Skip to content

GitLab

Commit cd4d4629 authored by danyao12's avatar danyao12
Browse files

Merge branch 'develop' into ck_tile/fa_bwd_v3

parents 21d12bb7 888317e6
Hide whitespace changes
Inline Side-by-side
Showing with 1127 additions and 0 deletions
example/ck_tile/12_smoothquant/instances/smoothquant_fwd_api.cpp 0 → 100644
View file @ cd4d4629
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include <ck_tile/core.hpp>
#include "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_ = smoothquant_traits_<DataType_,
Repeat_M_,
Repeat_N_,
ThreadPerBlock_M_,
ThreadPerBlock_N_,
Vector_N_,
kPadN_,
kTwoPass_>;
template <typename data_type>
float smoothquant_dispatch(smoothquant_traits /*t*/,
smoothquant_args a,
const ck_tile::stream_config& s)
{
float r = -1;
// clang-format off
// rm rn tm tn vn pd 2p
if(a.n <= 64) {
r = smoothquant_<trait_<data_type, 1, 1, 4, 64, 1, true, false>>(s, a);
}
else if(a.n <= 128) {
if (a.n % 2 == 0)
r = smoothquant_<trait_<data_type, 1, 1, 4, 64, 2, true, false>>(s, a);
else
r = smoothquant_<trait_<data_type, 1, 2, 4, 64, 1, true, false>>(s, a);
}
else if(a.n <= 256) {
if (a.n % 4 == 0)
r = smoothquant_<trait_<data_type, 1, 1, 4, 64, 4, true, false>>(s, a);
else if (a.n % 2 == 0)
r = smoothquant_<trait_<data_type, 1, 2, 4, 64, 2, true, false>>(s, a);
else
r = smoothquant_<trait_<data_type, 1, 4, 4, 64, 1, true, false>>(s, a);
}
else if(a.n <= 512) {
if (a.n % 8 == 0)
r = smoothquant_<trait_<data_type, 1, 1, 4, 64, 8, true, false>>(s, a);
else if (a.n % 4 == 0)
r = smoothquant_<trait_<data_type, 1, 2, 4, 64, 4, true, false>>(s, a);
else if (a.n % 2 == 0)
r = smoothquant_<trait_<data_type, 1, 4, 4, 64, 2, true, false>>(s, a);
else
r = smoothquant_<trait_<data_type, 1, 8, 4, 64, 1, true, false>>(s, a);
}
else if(a.n <= 768) {
if (a.n % 4 == 0)
r = smoothquant_<trait_<data_type, 1, 3, 4, 64, 4, true, false>>(s, a);
else if (a.n % 2 == 0)
r = smoothquant_<trait_<data_type, 1, 6, 4, 64, 2, true, false>>(s, a);
else
r = smoothquant_<trait_<data_type, 1,12, 4, 64, 1, true, false>>(s, a);
}
else if(a.n <= 1024) {
if (a.n % 8 == 0)
r = smoothquant_<trait_<data_type, 1, 1, 2, 128, 8, true, false>>(s, a);
else if (a.n % 4 == 0)
r = smoothquant_<trait_<data_type, 1, 2, 2, 128, 4, true, false>>(s, a);
else if (a.n % 2 == 0)
r = smoothquant_<trait_<data_type, 1, 4, 2, 128, 2, true, false>>(s, a);
else
r = smoothquant_<trait_<data_type, 1, 4, 1, 256, 1, true, false>>(s, a);
}
else if(a.n <= 1536) {
if (a.n % 8 == 0)
r = smoothquant_<trait_<data_type, 1, 3, 4, 64, 8, true, false>>(s, a);
else if (a.n % 4 == 0)
r = smoothquant_<trait_<data_type, 1, 3, 2, 128, 4, true, false>>(s, a);
else if (a.n % 2 == 0)
r = smoothquant_<trait_<data_type, 1, 3, 1, 256, 2, true, false>>(s, a);
else
r = smoothquant_<trait_<data_type, 1, 6, 1, 256, 1, true, false>>(s, a);
}
else if(a.n <= 2048) {
if (a.n % 8 == 0)
r = smoothquant_<trait_<data_type, 1, 1, 1, 256, 8, true, false>>(s, a);
else if (a.n % 4 == 0)
r = smoothquant_<trait_<data_type, 1, 2, 1, 256, 4, true, false>>(s, a);
else if (a.n % 2 == 0)
r = smoothquant_<trait_<data_type, 1, 4, 1, 256, 2, true, false>>(s, a);
else
r = smoothquant_<trait_<data_type, 1, 8, 1, 256, 1, true, false>>(s, a);
}
else if(a.n <= 3072) {
if (a.n % 8 == 0)
r = smoothquant_<trait_<data_type, 1, 3, 1, 128, 8, true, false>>(s, a);
else if (a.n % 4 == 0)
r = smoothquant_<trait_<data_type, 1, 3, 1, 256, 4, true, false>>(s, a);
