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Commit 667cd6ab authored by illsilin's avatar illsilin
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merge from public repo

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example/ck_tile/12_smoothquant/instances/smoothquant_fp16_n4096_tp_instance.cpp 0 → 100644
View file @ 667cd6ab
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "smoothquant_instance_common.hpp"
// clang-format off
// rm rn tm tn vn pd 2p
template float smoothquant_<trait_<ck_tile::fp16_t, 1, 2, 1, 256, 8, true, true>>(const S&, A);
template float smoothquant_<trait_<ck_tile::fp16_t, 1, 4, 1, 256, 4, true, true>>(const S&, A);
template float smoothquant_<trait_<ck_tile::fp16_t, 1, 2, 1, 1024, 2, true, true>>(const S&, A);
template float smoothquant_<trait_<ck_tile::fp16_t, 1, 4, 1, 1024, 1, true, true>>(const S&, A);
// clang-format on
example/ck_tile/12_smoothquant/instances/smoothquant_fp16_n512_instance.cpp 0 → 100644
View file @ 667cd6ab
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "smoothquant_instance_common.hpp"
// clang-format off
// rm rn tm tn vn pd 2p
template float smoothquant_<trait_<ck_tile::fp16_t, 1, 1, 4, 64, 8, true , false>>(const S&, A);
template float smoothquant_<trait_<ck_tile::fp16_t, 1, 2, 4, 64, 4, true , false>>(const S&, A);
template float smoothquant_<trait_<ck_tile::fp16_t, 1, 4, 4, 64, 2, true , false>>(const S&, A);
template float smoothquant_<trait_<ck_tile::fp16_t, 1, 8, 4, 64, 1, true , false>>(const S&, A);
// clang-format on
example/ck_tile/12_smoothquant/instances/smoothquant_fp16_n64_n128_instance.cpp 0 → 100644
View file @ 667cd6ab
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "smoothquant_instance_common.hpp"
// clang-format off
// rm rn tm tn vn pd 2p
template float smoothquant_<trait_<ck_tile::fp16_t, 1, 1, 4, 64, 1, true, false>>(const S&, A);
template float smoothquant_<trait_<ck_tile::fp16_t, 1, 1, 4, 64, 2, true, false>>(const S&, A);
template float smoothquant_<trait_<ck_tile::fp16_t, 1, 2, 4, 64, 1, true, false>>(const S&, A);
// clang-format on
example/ck_tile/12_smoothquant/instances/smoothquant_fp16_n768_instance.cpp 0 → 100644
View file @ 667cd6ab
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "smoothquant_instance_common.hpp"
// clang-format off
// rm rn tm tn vn pd 2p
template float smoothquant_<trait_<ck_tile::fp16_t, 1, 3, 4, 64, 4, true , false>>(const S&, A);
template float smoothquant_<trait_<ck_tile::fp16_t, 1, 6, 4, 64, 2, true , false>>(const S&, A);
template float smoothquant_<trait_<ck_tile::fp16_t, 1, 12, 4, 64, 1, true , false>>(const S&, A);
// clang-format on
example/ck_tile/12_smoothquant/instances/smoothquant_fwd_api.cpp 0 → 100644
View file @ 667cd6ab
// 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 @ 667cd6ab
// 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 @ 667cd6ab
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
$EXE -m=700 -n=4080 -e=1e-12 -v=1 -prec=fp16 -repeat=1000
\ No newline at end of file
example/ck_tile/12_smoothquant/script/smoke_test.sh 0 → 100755
View file @ 667cd6ab
#!/bin/sh
EXE="$(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 @ 667cd6ab
#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("stride", "-1", "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 stride = arg_parser.get_int("stride");
if(stride < 0)
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(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}, {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}, {stride, 1});
ck_tile::HostTensor<QYDataType> qy_host_dev({m, n}, {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 << ", stride:" << 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,
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}, {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(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 * stride,
qy_host_dev.begin() + i_r * stride + n);
std::vector<QYDataType> qy_host_ref_row(qy_host_ref.begin() + i_r * stride,
qy_host_ref.begin() + i_r * 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 @ 667cd6ab
// 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/CMakeLists.txt
View file @ 667cd6ab
...@@ -11,3 +11,4 @@ add_subdirectory(06_permute) ...@@ -11,3 +11,4 @@ add_subdirectory(06_permute)
add_subdirectory(09_topk_softmax) add_subdirectory(09_topk_softmax)
add_subdirectory(10_rmsnorm2d) add_subdirectory(10_rmsnorm2d)
add_subdirectory(11_add_rmsnorm2d_rdquant) add_subdirectory(11_add_rmsnorm2d_rdquant)
add_subdirectory(12_smoothquant)
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_data_multiple_d_xdl_cshuffle_v1.hpp
View file @ 667cd6ab
...@@ -93,12 +93,12 @@ __global__ void ...@@ -93,12 +93,12 @@ __global__ void
__builtin_amdgcn_readfirstlane(get_grid_size() / batch_count); __builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch); const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
const long_index_t a_batch_offset = const long_index_t a_batch_offset = amd_wave_read_first_lane(
amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)); static_cast<long_index_t>(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)));
const long_index_t b_batch_offset = const long_index_t b_batch_offset = amd_wave_read_first_lane(
amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx)); static_cast<long_index_t>(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx)));
const long_index_t e_batch_offset = const long_index_t e_batch_offset = amd_wave_read_first_lane(
amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetEPtrOffset(g_idx)); static_cast<long_index_t>(compute_ptr_offset_of_batch.GetEPtrOffset(g_idx)));
const auto ds_batch_offset = compute_ptr_offset_of_batch.GetDsPtrOffset(g_idx); const auto ds_batch_offset = compute_ptr_offset_of_batch.GetDsPtrOffset(g_idx);
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_two_stage_xdl_cshuffle.hpp
View file @ 667cd6ab
...@@ -60,12 +60,12 @@ __global__ void ...@@ -60,12 +60,12 @@ __global__ void
const index_t g_idx = __builtin_amdgcn_readfirstlane(blockIdx.z * NumGroupsToMerge); const index_t g_idx = __builtin_amdgcn_readfirstlane(blockIdx.z * NumGroupsToMerge);
const index_t k_idx = __builtin_amdgcn_readfirstlane(blockIdx.y * num_k_per_block); const index_t k_idx = __builtin_amdgcn_readfirstlane(blockIdx.y * num_k_per_block);
const long_index_t a_batch_offset = const long_index_t a_batch_offset = amd_wave_read_first_lane(
amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)); static_cast<long_index_t>(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)));
const long_index_t b_batch_offset = const long_index_t b_batch_offset = amd_wave_read_first_lane(
amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx)); static_cast<long_index_t>(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx)));
const long_index_t e_batch_offset = const long_index_t e_batch_offset = amd_wave_read_first_lane(
amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetEPtrOffset(g_idx)); static_cast<long_index_t>(compute_ptr_offset_of_batch.GetEPtrOffset(g_idx)));
__shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()]; __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
...@@ -117,12 +117,12 @@ __global__ void ...@@ -117,12 +117,12 @@ __global__ void
const index_t g_idx = __builtin_amdgcn_readfirstlane(blockIdx.z * NumGroupsToMerge); const index_t g_idx = __builtin_amdgcn_readfirstlane(blockIdx.z * NumGroupsToMerge);
const index_t k_idx = __builtin_amdgcn_readfirstlane(blockIdx.y * num_k_per_block); const index_t k_idx = __builtin_amdgcn_readfirstlane(blockIdx.y * num_k_per_block);
const long_index_t a_batch_offset = const long_index_t a_batch_offset = amd_wave_read_first_lane(
amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)); static_cast<long_index_t>(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)));
const long_index_t b_batch_offset = const long_index_t b_batch_offset = amd_wave_read_first_lane(
amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx)); static_cast<long_index_t>(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx)));
const long_index_t e_batch_offset = const long_index_t e_batch_offset = amd_wave_read_first_lane(
amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetEPtrOffset(g_idx)); static_cast<long_index_t>(compute_ptr_offset_of_batch.GetEPtrOffset(g_idx)));
// Pass two lds pointer is the key to tell compiler that ds_read/write // Pass two lds pointer is the key to tell compiler that ds_read/write
// operate on different lds chunk at same time without order dependecy // operate on different lds chunk at same time without order dependecy
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_dl_multiple_d_nhwc_kyxc_nhwk.hpp
View file @ 667cd6ab
...@@ -98,12 +98,12 @@ __global__ void ...@@ -98,12 +98,12 @@ __global__ void
__builtin_amdgcn_readfirstlane(get_grid_size() / batch_count); __builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch); const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
const long_index_t a_batch_offset = const long_index_t a_batch_offset = amd_wave_read_first_lane(
amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)); static_cast<long_index_t>(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)));
const long_index_t b_batch_offset = const long_index_t b_batch_offset = amd_wave_read_first_lane(
amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx)); static_cast<long_index_t>(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx)));
const long_index_t c_batch_offset = const long_index_t c_batch_offset = amd_wave_read_first_lane(
amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetEPtrOffset(g_idx)); static_cast<long_index_t>(compute_ptr_offset_of_batch.GetEPtrOffset(g_idx)));
const auto ds_batch_offset = compute_ptr_offset_of_batch.GetDsPtrOffset(g_idx); const auto ds_batch_offset = compute_ptr_offset_of_batch.GetDsPtrOffset(g_idx);
......
