>::max());
+            };
+            ck_tile::reference_unary_elementwise<YDataType, YScaleDataType, ComputeDataType>(
+                y_rowwise_amax_host, yscale_host_ref, op);
+
+            yscale_buf.FromDevice(yscale_host_dev.mData.data());
+
+            auto [rtol, atol] = get_elimit<YScaleDataType>();
+            pass &= ck_tile::check_err(yscale_host_dev,
+                                       yscale_host_ref,
+                                       std::string("yscale Error: Incorrect results!"),
+                                       rtol,
+                                       atol);
+        }
+
+        // rowwise quantization
+        {
+            ck_tile::reference_rowwise_quantization2d<YDataType, YScaleDataType, QYDataType>(
+                y_host, yscale_host_ref, qy_host_ref);
+
+            qy_buf.FromDevice(qy_host_dev.data());
+            auto [rtol, atol] = get_elimit<QYDataType>();
+
+            if(stride == hidden_size)
+            {
+                pass = ck_tile::check_err(qy_host_dev,
+                                          qy_host_ref,
+                                          std::string("qy Error: Incorrect results!"),
+                                          rtol,
+                                          atol);
+            }
+            else
+            {
+                for(int i_r = 0; i_r < topk * tokens; i_r++)
+                {
+                    std::vector<QYDataType> qy_host_dev_row(qy_host_dev.begin() + i_r * stride,
+                                                            qy_host_dev.begin() + i_r * stride +
+                                                                hidden_size);
+                    std::vector<QYDataType> qy_host_ref_row(qy_host_ref.begin() + i_r * stride,
+                                                            qy_host_ref.begin() + i_r * stride +
+                                                                hidden_size);
+                    pass &= ck_tile::check_err(qy_host_dev_row,
+                                               qy_host_ref_row,
+                                               std::string("qy[") + std::to_string(i_r) +
+                                                   std::string("] Error: Incorrect results!"),
+                                               rtol,
+                                               atol);
+                }
+            }
+        }
+
+        std::cout << ", valid:" << (pass ? "y" : "n") << std::flush << std::endl;
+    }
+
+    return pass;
+}
+
+int main(int argc, char* argv[])
+{
+    auto [result, arg_parser] = create_args(argc, argv);
+    if(!result)
+        return -1;
+
+    const std::string data_type = arg_parser.get_str("prec");
+    if(data_type == "fp16")
+    {
+        return run<ck_tile::half_t>(arg_parser) ? 0 : -2;
+    }
+    else if(data_type == "bf16")
+    {
+        return run<ck_tile::bf16_t>(arg_parser) ? 0 : -2;
+    }
+
+    return -3;
+}