else if (a.n % 2 == 0)
r = smoothquant_<trait_<data_type, 1, 6, 1, 256, 2, true, false>>(s, a);
else
r = smoothquant_<trait_<data_type, 1, 3, 1, 1024, 1, true, false>>(s, a);
}
else if(a.n <= 4096) {
if (a.n % 8 == 0)
r = smoothquant_<trait_<data_type, 1, 2, 1, 256, 8, true, false>>(s, a);
else if (a.n % 4 == 0)
r = smoothquant_<trait_<data_type, 1, 4, 1, 256, 4, true, false>>(s, a);
else if (a.n % 2 == 0)
r = smoothquant_<trait_<data_type, 1, 2, 1, 1024, 2, true, false>>(s, a);
else
r = smoothquant_<trait_<data_type, 1, 4, 1, 1024, 1, true, false>>(s, a);
}
else if(a.n > 4096) {
if (a.n % 8 == 0)
r = smoothquant_<trait_<data_type, 1, 2, 1, 256, 8, true, true>>(s, a);
else if (a.n % 4 == 0)
r = smoothquant_<trait_<data_type, 1, 4, 1, 256, 4, true, true>>(s, a);
else if (a.n % 2 == 0)
r = smoothquant_<trait_<data_type, 1, 2, 1, 1024, 2, true, true>>(s, a);
else
r = smoothquant_<trait_<data_type, 1, 4, 1, 1024, 1, true, true>>(s, a);
}
return r;
// clang-format on
}
float smoothquant(smoothquant_traits t, smoothquant_args a, const ck_tile::stream_config& s)
{
if(t.data_type.compare("fp16") == 0)
{
return smoothquant_dispatch<ck_tile::fp16_t>(t, a, s);
}
else if(t.data_type.compare("bf16") == 0)
{
return smoothquant_dispatch<ck_tile::bf16_t>(t, a, s);
}
else
throw std::runtime_error("Without supported instances!");
}
example/ck_tile/12_smoothquant/instances/smoothquant_instance_common.hpp 0 → 100644
View file @ cd4d4629
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include <ck_tile/core.hpp>
#include "smoothquant.hpp"
#include <iostream>
#pragma once
using S = ck_tile::stream_config;
using A = 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_ = smoothquant_traits_<DataType_,
Repeat_M_,
Repeat_N_,
ThreadPerBlock_M_,
ThreadPerBlock_N_,
Vector_N_,
kPadN_,
kTwoPass_>;
template <typename Traits_>
float smoothquant_(const S& s, A a)
{
using DataType = typename Traits_::DataType;
using PipelineProblem = ck_tile::SmoothquantPipelineProblem<
typename SmoothquantTypeConfig<DataType>::XDataType,
typename SmoothquantTypeConfig<DataType>::XScaleDataType,
typename SmoothquantTypeConfig<DataType>::ComputeDataType,
typename SmoothquantTypeConfig<DataType>::YScaleDataType,
typename SmoothquantTypeConfig<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::Smoothquant<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/12_smoothquant/script/perf_test.sh 0 → 100755
View file @ cd4d4629
EXE="$(find . -name tile_smoothquant -type f | head -n 1)"
$EXE -m=1 -n=1 -e=1e-12 -v=1 -prec=bf16 -repeat=1000
$EXE -m=700 -n=80 -e=1e-12 -v=1 -prec=bf16 -repeat=1000
$EXE -m=700 -n=128 -e=1e-12 -v=1 -prec=bf16 -repeat=1000
$EXE -m=700 -n=144 -e=1e-12 -v=1 -prec=bf16 -repeat=1000
$EXE -m=700 -n=168 -e=1e-12 -v=1 -prec=bf16 -repeat=1000
$EXE -m=700 -n=184 -e=1e-12 -v=1 -prec=bf16 -repeat=1000
$EXE -m=700 -n=256 -e=1e-12 -v=1 -prec=bf16 -repeat=1000
$EXE -m=700 -n=288 -e=1e-12 -v=1 -prec=bf16 -repeat=1000
$EXE -m=700 -n=344 -e=1e-12 -v=1 -prec=bf16 -repeat=1000
$EXE -m=700 -n=376 -e=1e-12 -v=1 -prec=bf16 -repeat=1000
$EXE -m=700 -n=448 -e=1e-12 -v=1 -prec=bf16 -repeat=1000
$EXE -m=700 -n=512 -e=1e-12 -v=1 -prec=bf16 -repeat=1000
$EXE -m=700 -n=924 -e=1e-12 -v=1 -prec=bf16 -repeat=1000
$EXE -m=700 -n=1024 -e=1e-12 -v=1 -prec=bf16 -repeat=1000
$EXE -m=700 -n=1078 -e=1e-12 -v=1 -prec=bf16 -repeat=1000
$EXE -m=700 -n=1996 -e=1e-12 -v=1 -prec=bf16 -repeat=1000
$EXE -m=700 -n=4080 -e=1e-12 -v=1 -prec=bf16 -repeat=1000