include/ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_d_wmma_cshuffle.hpp
View file @ 667cd6ab
...@@ -60,12 +60,12 @@ __global__ void ...@@ -60,12 +60,12 @@ __global__ void
__builtin_amdgcn_readfirstlane(get_grid_size() / batch_count); __builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch); const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
const long_index_t a_batch_offset = const long_index_t a_batch_offset = amd_wave_read_first_lane(
amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)); static_cast<long_index_t>(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)));
const long_index_t b_batch_offset = const long_index_t b_batch_offset = amd_wave_read_first_lane(
amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx)); static_cast<long_index_t>(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx)));
const long_index_t e_batch_offset = const long_index_t e_batch_offset = amd_wave_read_first_lane(
amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetEPtrOffset(g_idx)); static_cast<long_index_t>(compute_ptr_offset_of_batch.GetEPtrOffset(g_idx)));
const auto ds_batch_offset = compute_ptr_offset_of_batch.GetDsPtrOffset(g_idx); const auto ds_batch_offset = compute_ptr_offset_of_batch.GetDsPtrOffset(g_idx);
...@@ -155,12 +155,12 @@ __global__ void ...@@ -155,12 +155,12 @@ __global__ void
__builtin_amdgcn_readfirstlane(get_grid_size() / batch_count); __builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch); const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
const long_index_t a_batch_offset = const long_index_t a_batch_offset = amd_wave_read_first_lane(
amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)); static_cast<long_index_t>(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)));
const long_index_t b_batch_offset = const long_index_t b_batch_offset = amd_wave_read_first_lane(
amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx)); static_cast<long_index_t>(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx)));
const long_index_t e_batch_offset = const long_index_t e_batch_offset = amd_wave_read_first_lane(
amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetEPtrOffset(g_idx)); static_cast<long_index_t>(compute_ptr_offset_of_batch.GetEPtrOffset(g_idx)));
const auto ds_batch_offset = compute_ptr_offset_of_batch.GetDsPtrOffset(g_idx); const auto ds_batch_offset = compute_ptr_offset_of_batch.GetDsPtrOffset(g_idx);
......
include/ck/tensor_operation/gpu/grid/gridwise_tensor_rearrange.hpp
View file @ 667cd6ab
...@@ -121,10 +121,10 @@ struct GridwiseTensorRearrange ...@@ -121,10 +121,10 @@ struct GridwiseTensorRearrange
__builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch); __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
// Global Memory // Global Memory
const index_t a_batch_offset = const index_t a_batch_offset = __builtin_amdgcn_readfirstlane(
__builtin_amdgcn_readfirstlane(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)); static_cast<long_index_t>(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)));
const index_t c_batch_offset = const index_t c_batch_offset = __builtin_amdgcn_readfirstlane(
__builtin_amdgcn_readfirstlane(compute_ptr_offset_of_batch.GetCPtrOffset(g_idx)); static_cast<long_index_t>(compute_ptr_offset_of_batch.GetCPtrOffset(g_idx)));
const auto in_global_buf = make_dynamic_buffer<AddressSpaceEnum::Global>( const auto in_global_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_in_global + a_batch_offset, in_grid_desc.GetElementSpaceSize()); p_in_global + a_batch_offset, in_grid_desc.GetElementSpaceSize());
......