example/ck_tile/14_moe_smoothquant/moe_smoothquant.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck_tile/core.hpp"
+#include "ck_tile/host/kernel_launch.hpp"
+#include "ck_tile/ops/smoothquant.hpp"
+#include <string>
+
+template <typename DataType>
+struct MoeSmoothquantTypeConfig;
+
+template <>
+struct MoeSmoothquantTypeConfig<ck_tile::half_t>
+{
+    using XDataType       = ck_tile::half_t;
+    using XScaleDataType  = float;
+    using YScaleDataType  = float;
+    using QYDataType      = ck_tile::int8_t;
+    using ComputeDataType = float;
+};
+
+template <>
+struct MoeSmoothquantTypeConfig<ck_tile::bf16_t>
+{
+    using XDataType       = ck_tile::bf16_t;
+    using XScaleDataType  = float;
+    using YScaleDataType  = float;
+    using QYDataType      = ck_tile::int8_t;
+    using ComputeDataType = float;
+};
+
+// runtime args
+struct moe_smoothquant_args : public ck_tile::MoeSmoothquantHostArgs
+{
+};
+
+// this is used to pattern-match internl kernel implementation, not to instantiate kernel
+template <typename DataType_,
+          ck_tile::index_t Repeat_M_,         // each thread repeat along M
+          ck_tile::index_t Repeat_N_,         // each thread repeat along N
+          ck_tile::index_t ThreadPerBlock_M_, // num threads along M
+          ck_tile::index_t ThreadPerBlock_N_, // num threads along N
+          ck_tile::index_t Vector_N_,         // vector size along N
+          bool kPadN_,
+          bool kTwoPass_>
+struct moe_smoothquant_traits_
+{
+    using DataType = ck_tile::remove_cvref_t<DataType_>;
+
+    static constexpr bool is_warp_per_row = ThreadPerBlock_N_ <= warpSize;
+    static_assert((ThreadPerBlock_M_ * ThreadPerBlock_N_) % warpSize == 0);
+    static constexpr ck_tile::index_t total_warps =
+        (ThreadPerBlock_M_ * ThreadPerBlock_N_) / warpSize;
+
+    // num of warps along m
+    static constexpr ck_tile::index_t BlockWarps_M = []() {
+        if constexpr(is_warp_per_row)
+        {
+            static_assert(warpSize % ThreadPerBlock_N_ == 0);
+            return total_warps * (warpSize / ThreadPerBlock_N_);
+        }
+        else
+        {
+            // static_assert(warpSize % ThreadPerBlock_M_ == 0);
+            return total_warps / (ThreadPerBlock_N_ / warpSize);
+        }
+    }();
+
+    // num of warps along n
+    static constexpr ck_tile::index_t BlockWarps_N = []() {
+        if constexpr(is_warp_per_row)
+        {
+            static_assert(warpSize % ThreadPerBlock_N_ == 0);
+            return 1;
+        }
+        else
+        {
+            static_assert(ThreadPerBlock_N_ % warpSize == 0);
+            return ThreadPerBlock_N_ / warpSize;
+        }
+    }();
+
+    static constexpr ck_tile::index_t Repeat_M = Repeat_M_;
+    static constexpr ck_tile::index_t Repeat_N = Repeat_N_;
+
+    static constexpr ck_tile::index_t Block_M = Repeat_M_ * ThreadPerBlock_M_;
+    static constexpr ck_tile::index_t Block_N = Repeat_N_ * ThreadPerBlock_N_ * Vector_N_;
+
+    static constexpr ck_tile::index_t Warp_M = ThreadPerBlock_M_ / BlockWarps_M;
+    static constexpr ck_tile::index_t Warp_N = ThreadPerBlock_N_ / BlockWarps_N * Vector_N_;
+
+    using BlockTile  = ck_tile::sequence<Block_M, Block_N>;
+    using BlockWarps = ck_tile::sequence<BlockWarps_M, BlockWarps_N>;
+    using WarpTile   = ck_tile::sequence<Warp_M, Warp_N>;
+    using Vector     = ck_tile::sequence<1, Vector_N_>;
+
+    using Shape = ck_tile::Generic2dBlockShape<BlockTile, BlockWarps, WarpTile, Vector>;
+
+    static constexpr bool kPadN    = kPadN_;
+    static constexpr bool kTwoPass = kTwoPass_;
+};
+
+template <typename Traits_>
+float moe_smoothquant_(const ck_tile::stream_config& s, moe_smoothquant_args a);
+
+// This is the public API, will be generated by script
+struct moe_smoothquant_traits
+{
+    std::string data_type;
+};
+
+float moe_smoothquant(moe_smoothquant_traits, moe_smoothquant_args, const ck_tile::stream_config&);