$EXE -m=700 -n=80 -e=1e-12 -v=1 -prec=fp16 -repeat=1000
$EXE -m=700 -n=128 -e=1e-12 -v=1 -prec=fp16 -repeat=1000
$EXE -m=700 -n=144 -e=1e-12 -v=1 -prec=fp16 -repeat=1000
$EXE -m=700 -n=168 -e=1e-12 -v=1 -prec=fp16 -repeat=1000
$EXE -m=700 -n=184 -e=1e-12 -v=1 -prec=fp16 -repeat=1000
$EXE -m=700 -n=256 -e=1e-12 -v=1 -prec=fp16 -repeat=1000
$EXE -m=700 -n=288 -e=1e-12 -v=1 -prec=fp16 -repeat=1000
$EXE -m=700 -n=344 -e=1e-12 -v=1 -prec=fp16 -repeat=1000
$EXE -m=700 -n=376 -e=1e-12 -v=1 -prec=fp16 -repeat=1000
$EXE -m=700 -n=448 -e=1e-12 -v=1 -prec=fp16 -repeat=1000
$EXE -m=700 -n=512 -e=1e-12 -v=1 -prec=fp16 -repeat=1000
$EXE -m=700 -n=924 -e=1e-12 -v=1 -prec=fp16 -repeat=1000
$EXE -m=700 -n=1024 -e=1e-12 -v=1 -prec=fp16 -repeat=1000
$EXE -m=700 -n=1078 -e=1e-12 -v=1 -prec=fp16 -repeat=1000
$EXE -m=700 -n=1996 -e=1e-12 -v=1 -prec=fp16 -repeat=1000
$EXE -m=700 -n=4080 -e=1e-12 -v=1 -prec=fp16 -repeat=1000
\ No newline at end of file
example/ck_tile/12_smoothquant/script/smoke_test.sh 0 → 100755
View file @ cd4d4629
#!/bin/sh
EXE="$(find . -name tile_smoothquant -type f | head -n 1)"
for pr_i in "fp16" "bf16" ; do
$EXE -prec=$pr_i -m=99 -n=13
$EXE -prec=$pr_i -m=17 -n=16
$EXE -prec=$pr_i -m=1 -n=100
$EXE -prec=$pr_i -m=4 -n=128
$EXE -prec=$pr_i -m=80 -n=127
$EXE -prec=$pr_i -m=22 -n=255 -stride=256
$EXE -prec=$pr_i -m=7 -n=599
$EXE -prec=$pr_i -m=19 -n=512
$EXE -prec=$pr_i -m=33 -n=313 -stride=1000
$EXE -prec=$pr_i -m=11 -n=510
$EXE -prec=$pr_i -m=171 -n=676 -stride=818
$EXE -prec=$pr_i -m=91 -n=636
$EXE -prec=$pr_i -m=12 -n=768 -stride=800
$EXE -prec=$pr_i -m=100 -n=766 -stride=812
$EXE -prec=$pr_i -m=31 -n=1024
$EXE -prec=$pr_i -m=64 -n=1000 -stride=1004
$EXE -prec=$pr_i -m=8 -n=1501
$EXE -prec=$pr_i -m=3 -n=1826
$EXE -prec=$pr_i -m=5 -n=2040
$EXE -prec=$pr_i -m=7 -n=2734
$EXE -prec=$pr_i -m=1 -n=3182
$EXE -prec=$pr_i -m=9 -n=4096
$EXE -prec=$pr_i -m=3 -n=8192
$EXE -prec=$pr_i -m=1 -n=10547
$EXE -prec=$pr_i -m=3 -n=17134
done
example/ck_tile/12_smoothquant/smoothquant.cpp 0 → 100644
View file @ cd4d4629
#include "ck_tile/host.hpp"
#include "smoothquant.hpp"
#include <cstring>
// 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);
}
auto create_args(int argc, char* argv[])
{
ck_tile::ArgParser arg_parser;
arg_parser.insert("m", "3328", "m dimension")
.insert("n", "4096", "n dimension")
.insert("x_stride", "-1", "input stride per row, if -1 then equal to n")
.insert("y_stride", "-1", "output stride per row, if -1 then equal to n")
.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 m = arg_parser.get_int("m");
ck_tile::index_t n = arg_parser.get_int("n");
ck_tile::index_t x_stride = arg_parser.get_int("x_stride");
if(x_stride < 0)
x_stride = n;
ck_tile::index_t y_stride = arg_parser.get_int("y_stride");
if(y_stride < 0)
y_stride = n;
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(x_stride >= n);
using TypeConfig = SmoothquantTypeConfig<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({m, n}, {x_stride, 1});
ck_tile::HostTensor<XScaleDataType> xscale_host({n});
ck_tile::HostTensor<YScaleDataType> yscale_host_ref({m}, {1});
ck_tile::HostTensor<YScaleDataType> yscale_host_dev({m}, {1});
ck_tile::HostTensor<QYDataType> qy_host_ref({m, n}, {y_stride, 1});
ck_tile::HostTensor<QYDataType> qy_host_dev({m, n}, {y_stride, 1});
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 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());