include/ck_tile/ops/add_rmsnorm2d_rdquant.hpp
View file @ 667cd6ab
...@@ -4,7 +4,6 @@ ...@@ -4,7 +4,6 @@
#pragma once #pragma once
#include "ck_tile/ops/add_rmsnorm2d_rdquant/kernel/add_rmsnorm2d_rdquant_fwd_kernel.hpp" #include "ck_tile/ops/add_rmsnorm2d_rdquant/kernel/add_rmsnorm2d_rdquant_fwd_kernel.hpp"
#include "ck_tile/ops/add_rmsnorm2d_rdquant/kernel/add_rmsnorm2d_rdquant_fwd_shape.hpp"
#include "ck_tile/ops/add_rmsnorm2d_rdquant/pipeline/add_rmsnorm2d_rdquant_fwd_pipeline_default_policy.hpp" #include "ck_tile/ops/add_rmsnorm2d_rdquant/pipeline/add_rmsnorm2d_rdquant_fwd_pipeline_default_policy.hpp"
#include "ck_tile/ops/add_rmsnorm2d_rdquant/pipeline/add_rmsnorm2d_rdquant_fwd_pipeline_one_pass.hpp" #include "ck_tile/ops/add_rmsnorm2d_rdquant/pipeline/add_rmsnorm2d_rdquant_fwd_pipeline_one_pass.hpp"
#include "ck_tile/ops/add_rmsnorm2d_rdquant/pipeline/add_rmsnorm2d_rdquant_fwd_pipeline_problem.hpp" #include "ck_tile/ops/add_rmsnorm2d_rdquant/pipeline/add_rmsnorm2d_rdquant_fwd_pipeline_problem.hpp"
......
include/ck_tile/ops/add_rmsnorm2d_rdquant/kernel/add_rmsnorm2d_rdquant_fwd_kernel.hpp
View file @ 667cd6ab
...@@ -9,15 +9,16 @@ ...@@ -9,15 +9,16 @@
namespace ck_tile { namespace ck_tile {
// host side args // host side args
// X = A + B, Y = Rmsnorm2d(X), QY = RowwiseDynamicQuant(Y) = SaturateCast(Y / YScale)
struct AddRmsnorm2dRdquantFwdHostArgs struct AddRmsnorm2dRdquantFwdHostArgs
{ {
const void* p_a; const void* p_a; // [m ,n], input, fp16/bf16
const void* p_b; const void* p_b; // [m ,n], input, fp16/bf16
const void* p_gamma; const void* p_gamma; // [1, n], gamma, prec same as input
void* p_x; void* p_x; // [m, n], output, p_a + p_b, fp16/bf16
void* p_yscale; void* p_yscale; // [m, 1], output, rowwise quant scale (amax / 127) of reuslt of rmsnorm2d(x)
void* p_qy; void* p_qy; // [m, n], output, result of quant tensor of rmsnorm2d(x) int8
float epsilon; float epsilon;
...@@ -90,7 +91,7 @@ struct AddRmsnorm2dRdquantFwd ...@@ -90,7 +91,7 @@ struct AddRmsnorm2dRdquantFwd
CK_TILE_HOST static constexpr auto GridSize(const Hargs& hargs) CK_TILE_HOST static constexpr auto GridSize(const Hargs& hargs)
{ {
return integer_divide_ceil(hargs.m, Block_M); return dim3(integer_divide_ceil(hargs.m, Block_M));
} }
CK_TILE_HOST static constexpr auto BlockSize() { return Problem::BlockShape::BlockSize; } CK_TILE_HOST static constexpr auto BlockSize() { return Problem::BlockShape::BlockSize; }
...@@ -170,7 +171,7 @@ struct AddRmsnorm2dRdquantFwd ...@@ -170,7 +171,7 @@ struct AddRmsnorm2dRdquantFwd
number<1>{}); number<1>{});
const auto tmp2_ = const auto tmp2_ =
pad_tensor_view(tmp_, make_tuple(number<Block_N>{}), sequence<kPadM>{}); pad_tensor_view(tmp_, make_tuple(number<Block_N>{}), sequence<kPadN>{});
return make_tile_window(tmp2_, make_tuple(number<Block_N>{}), {0}); return make_tile_window(tmp2_, make_tuple(number<Block_N>{}), {0});
}(); }();
......