example/ck_tile/14_moe_smoothquant/script/perf_test.sh

+
+EXE=build/bin/tile_example_moe_smoothquant
+
+$EXE -t=1 -h=1  -v=1 -prec=bf16 -repeat=1000
+$EXE -t=700 -h=80  -v=1 -prec=bf16 -repeat=1000
+$EXE -t=700 -h=128  -v=1 -prec=bf16 -repeat=1000
+$EXE -t=700 -h=144  -v=1 -prec=bf16 -repeat=1000
+$EXE -t=700 -h=168  -v=1 -prec=bf16 -repeat=1000
+$EXE -t=700 -h=184  -v=1 -prec=bf16 -repeat=1000
+$EXE -t=700 -h=256  -v=1 -prec=bf16 -repeat=1000
+$EXE -t=700 -h=288  -v=1 -prec=bf16 -repeat=1000
+$EXE -t=700 -h=344  -v=1 -prec=bf16 -repeat=1000
+$EXE -t=700 -h=376  -v=1 -prec=bf16 -repeat=1000
+$EXE -t=700 -h=448  -v=1 -prec=bf16 -repeat=1000
+$EXE -t=700 -h=512  -v=1 -prec=bf16 -repeat=1000
+$EXE -t=700 -h=924  -v=1 -prec=bf16 -repeat=1000
+$EXE -t=700 -h=1024  -v=1 -prec=bf16 -repeat=1000
+$EXE -t=700 -h=1078  -v=1 -prec=bf16 -repeat=1000
+$EXE -t=700 -h=1996  -v=1 -prec=bf16 -repeat=1000
+$EXE -t=700 -h=4080  -v=1 -prec=bf16 -repeat=1000
+
+$EXE -t=700 -h=80  -v=1  -prec=fp16 -repeat=1000
+$EXE -t=700 -h=128  -v=1 -prec=fp16 -repeat=1000
+$EXE -t=700 -h=144  -v=1 -prec=fp16 -repeat=1000
+$EXE -t=700 -h=168  -v=1 -prec=fp16 -repeat=1000
+$EXE -t=700 -h=184  -v=1 -prec=fp16 -repeat=1000
+$EXE -t=700 -h=256  -v=1 -prec=fp16 -repeat=1000
+$EXE -t=700 -h=288  -v=1 -prec=fp16 -repeat=1000
+$EXE -t=700 -h=344  -v=1 -prec=fp16 -repeat=1000
+$EXE -t=700 -h=376  -v=1 -prec=fp16 -repeat=1000
+$EXE -t=700 -h=448  -v=1 -prec=fp16 -repeat=1000
+$EXE -t=700 -h=512  -v=1 -prec=fp16 -repeat=1000
+$EXE -t=700 -h=924  -v=1 -prec=fp16 -repeat=1000
+$EXE -t=700 -h=1024  -v=1 -prec=fp16 -repeat=1000
+$EXE -t=700 -h=1078  -v=1 -prec=fp16 -repeat=1000
+$EXE -t=700 -h=1996  -v=1 -prec=fp16 -repeat=1000
+$EXE -t=700 -h=4080  -v=1 -prec=fp16 -repeat=1000
\ No newline at end of file

example/ck_tile/14_moe_smoothquant/script/smoke_test.sh

+#!/bin/sh
+EXE=build/bin/tile_example_moe_smoothquant
+
+for pr_i in "fp16" "bf16" ; do
+$EXE -prec=$pr_i -t=99  -h=13
+$EXE -prec=$pr_i -t=17  -h=16
+$EXE -prec=$pr_i -t=1   -h=100
+$EXE -prec=$pr_i -t=4   -h=128
+$EXE -prec=$pr_i -t=80  -h=127
+$EXE -prec=$pr_i -t=22  -h=255 -stride=256
+$EXE -prec=$pr_i -t=7   -h=599
+$EXE -prec=$pr_i -t=19  -h=512
+$EXE -prec=$pr_i -t=33  -h=313 -stride=1000
+$EXE -prec=$pr_i -t=11  -h=510
+$EXE -prec=$pr_i -t=171 -h=676 -stride=818
+$EXE -prec=$pr_i -t=91  -h=636
+$EXE -prec=$pr_i -t=12  -h=768 -stride=800
+$EXE -prec=$pr_i -t=100 -h=766 -stride=812
+$EXE -prec=$pr_i -t=31  -h=1024
+$EXE -prec=$pr_i -t=64  -h=1000 -stride=1004
+$EXE -prec=$pr_i -t=8   -h=1501
+$EXE -prec=$pr_i -t=3   -h=1826
+$EXE -prec=$pr_i -t=5   -h=2040
+$EXE -prec=$pr_i -t=7   -h=2734
+$EXE -prec=$pr_i -t=1   -h=3182
+$EXE -prec=$pr_i -t=9   -h=4096
+$EXE -prec=$pr_i -t=3   -h=8192
+$EXE -prec=$pr_i -t=1   -h=10547
+$EXE -prec=$pr_i -t=3   -h=17134
+done

example/ck_tile/CMakeLists.txt

@@ -13,3 +13,4 @@ add_subdirectory(10_rmsnorm2d)
 add_subdirectory(11_add_rmsnorm2d_rdquant)
 add_subdirectory(12_smoothquant)
 add_subdirectory(13_moe_sorting)
+add_subdirectory(14_moe_smoothquant)

include/ck_tile/core/config.hpp

@@ -64,6 +64,7 @@
 #define CK_TILE_FLOAT_TO_BFLOAT16_TRUNCATE_WITH_NAN 1
 #define CK_TILE_FLOAT_TO_BFLOAT16_TRUNCATE 2
 #define CK_TILE_FLOAT_TO_BFLOAT16_STANDARD_ASM 3
+#define CK_TILE_FLOAT_TO_BFLOAT16_RTA_ASM 4
 