std::cout << "[" << data_type << "]"
<< " m:" << m << ", n:" << n << ", x_stride:" << x_stride << ", y_stride:" << y_stride
<< std::flush;
smoothquant_traits traits{data_type};
smoothquant_args args{x_buf.GetDeviceBuffer(),
xscale_buf.GetDeviceBuffer(),
yscale_buf.GetDeviceBuffer(),
qy_buf.GetDeviceBuffer(),
m,
n,
x_stride,
y_stride};
float ave_time = smoothquant(
traits, args, ck_tile::stream_config{nullptr, true, kname ? 1 : 0, warmup, repeat});
std::size_t num_byte = sizeof(XDataType) * m * n + sizeof(XScaleDataType) * n +
sizeof(YScaleDataType) * m + sizeof(QYDataType) * m * n;
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({m, n}, {y_stride, 1});
// smooth outlier
{
auto f = [&](auto n_) {
auto v_xscale = ck_tile::type_convert<ComputeDataType>(xscale_host(n_));
for(int m_ = 0; m_ < m; ++m_)
{
auto v_x = ck_tile::type_convert<ComputeDataType>(x_host(m_, n_));
y_host(m_, n_) = v_x * v_xscale;
}
};
ck_tile::make_ParallelTensorFunctor(f, xscale_host.get_element_space_size())(
std::thread::hardware_concurrency());
}
// yscale
{
ck_tile::HostTensor<YDataType> y_rowwise_amax_host({m});
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(y_stride == n)
{
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 < m; i_r++)
{
std::vector<QYDataType> qy_host_dev_row(qy_host_dev.begin() + i_r * y_stride,
qy_host_dev.begin() + i_r * y_stride +
n);
std::vector<QYDataType> qy_host_ref_row(qy_host_ref.begin() + i_r * y_stride,
qy_host_ref.begin() + i_r * y_stride +
n);
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/12_smoothquant/smoothquant.hpp 0 → 100644
View file @ cd4d4629
// 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 SmoothquantTypeConfig;
template <>
struct SmoothquantTypeConfig<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 SmoothquantTypeConfig<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 smoothquant_args : public ck_tile::SmoothquantHostArgs
{
};
// 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 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 smoothquant_(const ck_tile::stream_config& s, smoothquant_args a);
// This is the public API, will be generated by script
struct smoothquant_traits
{
std::string data_type;
};
float smoothquant(smoothquant_traits, smoothquant_args, const ck_tile::stream_config&);
example/ck_tile/13_moe_sorting/CMakeLists.txt 0 → 100644
View file @ cd4d4629
add_executable(tile_example_moe_sorting EXCLUDE_FROM_ALL moe_sorting.cpp moe_sorting_api.cpp)
target_include_directories(tile_example_moe_sorting PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/)
set(EXAMPLE_MOE_SORTING_COMPILE_OPTIONS)
# NOTE: we turn off undefined-func-template to let source compile without explicit declare function specializations
list(APPEND EXAMPLE_MOE_SORTING_COMPILE_OPTIONS -Wno-undefined-func-template -Wno-float-equal)
# list(APPEND EXAMPLE_MOE_SORTING_COMPILE_OPTIONS -v --save-temps -Wno-gnu-line-marker)
target_compile_options(tile_example_moe_sorting PRIVATE ${EXAMPLE_MOE_SORTING_COMPILE_OPTIONS})
example/ck_tile/13_moe_sorting/README.md 0 → 100644
View file @ cd4d4629
# moe-sorting
This folder contains example for moe-sorting kernel using ck_tile tile-programming implementation. This kernel is often used in Moe model, before launching the fused-moe-gemm block. The input&weight is a `token*topk` 2d matrix. The op rearange the input weight ids into different experts and feed into fuse moe gemm kernel.
