Files
composable_kernel/example/ck_tile/12_smoothquant/smoothquant.cpp
rahjain-amd 4d041837ad Add json dump support to output details from CK/CKTile Examples. (#2551)
* Adding RapidJson Library

* Adding Json Dumps in all CK_Tile Examples

Not verified yet

* Adding json to cktile Batched Transpose

* adding json dumps to layernorm2d_fwd

* Adding  json dump to flatmm_basic

* Adding RapidJson Library

* Adding Json Dumps in all CK_Tile Examples

Not verified yet

* Adding json to cktile Batched Transpose

* adding json dumps to layernorm2d_fwd

* Adding  json dump to flatmm_basic

* Adding json in 03_gemm

* Add json dump to 16_batched_gemm

* Add json dump to gemm_multi_d_fp16

* Add json dump to grouped_gemm

* fix fmha_bwd/fwd

* Fix clang-format errors

exclude include/rapidjson in jenkins as its a third-party library

* Saparating function and defination.

* Update Documentation of 03_gemm

* Refactoring as per code review

* Disable fp8 instances on unsupported targets (#2592)

* Restrict building of gemm_universal_preshuffle_f8 instances to specific targets in CMakeLists.txt

* Add condition to skip gemm_xdl_universal_preshuffle_f8 instances for unsupported targets in CMakeLists.txt

* Add conditions to skip unsupported targets for gemm_universal_preshuffle_f8 and gemm_xdl_universal_preshuffle_f8 instances in CMakeLists.txt

* Refine conditions to exclude gemm_universal_preshuffle_f8 instances for unsupported targets in CMakeLists.txt

---------

Co-authored-by: AviralGoelAMD <aviralgoel@amd.com>

* fix clang format

* remove duplicate lines of code from library/src/tensor_operation_instance/gpu/CMakeLists.txt

* Fixing Readme and unifying jsondumps

* adding moe_smoothquant

* adding fused_moe

* Fixing Readme for batched_gemm

* Fixing Readme for grouped_gemm

* adding flatmm

* adding gemm_multi_d_fp16

* adding elementwise

* adding File name when json is dumped

* Fixing Reduce after merge

* adding batched_transpose

* Adding Warptile in Gemm

* Fixing Clang Format

---------

Co-authored-by: Aviral Goel <aviral.goel@amd.com>
Co-authored-by: AviralGoelAMD <aviralgoel@amd.com>
Co-authored-by: illsilin_amdeng <Illia.Silin@amd.com>
2025-09-02 23:31:29 -07:00

242 lines
8.9 KiB
C++

#include "ck_tile/host.hpp"
#include "smoothquant.hpp"
#include "json_dump.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")
.insert("json", "0", "0: No Json, 1: Dump Results in Json format")
.insert("jsonfile", "smoothquant.json", "json file name to dump results");
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 SmoothScaleDataType = typename TypeConfig::SmoothScaleDataType;
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<SmoothScaleDataType> smscale_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<SmoothScaleDataType>{1e-3, .5f}(smscale_host);
ck_tile::DeviceMem x_buf(x_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem smscale_buf(smscale_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());
smscale_buf.ToDevice(smscale_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(),
smscale_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(SmoothScaleDataType) * 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_smscale = ck_tile::type_convert<ComputeDataType>(smscale_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_smscale;
}
};
ck_tile::make_ParallelTensorFunctor(f, smscale_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;
}
if(arg_parser.get_int("json") == 1)
{
dump_smoothquant_json(arg_parser.get_str("jsonfile"),
data_type,
m,
n,
x_stride,
y_stride,
ave_time,
0,
gb_per_sec,
pass);
}
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;
}