 #ifndef CK_TILE_FLOAT_TO_BFLOAT16_DEFAULT
 #define CK_TILE_FLOAT_TO_BFLOAT16_DEFAULT CK_TILE_FLOAT_TO_BFLOAT16_TRUNCATE
@@ -225,3 +226,7 @@
 #ifndef CK_TILE_WORKAROUND_SWDEV_383542
 #define CK_TILE_WORKAROUND_SWDEV_383542 1
 #endif
+
+#ifndef CK_TILE_REFERENCE_MOE_SORTING_MOCK_ID
+#define CK_TILE_REFERENCE_MOE_SORTING_MOCK_ID 1
+#endif

include/ck_tile/core/numeric/bfloat16.hpp

@@ -18,6 +18,7 @@ enum class bf16_rounding_mode
     truncate_with_nan,
     truncate,
     standard_asm,
+    rta_asm, // round to nearest away
 };
 
 template <bf16_rounding_mode rounding =
@@ -180,6 +181,39 @@ uint16_t float_to_bf16_rtn_asm(float f)
     return uint16_t(u.int32);
 }
 
+// TODO: do we need this on host?
+CK_TILE_HOST
+uint16_t float_to_bf16_rta_asm(float f) { return float_to_bf16_rtn_raw(f); }
+
+CK_TILE_DEVICE
+uint16_t float_to_bf16_rta_asm(float f)
+{
+    union
+    {
+        float fp32;
+        struct
+        {
+            uint16_t lo;
+            uint16_t hi;
+        };
+    } u = {f};
+
+    const uint32_t low_nan = 0x7fff;
+    const uint32_t hi_nan  = 0x7fff0000;
+
+    using uint32x2_t = uint32_t __attribute__((ext_vector_type(2)));
+    uint32x2_t check_nan;
+
+    asm volatile("v_cmp_u_f32 %[s_cnan], %[v_x], %[v_x] \n"
+                 "v_add3_u32 %[v_x], %[v_x], %[v_blo], 1 \n"
+                 "v_cndmask_b32 %[v_x], %[v_x], %[v_bhi], %[s_cnan]"
+                 : [s_cnan] "+s"(check_nan), [v_x] "+v"(u.fp32)
+                 : [v_blo] "v"(low_nan), [v_bhi] "v"(hi_nan));
+
+    // Note: in above code snipet, we use hi 16 bit
+    return u.hi;
+}
+
 // Truncate instead of rounding, preserving SNaN
 CK_TILE_HOST_DEVICE
 constexpr uint16_t float_to_bf16_truc_nan_raw(float f)
@@ -213,6 +247,8 @@ CK_TILE_HOST_DEVICE constexpr uint16_t float_to_bf16_raw(float f, constant<round
         return float_to_bf16_rtn_asm(f);
     else if constexpr(rounding == bf16_rounding_mode::truncate_with_nan)
         return float_to_bf16_truc_nan_raw(f);
+    else if constexpr(rounding == bf16_rounding_mode::rta_asm)
+        return float_to_bf16_rta_asm(f);
     else
         return float_to_bf16_truc_raw(f);
 }