## build
```
# in the root of ck_tile
mkdir build && cd build
sh ../script/cmake-ck-dev.sh ../ <arch> # you can replace this <arch> to gfx90a, gfx942...
make tile_example_moe_sorting -j
```
This will result in an executable `build/bin/tile_example_moe_sorting`
## example
```
args:
-v weather do CPU validation or not (default:1)
-pr_i index data type. (currently only fp32 supported now) (default:int32)
-pr_w output weight data type(currently only fp32 supported now) (default:fp32)
-t number of input tokens (default:32)
-e number of experts (default:8)
-k topk (default:2)
-st_i row stride of input, -1 means same as experts (default:-1)
-seed seed to be used, -1 means random every time (default:-1)
-kname when set to 1 it will print kernel name (default:0)
```
example/ck_tile/13_moe_sorting/moe_sorting.cpp 0 → 100644
View file @ cd4d4629
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include <set>
#include <vector>
#include <iostream>
#include <numeric>
#include <cassert>
#include <cstdlib>
#include <iostream>
#include <time.h>
#include <unordered_set>
#include "ck_tile/core.hpp"
#include "ck_tile/ops/reduce.hpp"
#include "moe_sorting_api.hpp"
auto create_args(int argc, char* argv[])
{
ck_tile::ArgParser arg_parser;
arg_parser.insert("v", "1", "weather do CPU validation or not")
.insert("pr_i", "int32", "index data type. (currently only int32 supported now)")
.insert("pr_w", "fp32", "output weight data type(currently only fp32 supported now)")
.insert("t", "128", "number of input tokens")
.insert("e", "8", "number of num_experts")
.insert("k", "4", "topk")
.insert("unit", "32", "unit_size")
.insert("moe_buf_size", "0", "moe_buf_size")
.insert("seed", "-1", "seed to be used, -1 means random every time")
.insert("kname", "0", "when set to 1 it will print kernel name")
.insert("warmup", "5", "number of iterations before benchmark the kernel")
.insert("repeat", "20", "number of iterations to benchmark the kernel");
bool result = arg_parser.parse(argc, argv);
return std::make_tuple(result, arg_parser);
}
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;
}
}
template <typename WeightType, typename IndexType = ck_tile::index_t>
bool test_moe_sorting(ck_tile::ArgParser args)
{
int validate = args.get_int("v");
std::string index_prec = args.get_str("pr_i");
std::string weight_prec = args.get_str("pr_w");
int tokens = args.get_int("t");
int num_experts = args.get_int("e");
int topk = args.get_int("k");
int seed = args.get_int("seed");
int unit_size = args.get_int("unit");
int moe_buf_size = args.get_int("moe_buf_size");
int kname = args.get_int("kname");
int warmup = args.get_int("warmup");
int repeat = args.get_int("repeat");
int max_output_ids =
ck_tile::integer_least_multiple(topk * tokens + num_experts * unit_size - topk, unit_size);
if(seed < 0)
{
seed = std::time(nullptr);
}
if(topk > num_experts)
{
printf("topk:%d value should be smaller than, or equal to number of num_experts:%d\n",
topk,
num_experts);
return false;
}
// tokens already considered batch size
ck_tile::HostTensor<IndexType> topk_ids_host({tokens, topk}, {topk, 1});
ck_tile::HostTensor<WeightType> weights_host({tokens, topk}, {topk, 1});
ck_tile::HostTensor<IndexType> sorted_ids_host({max_output_ids}, {1});
ck_tile::HostTensor<WeightType> sorted_weights_host({max_output_ids}, {1});
ck_tile::HostTensor<IndexType> sorted_expert_ids_host({max_output_ids / unit_size}, {1});
ck_tile::HostTensor<IndexType> sorted_id_cnt_host({1}, {1});
ck_tile::HostTensor<float> moe_buf_host({moe_buf_size});
ck_tile::FillUniformDistribution<WeightType>{-.5f, .5f}(weights_host);
ck_tile::FillUniformDistribution<WeightType>{-.5f, .5f}(moe_buf_host);
topid_unique_gen<IndexType>(topk_ids_host.mData, tokens, topk, num_experts, seed);