include/ck_tile/ops/add_rmsnorm2d_rdquant/kernel/add_rmsnorm2d_rdquant_fwd_shape.hpp deleted 100644 → 0
View file @ 7d50244e
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
namespace ck_tile {
/*
// clang-format off
4-level descriptor: BlockTile-> WarpPerBlock-> WarpTile-> Vector
Block_N (Warp_N * WarpPerBlock_N * Repeat_N )
+<----------------------< Repeat_N(2)>--------------------->+
| |
+<-- <WarpPerBlock_N(2)> -->+
Warp_N
+--------------+--------------+--------------+--------------+----+----------------+
Warp_M | wrap_0 | wrap_1 | | ^ ^
+--------------+--------------+ | <WarpPerBlock_M(2)> |
| wrap_2 | wrap_3 | | v
+--------------+--------------+--------------+--------------+----+ Block_M
| | |
+ + |
| | | v
+--------------+--------------+--------------+--------------+ +
each Warp-tile (e.g 16 thrd per row)
Vector_N (contiguous pixels each thrd holds along N, or vector size)
+-----------+-----------+-----------+-----------+-----------+
| thrd_0 | thrd_1 | thrd_2 | thrd_3 | ... Vector_M
+-----------+-----------+-----------+-----------+-----------+
| thrd_16 | thrd_17 | thrd_18 | thrd_19 | ...
+-----------+-----------+-----------+-----------+-----------+
// clang-format on
*/
template <typename BlockTile_, // block size, seq<M, N>
typename WarpPerBlock_, // num warps along seq<M, N>
typename WarpTile_, // warp size, seq<M, N>
typename Vector_, // contiguous pixels(vector size) along seq<M, N>
index_t BlockSize_ =
warpSize* reduce_on_sequence(WarpPerBlock_{}, multiplies{}, number<1>{})>
struct AddRmsnorm2dRdquantShape
{
// block size
static constexpr index_t Block_M = BlockTile_::at(number<0>{});
static constexpr index_t Block_N = BlockTile_::at(number<1>{});
// num warps along seq<M, N>, within each block
static constexpr index_t WarpPerBlock_M = WarpPerBlock_::at(number<0>{});
static constexpr index_t WarpPerBlock_N = WarpPerBlock_::at(number<1>{});
// warp size
static constexpr index_t Warp_M = WarpTile_::at(number<0>{});
static constexpr index_t Warp_N = WarpTile_::at(number<1>{});
static_assert(Block_M % (WarpPerBlock_M * Warp_M) == 0);
static_assert(Block_N % (WarpPerBlock_N * Warp_N) == 0);
// repeat of each thread along seq<M, N>
static constexpr index_t Repeat_M = Block_M / (WarpPerBlock_M * Warp_M);
static constexpr index_t Repeat_N = Block_N / (WarpPerBlock_N * Warp_N);
// vector size along seq<M, N>
static constexpr index_t Vector_M = Vector_::at(number<0>{});
static constexpr index_t Vector_N = Vector_::at(number<1>{});
static_assert(Warp_M % Vector_M == 0);
static_assert(Warp_N % Vector_N == 0);
// num of threads along seq<M, N>, within each warp
static constexpr index_t ThreadPerWarp_M = Warp_M / Vector_M;
static constexpr index_t ThreadPerWarp_N = Warp_N / Vector_N;
static constexpr index_t BlockSize = BlockSize_;
};
} // namespace ck_tile
include/ck_tile/ops/add_rmsnorm2d_rdquant/pipeline/add_rmsnorm2d_rdquant_fwd_pipeline_default_policy.hpp
View file @ 667cd6ab
...@@ -26,6 +26,7 @@ struct AddRmsnorm2dRdquantFwdPipelineDefaultPolicy ...@@ -26,6 +26,7 @@ struct AddRmsnorm2dRdquantFwdPipelineDefaultPolicy
sequence<1, 1, 2, 2>, sequence<1, 1, 2, 2>,
sequence<0, 3, 0, 3>>{}); sequence<0, 3, 0, 3>>{});
} }
template <typename Problem> template <typename Problem>
CK_TILE_DEVICE static constexpr auto MakeGammaBlockTileDistribution() CK_TILE_DEVICE static constexpr auto MakeGammaBlockTileDistribution()
{ {
......
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