include/ck_tile/host/reference/reference_moe_sorting.hpp

@@ -8,6 +8,9 @@
 
 namespace ck_tile {
 
+#define MOE_SORTING_MOCK_ID(token_id_, topk_id_) \
+    static_cast<uint32_t>(((token_id_)&0x00ffffff) | (((topk_id_)&0xff) << 24))
+
 template <typename WeightType, typename IndexType = index_t>
 CK_TILE_HOST void reference_moe_sorting(const HostTensor<IndexType>& topk_ids,
                                         const HostTensor<WeightType>& weights,
@@ -20,8 +23,14 @@ CK_TILE_HOST void reference_moe_sorting(const HostTensor<IndexType>& topk_ids,
 {
     const index_t num_token = topk_ids.mDesc.get_lengths()[0];
     const index_t topk      = topk_ids.mDesc.get_lengths()[1];
-    std::vector<std::vector<IndexType>> expert_tokens(experts,
+    // allocate a temp buffer, and fill the value with [number_token|topk]
+    std::vector<std::vector<IndexType>> expert_tokens(
+        experts,
+#if CK_TILE_REFERENCE_MOE_SORTING_MOCK_ID
+        std::vector<IndexType>(unit_size, MOE_SORTING_MOCK_ID(num_token, topk)));
+#else
         std::vector<IndexType>(unit_size, num_token));
+#endif
     std::vector<std::vector<WeightType>> expert_token_weights(
         experts, std::vector<WeightType>(unit_size, 0));
     std::vector<IndexType> expert_slices(experts, 1);
@@ -42,12 +51,19 @@ CK_TILE_HOST void reference_moe_sorting(const HostTensor<IndexType>& topk_ids,
                 expert_token_weights[e].resize(new_size);
                 for(index_t i = (expert_slices[e] - 1) * unit_size; i < new_size; i++)
                 {
+#if CK_TILE_REFERENCE_MOE_SORTING_MOCK_ID
+                    expert_tokens[e][i] = MOE_SORTING_MOCK_ID(num_token, topk);
+#else
                     expert_tokens[e][i] = num_token;
+#endif
                     expert_token_weights[e][i] = 0;
                 }
             }
-
+#if CK_TILE_REFERENCE_MOE_SORTING_MOCK_ID
+            expert_tokens[e][idx] = MOE_SORTING_MOCK_ID(t, k);
+#else
             expert_tokens[e][idx] = t;
+#endif
             expert_token_weights[e][idx] = w;
             expert_slice_idxs[e]++;
         }
@@ -75,4 +91,7 @@ CK_TILE_HOST void reference_moe_sorting(const HostTensor<IndexType>& topk_ids,
     unit_cnt *= unit_size;
     return;
 }
+
+#undef MOE_SORTING_MOCK_ID
+
 } // namespace ck_tile

include/ck_tile/ops/fused_moe/kernel/moe_sorting_kernel.hpp

@@ -12,20 +12,77 @@
 
 namespace ck_tile {
 
+#define MOE_SORTING_MOCK_ID(token_id_, topk_id_) \
+    static_cast<uint32_t>(((token_id_)&0x00ffffff) | (((topk_id_)&0xff) << 24))
+
+// clang-format off
+// [indexing implementation-1]
+// using M_a as constexpr block_size to partition all tokens into different slices
+// each slice map to one expert, and one expert can have multiple slices
+// e.g. num_experts = 6, topk=3, M_a = 4, input_tokens = 5
+// before sort, topk_ids is : [[0, 3, 5], [2, 3, 5], [1, 3, 5], [1, 2, 3], [1, 3, 5]]
+//                            tok-0      tok-1      tok-2      tok-3      tok-4
+//           topk_weight is : [[a, b, c], [d, e, f], [g, h, i], [j, k, l], [m, n, o]] (some float number)
+//
+// token_id_per_expert is : [[0], [2, 3, 4], [1, 3], [0, 1, 2, 3, 4], [], [0, 1, 2, 5]]
+//  (only for reference)    exp-0  exp-1     exp-2   exp-3          exp-4  exp-5
+// weight_id_per_expert is: [[a], [g, j, m], [d, k], [b, e, h, l, n], [], [c, f, i, o]]
+//
+// max_num_tokens_padded : topk * input_tokens + num_experts * (M_a - 1)
+// * this could be larger than actual, since actual tokens are on GPU
+//
+// sorted_token_ids_ptr   : [0, 6, 6, 6, 2, 3, 4, 6, 1, 3, 6, 6, 0, 1, 2, 3, 4, 6, 6, 6, 6, 6, 6, 6, 0, 1, 2, 5]
+//                          |-  exp-0  -|-  exp-1  -|-  exp-2  -|-      exp-3          -|-  exp-4 -|-  exp-5  -|
+// sorted_weight_ptr      : [a, *, *, *, g, j, m, *, d, k, *, *, b, e, h, l, n, *, *, *, *, *, *, *, c, f, i, o]
+//
+// * length is max_num_tokens_padded, actual size is num_tokens_post_padded_ptr
+//
+// * Note on token_id_per_expert/sorted_token_ids_ptr data:
+// currently we do not have topk information from the data of token_id_per_expert/sorted_token_ids_ptr.
+// In some cases(like smooth-quant), we need topk information to indexing into tokens quant from 
+// different expert smooth quant. So we modify the number stored inside token_id_per_expert/sorted_token_ids_ptr
+//
+//       32bit    0........23 24.....31 bit
+//      (data) -> (token_id | topk_id)
+// low 24 bit is for token id, top 8 bit is for topk id
+//
+// the input after smooth-quant is [topk, token, hidden_dim], originally it is [token, hidden_dim]
+// the input scale for token is [topk, token, 1], the smooth-quant scale for first gemm is [expert, interm_dim]
+//
+// sorted_expert_ids_ptr  : [0, 1, 2, 3, 3, 4, 5]
+// * length is (max_num_tokens_padded + block_size - 1) / block_size
+//
+// num_tokens_post_padded_ptr : [28]
+// num_sorted_tiles_ptr : [7]
+//
+// * different from vLLM
+//   1) token_id stored in sorted_token_ids_ptr is actual token_id, not token_id*top_K expanded id
+//   2）need sorted_weight_ptr
+//   3) use num_sorted_tiles_ptr, already divided by M_a
+//
+// * below used for indexing
+//  1) sorted_token_ids_ptr [max_num_tokens_padded]
+//  2) sorted_weight_ptr
+//  3) sorted_expert_ids_ptr
+//  4）num_tokens_post_padded_ptr/num_sorted_tiles_ptr (select one)
+//
+//   max_num_tokens_padded: opk_ids.numel() + num_experts * (block_size - 1)
 struct MoeSortingHostArgs
 {
-    const void* p_topk_ids;
-    const void* p_weights;
+    const void* p_topk_ids;     // [token, topk]
+    const void* p_weights;      // [token, topk]
     void* p_sorted_token_ids;
     void* p_sorted_weights;
     void* p_sorted_expert_ids;
     void* p_total_tokens_post_pad;
+    // we fused the setzero of output of fused-moe buffer
+    // set this pointer to nullptr will skip this operation
     void* p_moe_buf;
     index_t tokens;
-    index_t unit_size;
+    index_t unit_size;      // this is the M_a of fused-moe kernel
     index_t num_experts;
     index_t topk;
-    index_t moe_buf_bytes;
+    index_t moe_buf_bytes;  // byte size of p_moe_buf
 };
 
 template <typename Problem_>
@@ -183,7 +240,13 @@ struct MoeSortingKernel
             index_t expert_id = topk_id[i];
             index_t rank_post_pad =
                 tokens_cnts[calc_index(num_experts, tid, expert_id)] + cumsum[expert_id];
+#if CK_TILE_REFERENCE_MOE_SORTING_MOCK_ID
+            uint32_t curr_token_id, curr_topk_id;
+            topk_mdiv.divmod(i, curr_token_id, curr_topk_id);
+            p_sorted_token_ids[rank_post_pad] = MOE_SORTING_MOCK_ID(curr_token_id, curr_topk_id);
+#else
             p_sorted_token_ids[rank_post_pad] = topk_mdiv.div(i);
+#endif
             p_sorted_weights[rank_post_pad] = weights[i];
             ++tokens_cnts[calc_index(num_experts, tid, expert_id)];
         }
@@ -195,7 +258,12 @@ struct MoeSortingKernel
                 cumsum[tid] + tokens_cnts[calc_index(num_experts, blockDim.x, tid)];
             while(expert_offset < cumsum[tid + 1])
             {
+#if CK_TILE_REFERENCE_MOE_SORTING_MOCK_ID
+                p_sorted_token_ids[expert_offset] =
+                    MOE_SORTING_MOCK_ID(prefill_token, topk_mdiv.divisor);
+#else
                 p_sorted_token_ids[expert_offset] = prefill_token;
+#endif
                 p_sorted_weights[expert_offset] = static_cast<WeightType>(0.0);
                 expert_offset++;
             }
@@ -229,4 +297,7 @@ struct MoeSortingKernel
                                            smem);
     }
 };
+
+#undef MOE_SORTING_MOCK_ID
+
 } // namespace ck_tile

include/ck_tile/ops/smoothquant.hpp

@@ -3,6 +3,7 @@
 
 #pragma once
 
+#include "ck_tile/ops/smoothquant/kernel/moe_smoothquant_kernel.hpp"
 #include "ck_tile/ops/smoothquant/kernel/smoothquant_kernel.hpp"
 #include "ck_tile/ops/smoothquant/pipeline/smoothquant_pipeline_default_policy.hpp"
 #include "ck_tile/ops/smoothquant/pipeline/smoothquant_pipeline_one_pass.hpp"

include/ck_tile/ops/smoothquant/kernel/moe_smoothquant_kernel.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck_tile/core.hpp"
+#include "ck_tile/ops/common.hpp"
+
+namespace ck_tile {
+
+// host side args
+struct MoeSmoothquantHostArgs
+{
+    const void* p_x;        // [tokens ,hidden_size], input, fp16/bf16
+    const void* p_xscale;   // [experts, hidden_size], input, columnwise scale, fp32
+    const void* p_topk_ids; // [tokens, topk]
+
+    void* p_yscale; // [topk * tokens,  1], output, rowwise quant scale
+    void* p_qy;     // [topk * tokens, hidden_size], output
+
+    index_t tokens;
+    index_t hidden_size;
+    index_t experts;
+    index_t topk;
+    index_t x_stride; // input x row stride
+    index_t y_stride; // output y stride(stride for topk)
+};
+
+// TODO: Extract some type to wrapper class
+template <typename Pipeline_>
+struct MoeSmoothquant
+{
+    using Pipeline = remove_cvref_t<Pipeline_>;
+    using Problem  = typename Pipeline::Problem;
+
+    using XDataType       = remove_cvref_t<typename Problem::XDataType>;
+    using XScaleDataType  = remove_cvref_t<typename Problem::XScaleDataType>;
+    using ComputeDataType = remove_cvref_t<typename Problem::ComputeDataType>;
+    using YScaleDataType  = remove_cvref_t<typename Problem::YScaleDataType>;
+    using QYDataType      = remove_cvref_t<typename Problem::QYDataType>;
+
+    static constexpr index_t Block_M = Problem::BlockShape::Block_M;
+    static constexpr index_t Block_N = Problem::BlockShape::Block_N;
+    static constexpr bool kPadM      = false; // always no need to pad along M
+    static constexpr bool kPadN      = Problem::kPadN;
+    static constexpr bool kTwoPass   = Problem::kTwoPass;
+
+    static constexpr index_t ThreadPerWarp_N = Problem::BlockShape::ThreadPerWarp_N;
+    static constexpr index_t Vector_N        = Problem::BlockShape::Vector_N;
+    static constexpr index_t Repeat_N        = Problem::BlockShape::Repeat_N;
+
+    static constexpr auto I0 = number<0>{};
+    static constexpr auto I1 = number<1>{};
+
+    static_assert(Problem::BlockShape::Repeat_M == 1);
+
+    struct Kargs
+    {
+        const void* p_x;        // [tokens ,hidden_size], input, fp16/bf16
+        const void* p_xscale;   // [experts, hidden_size], input, columnwise scale, fp32
+        const void* p_topk_ids; // [tokens, topk]
+
+        void* p_yscale; // [topk, tokens, 1], output, rowwise quant scale
+        void* p_qy;     // [topk, tokens, hidden_size], output
+
+        index_t tokens;
+        index_t hidden_size;
+        index_t experts;
+        index_t topk;
+        index_t x_stride; // input x row stride
+        index_t y_stride; // output y stride(stride for topk)
+    };
+    using Hargs = MoeSmoothquantHostArgs;
+
+    CK_TILE_HOST static constexpr Kargs MakeKargs(const Hargs& hargs)
+    {
+        return Kargs{hargs.p_x,
+                     hargs.p_xscale,
+                     hargs.p_topk_ids,
+                     hargs.p_yscale,
+                     hargs.p_qy,
+                     hargs.tokens,
+                     hargs.hidden_size,
+                     hargs.experts,
+                     hargs.topk,
+                     hargs.x_stride,
+                     hargs.y_stride};
+    }
+
+    CK_TILE_HOST static constexpr auto GridSize(const Hargs& hargs)
+    {
+        return dim3(hargs.topk, integer_divide_ceil(hargs.tokens, Block_M), 1);
+    }
+
+    CK_TILE_HOST static constexpr auto BlockSize() { return Problem::BlockShape::BlockSize; }
+
+    // clang-format off
+    template <typename T> struct t2s;
+    template <> struct t2s<float> { static constexpr const char * name = "fp32"; };
+    template <> struct t2s<ck_tile::fp16_t> { static constexpr const char * name = "fp16"; };
+    template <> struct t2s<ck_tile::bf16_t> { static constexpr const char * name = "bf16"; };
+    template <> struct t2s<ck_tile::fp8_t> { static constexpr const char * name = "fp8"; };
+    template <> struct t2s<ck_tile::bf8_t> { static constexpr const char * name = "bf8"; };
+    // clang-format on
+
+    // in byte
+    CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize() { return Pipeline::GetSmemSize(); }
+
+    CK_TILE_HOST static std::string GetName()
+    {
+        // clang-format off
+        using S_ = typename Problem::BlockShape;
+        auto surfix = [&] () {
+            std::string n;
+            if (kPadN) n += "_pn";
+            if (kTwoPass) n += "_2p";
+            return n; }();
+
+        #define _SS_  std::string
+        #define _TS_  std::to_string
+        return _SS_("moe_smoothquant_") + _SS_(t2s<XDataType>::name) + "_" +
+             _TS_(S_::Block_M) + "x" + _TS_(S_::Block_N) + "_" + _TS_(S_::WarpPerBlock_M) + "x" + _TS_(S_::WarpPerBlock_N) + "_" +
+             _TS_(S_::Warp_M) + "x" + _TS_(S_::Warp_N) + "_" + _TS_(S_::Vector_M) + "x" + _TS_(S_::Vector_N) + "_" +
+             _SS_(Pipeline::name) + surfix;
+        #undef _SS_
+        #undef _TS_
+        // clang-format on
+    }
+
+    CK_TILE_DEVICE void operator()(Kargs kargs) const
+    {
+        const index_t i_topk  = blockIdx.x;
+        const index_t i_token = blockIdx.y * Block_M;
+        const index_t i_token_in_thrd =
+            __builtin_amdgcn_readfirstlane(threadIdx.x / Problem::BlockShape::ThreadPerBlock_N);
+
+        const index_t i_expert = reinterpret_cast<const index_t*>(
+            kargs.p_topk_ids)[(i_token + i_token_in_thrd) * kargs.topk + i_topk];
+
+        // [tokens ,hidden_size]
+        const auto x_window = [&]() {
+            const auto tmp_ = make_naive_tensor_view<address_space_enum::global>(
+                static_cast<const XDataType*>(kargs.p_x),
+                make_tuple(kargs.tokens, kargs.hidden_size),
+                make_tuple(kargs.x_stride, 1),
+                number<Vector_N>{},
+                number<1>{});
+
+            const auto tmp2_ = pad_tensor_view(
+                tmp_, make_tuple(number<Block_M>{}, number<Block_N>{}), sequence<kPadM, kPadN>{});
+            return make_tile_window(
+                tmp2_, make_tuple(number<Block_M>{}, number<Block_N>{}), {i_token, 0});
+        }();
+
+        // [experts, hidden_size],
+        const auto xscale_window = [&]() {
+            const auto tmp_ = make_naive_tensor_view<address_space_enum::global>(
+                static_cast<const XScaleDataType*>(kargs.p_xscale) + i_expert * kargs.hidden_size,
+                make_tuple(kargs.hidden_size),
+                make_tuple(1),
+                number<Vector_N>{},
+                number<1>{});
+
+            const auto tmp2_ =
+                pad_tensor_view(tmp_, make_tuple(number<Block_N>{}), sequence<kPadN>{});
+
+            return make_tile_window(tmp2_, make_tuple(number<Block_N>{}), {0});
+        }();
+
+        // [topk, tokens]
+        auto yscale_window = [&]() {
+            const auto tmp_ = make_naive_tensor_view<address_space_enum::global>(
+                static_cast<YScaleDataType*>(kargs.p_yscale) + i_topk * kargs.tokens,
+                make_tuple(kargs.tokens),
+                make_tuple(1),
+                number<1>{});
+
+            const auto tmp2_ =
+                pad_tensor_view(tmp_, make_tuple(number<Block_M>{}), sequence<kPadM>{});
+
+            return make_tile_window(tmp2_, make_tuple(number<Block_M>{}), {i_token});
+        }();
+
+        // [topk, tokens, hidden_size]
+        auto qy_window = [&]() {
+            auto tmp_ = make_naive_tensor_view<address_space_enum::global>(
+                static_cast<QYDataType*>(kargs.p_qy) + i_topk * kargs.tokens * kargs.y_stride,
+                make_tuple(kargs.tokens, kargs.hidden_size),
+                make_tuple(kargs.y_stride, 1),
+                number<Vector_N>{},
+                number<1>{});
+
+            auto tmp2_ = pad_tensor_view(
+                tmp_, make_tuple(number<Block_M>{}, number<Block_N>{}), sequence<kPadM, kPadN>{});
+            return make_tile_window(
+                tmp2_, make_tuple(number<Block_M>{}, number<Block_N>{}), {i_token, 0});
+        }();
+
+        __shared__ char smem[GetSmemSize()];
+
+        Pipeline{}(x_window, xscale_window, yscale_window, qy_window, kargs.hidden_size, smem);
+    }
+};
+
+} // namespace ck_tile