ck_tile::DeviceMem topk_ids_dev(topk_ids_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem weights_dev(weights_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem sorted_ids_dev(sorted_ids_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem sorted_weights_dev(sorted_weights_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem sorted_expert_ids_dev(
sorted_expert_ids_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem sorted_id_cnt_dev(sorted_id_cnt_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem moe_buf_dev(moe_buf_host.get_element_space_size_in_bytes());
topk_ids_dev.ToDevice(topk_ids_host.data());
weights_dev.ToDevice(weights_host.data());
if(moe_buf_size > 0)
{
moe_buf_dev.ToDevice(moe_buf_host.data());
}
moe_sorting_trait trait{index_prec, weight_prec};
moe_sorting_args karg{topk_ids_dev.GetDeviceBuffer(),
weights_dev.GetDeviceBuffer(),
sorted_ids_dev.GetDeviceBuffer(),
sorted_weights_dev.GetDeviceBuffer(),
sorted_expert_ids_dev.GetDeviceBuffer(),
sorted_id_cnt_dev.GetDeviceBuffer(),
moe_buf_size > 0 ? moe_buf_dev.GetDeviceBuffer() : nullptr,
tokens,
unit_size,
num_experts,
topk,
static_cast<ck_tile::index_t>(moe_buf_size * sizeof(float))};
ck_tile::stream_config sc{nullptr,
true,
/* log_level = */ (kname ? 1 : 0),
warmup,
repeat};
auto ms = moe_sorting(trait, karg, sc);
printf("[%s|%s]tokens:%d, num_experts:%d, topk:%d, ms:%f , ",
index_prec.c_str(),
weight_prec.c_str(),
tokens,
num_experts,
topk,
ms);
if(ms < 0)
printf("not supported\n");
fflush(stdout);
if(ms < 0)
{
return false;
}
sorted_ids_dev.FromDevice(sorted_ids_host.data());
sorted_weights_dev.FromDevice(sorted_weights_host.data());
sorted_expert_ids_dev.FromDevice(sorted_expert_ids_host.data());
sorted_id_cnt_dev.FromDevice(sorted_id_cnt_host.data());
if(moe_buf_size > 0)
{
moe_buf_dev.FromDevice(moe_buf_host.data());
}
bool rtn = true;
if(validate)
{
ck_tile::HostTensor<IndexType> sorted_ids_ref({max_output_ids}, {1});
ck_tile::HostTensor<WeightType> sorted_weights_ref({max_output_ids}, {1});
ck_tile::HostTensor<IndexType> sorted_expert_ids_ref({max_output_ids / unit_size}, {1});
int32_t ref_total_tokens_post_pad = 0;
ck_tile::reference_moe_sorting<WeightType, IndexType>(topk_ids_host,
weights_host,
sorted_ids_ref,
sorted_weights_ref,
sorted_expert_ids_ref,
ref_total_tokens_post_pad,
num_experts,
unit_size);
rtn &= ck_tile::check_err(
sorted_ids_host, sorted_ids_ref, std::string("OUT Error: Incorrect ids!"), 1e-6, 1e-6);
rtn &= ck_tile::check_err(sorted_weights_host,
sorted_weights_ref,
std::string("OUT Error: Incorrect w!"),
1e-6,
1e-6);
rtn &= ck_tile::check_err(sorted_expert_ids_host,
sorted_expert_ids_ref,
std::string("OUT Error: Incorrect eid!"),
1e-6,
1e-6);
if(moe_buf_size)
{
ck_tile::HostTensor<WeightType> moe_buf_ref({moe_buf_size});
rtn &= ck_tile::check_err(
moe_buf_host, moe_buf_ref, std::string("OUT Error: Incorrect zero buf!"), 0, 0);
}
rtn &= ref_total_tokens_post_pad == sorted_id_cnt_host.mData[0];
}
printf("valid:%s\n", rtn ? "y" : "n");
fflush(stdout);
return rtn;
}
int main(int argc, char** argv)
{
auto [result, args] = create_args(argc, argv);
if(!result)
return -1;
std::string index_prec = args.get_str("pr_i");
std::string weight_prec = args.get_str("pr_w");
bool r = true;
if(weight_prec.compare("fp32") == 0 && index_prec.compare("int32") == 0)
{
r &= test_moe_sorting<float, ck_tile::index_t>(args);
}
return r ? 0 : -1;
}
example/ck_tile/13_moe_sorting/moe_sorting_api.cpp 0 → 100644
View file @ cd4d4629
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "moe_sorting_api.hpp"
#define MOE_SORTING_DISPATCH_ETILE(unroll_num_, expert_tile_) \
constexpr ck_tile::index_t unroll_num = unroll_num_; \
constexpr ck_tile::index_t expert_tile = expert_tile_; \
using ms_problem = \
ck_tile::MoeSortingProblem<index_t, ms_weight_type, unroll_num, expert_tile>; \
using kernel = ck_tile::MoeSortingKernel<ms_problem>; \
auto kargs = kernel::MakeKargs(a); \
const dim3 grids = kernel::GridSize(a); \
const dim3 blocks = kernel::BlockSize(a); \
const auto lds_bytes = kernel::GetSmemSize(a); \
float ave_time = ck_tile::launch_kernel( \
s, ck_tile::make_kernel(kernel{}, grids, blocks, lds_bytes, kargs)); \
return ave_time;
#define MOE_SORTING_DISPATCH(unroll_num_) \
if(a.num_experts <= 8) \
{ \
MOE_SORTING_DISPATCH_ETILE(unroll_num_, 8) \
} \
else if(a.num_experts <= 16) \
{ \
MOE_SORTING_DISPATCH_ETILE(unroll_num_, 16) \
} \
else if(a.num_experts <= 32) \
{ \
MOE_SORTING_DISPATCH_ETILE(unroll_num_, 32) \
} \
else if(a.num_experts <= 64) \
{ \
MOE_SORTING_DISPATCH_ETILE(unroll_num_, 64) \
} \
else \
{ \
MOE_SORTING_DISPATCH_ETILE(unroll_num_, 0) \
}
float moe_sorting(moe_sorting_trait t, moe_sorting_args a, ck_tile::stream_config s)
{
if(t.weight_type == "fp32" && t.index_type == "int32")
{
if(a.num_experts > 127)
{
printf("lds size exceed, only support experts <127 \n");
return -1;
}
if(a.moe_buf_bytes % 16)
{
printf("buf set size %d unaligned, must be multiple of 16\n", a.moe_buf_bytes);
return -1;
}
using index_t = ck_tile::index_t;
using ms_weight_type = float;
index_t smem_io_unroll_num = ck_tile::integer_divide_ceil(a.tokens * a.topk, 64);
switch(smem_io_unroll_num)
{
case(1): {
MOE_SORTING_DISPATCH(1);
}
case(2): {
MOE_SORTING_DISPATCH(2);
}
case(3): {
MOE_SORTING_DISPATCH(3);
}
case(5): {
MOE_SORTING_DISPATCH(5);
}
case(6): {
MOE_SORTING_DISPATCH(6);
}
case(8): {
MOE_SORTING_DISPATCH(8);
}
case(10): {
MOE_SORTING_DISPATCH(10);
}
default: {
MOE_SORTING_DISPATCH(4);
}
}
}
return -1;
}
example/ck_tile/13_moe_sorting/moe_sorting_api.hpp 0 → 100644
View file @ cd4d4629
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <string>
#include "ck_tile/core.hpp"
#include "ck_tile/host.hpp"
#include "ck_tile/ops/fused_moe.hpp"
struct moe_sorting_trait
{
std::string index_type;
std::string weight_type; // currently always float
};
struct moe_sorting_args : public ck_tile::MoeSortingHostArgs
{
};
float moe_sorting(moe_sorting_trait t, moe_sorting_args a, ck_tile::stream_config s);
example/ck_tile/13_moe_sorting/script/smoke_test.sh 0 → 100644
View file @ cd4d4629
# #!/bin/sh
EXE=./build/bin/tile_example_moe_sorting
$EXE -t=80 -e=17 -moe_buf_size=16
$EXE -t=111 -e=117 -moe_buf_size=4
$EXE -t=1000 -e=55 -moe_buf_size=1024
$EXE -t=99 -e=120 -moe_buf_size=10244
$EXE -t=175 -e=64 -k=8
$EXE -t=65 -e=8 -k=2
$EXE -t=1 -e=25
$EXE -t=31 -e=19 -k=15
$EXE -t=81 -e=37 -k=7
$EXE -t=23 -e=1 -k=1
$EXE -t=127 -e=99 -k=19
$EXE -t=71 -e=11 -k=11
$EXE -t=1 -e=1 -k=1
$EXE -t=99 -e=2 -k=1
$EXE -t=333 -e=99 -k=13
$EXE -t=128 -e=32 -k=5 -moe_buf_size=262144
example/ck_tile/14_moe_smoothquant/CMakeLists.txt 0 → 100644
View file @ cd4d4629
function (add_moe_smoothquant_example TARGET_NAME MAIN_SRC)
message("adding ${TARGET_NAME}")
# not using add_example_executable() to add target, since we don't want this to have
# to be included in "make all/install/check"
add_executable(${TARGET_NAME} EXCLUDE_FROM_ALL ${MAIN_SRC})
target_include_directories(${TARGET_NAME} PRIVATE ${CMAKE_CURRENT_LIST_DIR})
foreach(source IN LISTS ARGN)
list(APPEND INSTANCE_SRCS ${source})
endforeach()
target_sources(${TARGET_NAME} PRIVATE ${INSTANCE_SRCS})
set(COMPILE_OPTIONS)
# NOTE: we turn off undefined-func-template to let source compile without explicit declare function specializations
list(APPEND COMPILE_OPTIONS -Wno-undefined-func-template -Wno-float-equal)
# list(APPEND COMPILE_OPTIONS -v --save-temps -Wno-gnu-line-marker)
target_compile_options(${TARGET_NAME} PRIVATE ${COMPILE_OPTIONS})
endfunction(add_moe_smoothquant_example TARGET_NAME MAIN_SRC)
file(GLOB INSTANCE_SRCS instances/*.cpp)
add_moe_smoothquant_example(tile_example_moe_smoothquant moe_smoothquant.cpp ${INSTANCE_SRCS})
example/ck_tile/14_moe_smoothquant/README.md 0 → 100644
View file @ cd4d4629
# moe-smoothquant
This folder contains example for moe-smoothquant using ck_tile tile-programming implementation.
![](misc/moe-sm.png)
Unlike standard smoothquant op, the input scale is from different expert `[expert, hidden]`, we need reuse the `topk-id` from previous `topk-softmax` and select the corresponding `expert` from current topk, and expand the output/per-token-scale by `topk`
## build
```
# in the root of ck_tile
mkdir build && cd build
sh ../script/cmake-ck-dev.sh ../ <arch> # you can replace this <arch> to gfx90a, gfx942...
make tile_example_moe_smoothquant -j
```
This will result in an executable `build/bin/tile_example_moe_smoothquant`
example/ck_tile/14_moe_smoothquant/instances/moe_smoothquant_bf16_n1024_instance.cpp 0 → 100644
View file @ cd4d4629
// 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
#if 0
template float moe_smoothquant_<trait_<ck_tile::bf16_t, 1, 2, 4, 64, 8, true, false>>(const S&, A);
template float moe_smoothquant_<trait_<ck_tile::bf16_t, 1, 4, 4, 64, 4, true, false>>(const S&, A);
template float moe_smoothquant_<trait_<ck_tile::bf16_t, 1, 8, 4, 64, 2, true, false>>(const S&, A);
template float moe_smoothquant_<trait_<ck_tile::bf16_t, 1, 16, 4, 64, 1, true, false>>(const S&, A);
template float moe_smoothquant_<trait_<ck_tile::bf16_t, 1, 1, 1, 256, 4, true, false>>(const S&, A);
#endif
template float moe_smoothquant_<trait_<ck_tile::bf16_t, 1, 1, 2, 128, 8, true, false>>(const S&, A);
template float moe_smoothquant_<trait_<ck_tile::bf16_t, 1, 2, 2, 128, 4, true, false>>(const S&, A);
template float moe_smoothquant_<trait_<ck_tile::bf16_t, 1, 4, 2, 128, 2, true, false>>(const S&, A);
template float moe_smoothquant_<trait_<ck_tile::bf16_t, 1, 4, 1, 256, 1, true, false>>(const S&, A);
// clang-format on
example/ck_tile/14_moe_smoothquant/instances/moe_smoothquant_bf16_n1536_instance.cpp 0 → 100644
View file @ cd4d4629
// 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::bf16_t, 1, 3, 4, 64, 8, true, false>>(const S&, A);
template float moe_smoothquant_<trait_<ck_tile::bf16_t, 1, 3, 2, 128, 4, true, false>>(const S&, A);
template float moe_smoothquant_<trait_<ck_tile::bf16_t, 1, 3, 1, 256, 2, true, false>>(const S&, A);
template float moe_smoothquant_<trait_<ck_tile::bf16_t, 1, 6, 1, 256, 1, true, false>>(const S&, A);
// clang-format on
example/ck_tile/14_moe_smoothquant/instances/moe_smoothquant_bf16_n2048_instance.cpp 0 → 100644
View file @ cd4d4629
// 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::bf16_t, 1, 1, 1, 256, 8, true, false>>(const S&, A);
template float moe_smoothquant_<trait_<ck_tile::bf16_t, 1, 2, 1, 256, 4, true, false>>(const S&, A);
template float moe_smoothquant_<trait_<ck_tile::bf16_t, 1, 4, 1, 256, 2, true, false>>(const S&, A);
template float moe_smoothquant_<trait_<ck_tile::bf16_t, 1, 8, 1, 256, 1, true, false>>(const S&, A);
// clang-format on
example/ck_tile/14_moe_smoothquant/instances/moe_smoothquant_bf16_n256_instance.cpp 0 → 100644
View file @ cd4d4629
// 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::bf16_t, 1, 1, 4, 64, 4, true, false>>(const S&, A);
template float moe_smoothquant_<trait_<ck_tile::bf16_t, 1, 2, 4, 64, 2, true, false>>(const S&, A);
template float moe_smoothquant_<trait_<ck_tile::bf16_t, 1, 4, 4, 64, 1, true, false>>(const S&, A);
// clang-format on
example/ck_tile/14_moe_smoothquant/instances/moe_smoothquant_bf16_n3072_instance.cpp 0 → 100644
View file @ cd4d4629
// 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::bf16_t, 1, 3, 1, 128, 8, true, false>>(const S&, A);
template float moe_smoothquant_<trait_<ck_tile::bf16_t, 1, 3, 1, 256, 4, true, false>>(const S&, A);
template float moe_smoothquant_<trait_<ck_tile::bf16_t, 1, 6, 1, 256, 2, true, false>>(const S&, A);
template float moe_smoothquant_<trait_<ck_tile::bf16_t, 1, 3, 1, 1024, 1, true, false>>(const S&, A);
// clang-format on
example/ck_tile/14_moe_smoothquant/instances/moe_smoothquant_bf16_n4096_instance.cpp 0 → 100644
View file @ cd4d4629
// 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::bf16_t, 1, 2, 1, 256, 8, true, false>>(const S&, A);
template float moe_smoothquant_<trait_<ck_tile::bf16_t, 1, 4, 1, 256, 4, true, false>>(const S&, A);
template float moe_smoothquant_<trait_<ck_tile::bf16_t, 1, 2, 1, 1024, 2, true, false>>(const S&, A);
template float moe_smoothquant_<trait_<ck_tile::bf16_t, 1, 4, 1, 1024, 1, true, false>>(const S&, A);
// clang-format on
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment