This commit is contained in:
Ding, Yi
2026-03-11 23:03:20 -04:00
commit e6cd3f1e3f
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# Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
# SPDX-License-Identifier: MIT
set(RMSNORM2D_FWD_KNOWN_APIS "fwd;bwd")
set(RMSNORM2D_FWD_ENABLE_APIS "fwd" CACHE STRING
"semicolon-separated list of APIs to generate (${RMSNORM2D_FWD_KNOWN_APIS}) & link, or \"all\".")
if(RMSNORM2D_FWD_ENABLE_APIS STREQUAL "all")
set(RMSNORM2D_FWD_ENABLE_APIS ${RMSNORM2D_FWD_KNOWN_APIS})
endif()
# generate a list of kernels, but not actually emit files at config sta
execute_process(
COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py
--api ${RMSNORM2D_FWD_ENABLE_APIS} --working_path ${CMAKE_CURRENT_BINARY_DIR} --list_blobs
RESULT_VARIABLE ret
)
if(ret AND NOT ret EQUAL 0)
message( FATAL_ERROR "Fail to generate kernels via Python. ${ret}")
endif()
file(STRINGS ${CMAKE_CURRENT_BINARY_DIR}/rmsnorm2d_fwd_blobs.txt RMSNORM2D_FWD_GEN_BLOBS)
add_custom_command(
OUTPUT ${RMSNORM2D_FWD_GEN_BLOBS}
COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py
--api ${RMSNORM2D_FWD_ENABLE_APIS} --working_path ${CMAKE_CURRENT_BINARY_DIR} --gen_blobs
)
set(TILE_RMSNORM2D_FWD "tile_rmsnorm2d_fwd")
message(DEBUG "adding ${TILE_RMSNORM2D_FWD}")
add_executable(${TILE_RMSNORM2D_FWD} rmsnorm2d_fwd.cpp)
target_include_directories(${TILE_RMSNORM2D_FWD} PRIVATE ${CMAKE_CURRENT_LIST_DIR})
target_sources(${TILE_RMSNORM2D_FWD} PRIVATE ${RMSNORM2D_FWD_GEN_BLOBS})
set(TILE_RMSNORM2D_FWD_COMPILE_OPTIONS)
# NOTE: we turn off undefined-func-template to let source compile without explicit declare function specializations
list(APPEND TILE_RMSNORM2D_FWD_COMPILE_OPTIONS -Wno-undefined-func-template -Wno-float-equal --offload-compress)
target_compile_options(${TILE_RMSNORM2D_FWD} PRIVATE ${TILE_RMSNORM2D_FWD_COMPILE_OPTIONS})
set(EXAMPLE_RMSNORM2D_FWD "tile_example_rmsnorm2d_fwd")
add_executable(${EXAMPLE_RMSNORM2D_FWD} example_rmsnorm2d_fwd.cpp)
target_compile_options(${EXAMPLE_RMSNORM2D_FWD} PRIVATE ${TILE_RMSNORM2D_FWD_COMPILE_OPTIONS})
# TODO: we have to turn off this global prop, otherwise the progress bar generated
# by cmake will print too many files, execvp: /bin/sh: Argument list too long
# however, this property may affect global
# TODO: consider codegen a makefile by us
set_property(GLOBAL PROPERTY RULE_MESSAGES OFF)

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# RMSNorm2D Forward with CK Tile
This example demonstrates 2D Root Mean Square Layer Normalization (RMSNorm) using the CK Tile programming model, a normalization technique widely used in LLMs and transformers.
---
## Algorithm and Math
For each row $x$:
$$
\text{rms}(x) = \sqrt{\frac{1}{N} \sum_{i=1}^N x_i^2 + \epsilon}
$$
$$
y_i = \frac{x_i}{\text{rms}(x)} \cdot \gamma_i
$$
where $\gamma$ is a learnable scale parameter.
- **Tilewise RMSNorm**: Each thread block processes a tile (row or block), computes the mean square, normalizes, and applies scale.
---
## Tile Programming Model
- **Tiles**: Each thread block processes a tile of the input matrix.
- **Pipeline**: Modular, can be extended for fused operations.
---
## Build & Run
```bash
# 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_rmsnorm2d_fwd -j`nproc`
```
This will result in an executable `build/bin/tile_rmsnorm2d_fwd`
### Arguments
```bash
args:
-m m dimension (default:3328)
-n n dimension (default:4096)
-x_stride x row_stride, if -1 then equal to n (default:-1)
-xr_stride x residule row_stride, if -1 then equal to n (default:-1)
-y_stride y row_stride, if -1 then equal to n (default:-1)
-yr_stride y residule row_stride, if -1 then equal to n (default:-1)
-e epsilon (default:1e-5)
-save_rms save rms(invrms) or not. set to 1 in training case (default:0)
-save_unquant save result before quant (default:0)
-v cpu validation or not (default:1)
-kname print kernel name or not (default:1)
-prec_i input precision (default:fp16)
-prec_o output precision, set auto will be the same as input (default:auto)
-prec_sm output quant scale type, set auto will use fp32. used when fquant=1 (default:auto)
-prec_sy output quant scale type, set auto will use fp32. used when fquant=1 or 2 (default:auto)
-fadd fused-add, 0:no fused add, 1:preadd+store, 2:preadd only (default:0)
-fquant fused-quant, 0:no, 1:smooth-dynamic-quant, 2:dynamic-quant (default:0)
-warmup cold iter (default:5)
-repeat hot iter (default:20)
-s sensitive model mode, 0: for no specific model, 1: for T5-like model (default:0)
-json 0: No Json, 1: Dump Results in Json format (default:0)
-jsonfile json file name to dump results (default:rmsnorm2d_fwd.json)
```
---
## Source Structure
- **Kernel**: [`rmsnorm2d_fwd.hpp`](rmsnorm2d_fwd.hpp) (tile-programming kernel template)
- **Executable**: [`rmsnorm2d_fwd.cpp`](rmsnorm2d_fwd.cpp) (argument parsing, kernel launch)
- **Build**: `CMakeLists.txt`, `generate.py`, `script/`
---
## Related CK Tile Examples
- [02_layernorm2d](../02_layernorm2d/README.md): LayerNorm2D with tiles
- [12_smoothquant](../12_smoothquant/README.md): SmoothQuant with tiles
- [05_reduce](../05_reduce/README.md): Reductions with tiles
For distribution, see [`include/ck_tile/tile_program/tile_distribution/`](../../../include/ck_tile/tile_program/tile_distribution/).
---
[Back to CK Tile Examples](../README.md)

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// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#include "ck_tile/host.hpp"
#include "ck_tile/core.hpp"
#include "ck_tile/host/kernel_launch.hpp"
#include "ck_tile/ops/epilogue.hpp"
#include "ck_tile/ops/rmsnorm2d.hpp"
#include <cstring>
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("e", "1e-5", "epsilon")
.insert("v", "1", "cpu validation or not")
.insert("prec", "fp16", "precision")
.insert("warmup", "0", "cold iter")
.insert("repeat", "1", "hot iter")
.insert("s", "0", "sensitive model mode, 0: for no specific model, 1: for T5-like model");
bool result = arg_parser.parse(argc, argv);
return std::make_tuple(result, arg_parser);
}
template <typename DataType, int USEModelSensitive>
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;
float epsilon = arg_parser.get_float("e");
std::string data_type = arg_parser.get_str("prec");
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 XDataType = DataType;
using YDataType = DataType;
using GammaDataType = DataType;
using InvRmsDataType = ck_tile::null_type;
using UnquantYDataType = ck_tile::null_type;
using SmoothScaleDataType = ck_tile::null_type;
using YScaleDataType = ck_tile::null_type;
using ComputeDataType = float;
// host verify
ck_tile::HostTensor<XDataType> x_host({m, n}, {stride, 1});
ck_tile::HostTensor<GammaDataType> gamma_host({n});
ck_tile::HostTensor<YDataType> y_host_ref({m, n}, {stride, 1});
ck_tile::HostTensor<YDataType> y_host_dev({m, n}, {stride, 1});
ck_tile::HostTensor<InvRmsDataType> invRms_host_ref({m});
ck_tile::HostTensor<UnquantYDataType> unquant_y_host_ref({m, n}, {stride, 1});
ck_tile::FillUniformDistribution<XDataType>{-.5f, .5f}(x_host);
ck_tile::FillUniformDistribution<GammaDataType>{-.5f, .5f}(gamma_host);
ck_tile::DeviceMem x_buf(x_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem gamma_buf(gamma_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem y_buf(y_host_dev.get_element_space_size_in_bytes());
x_buf.ToDevice(x_host.data());
gamma_buf.ToDevice(gamma_host.data());
constexpr bool kTwoPass = true;
using BlockTile = ck_tile::sequence<2, 128>;
using Vector = ck_tile::sequence<1, 1>;
using ThreadPerBlock = ck_tile::sequence<2, 128>;
using Shape = ck_tile::Generic2dBlockShape<BlockTile, ThreadPerBlock, Vector>;
using PipelineTraits =
ck_tile::Rmsnorm2dFwdTraits<true, // kPadN
false, // kSaveInvRms
false, // kSaveUnquant
kTwoPass,
ck_tile::Rmsnorm2dFusedAddEnum::NO_ADD, // fuse add
ck_tile::Rmsnorm2dFusedQuantEnum::NO_SWEEP, // fuse quant
static_cast<ck_tile::Rmsnorm2dSensitiveEnum>(
USEModelSensitive)>;
using Problem = ck_tile::Rmsnorm2dFwdPipelineProblem<XDataType,
GammaDataType,
ComputeDataType,
YDataType,
InvRmsDataType,
UnquantYDataType,
SmoothScaleDataType,
YScaleDataType,
Shape,
PipelineTraits>;
using OnePassPipeline = ck_tile::Rmsnorm2dFwdPipelineOnePass<Problem>;
using TwoPassPipeline = ck_tile::Rmsnorm2dFwdPipelineTwoPass<Problem>;
using T5PassPipeline = ck_tile::Rmsnorm2dFwdPipelineModelSensitiveT5Pass<Problem>;
using Pipeline =
std::conditional_t<(PipelineTraits::kUseModelSensitiveRMSNorm ==
ck_tile::Rmsnorm2dSensitiveEnum::NO_SPECIFIC_MODEL ||
PipelineTraits::kTwoPass), // TODO: consider TwoPass for T5PassPipeline
std::conditional_t<PipelineTraits::kTwoPass,
TwoPassPipeline,
OnePassPipeline>, // kUseModelSensitiveRMSNorm
// == 0
T5PassPipeline>;
using Default2DEpilogueProblem = ck_tile::
Default2DEpilogueProblem<ComputeDataType, YDataType, false, PipelineTraits::kPadN, false>;
using Default2DEpilogue = ck_tile::Default2DEpilogue<Default2DEpilogueProblem>;
using Kernel = ck_tile::Rmsnorm2dFwd<Pipeline, Default2DEpilogue>;
ck_tile::Rmsnorm2dFwdHostArgs args{x_buf.GetDeviceBuffer(),
nullptr,
nullptr,
gamma_buf.GetDeviceBuffer(),
y_buf.GetDeviceBuffer(),
nullptr,
nullptr,
nullptr,
nullptr,
epsilon,
m,
n,
stride,
stride,
stride,
stride};
auto kargs = Kernel::MakeKargs(args);
const dim3 grids = Kernel::GridSize(args);
const dim3 blocks = Kernel::BlockSize();
constexpr ck_tile::index_t kBlockPerCu = 1;
auto s = ck_tile::stream_config{nullptr, true, 0, warmup, repeat};
ck_tile::launch_kernel(s, ck_tile::make_kernel<kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
bool pass = true;
if(do_validation)
{
// reference
ck_tile::reference_rmsnorm2d_fwd<XDataType,
GammaDataType,
ComputeDataType,
YDataType,
InvRmsDataType,
UnquantYDataType>(
x_host, gamma_host, y_host_ref, invRms_host_ref, unquant_y_host_ref, epsilon);
y_buf.FromDevice(y_host_dev.data());
auto [rtol, atol] = ck_tile::make_tuple(1e-3, 1e-3);
if(stride == n)
{
pass = ck_tile::check_err(
y_host_dev, y_host_ref, std::string("OUT Error: Incorrect results!"), rtol, atol);
}
else
{
for(int i_r = 0; i_r < m; i_r++)
{
std::vector<YDataType> y_host_dev_row(y_host_dev.begin() + i_r * stride,
y_host_dev.begin() + i_r * stride + n);
std::vector<YDataType> y_host_ref_row(y_host_ref.begin() + i_r * stride,
y_host_ref.begin() + i_r * stride + n);
pass &= ck_tile::check_err(y_host_dev_row,
y_host_ref_row,
std::string("OUT[") + std::to_string(i_r) +
std::string("] Error: Incorrect results!"),
rtol,
atol);
}
}
std::cout << "[" << data_type << "]" << " m:" << m << ", n:" << n << ", stride:" << stride
<< ", s:" << USEModelSensitive << ", 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");
const int use_model_sensitive_rmsnorm = arg_parser.get_int("s");
if(data_type == "fp16")
{
if(use_model_sensitive_rmsnorm == 0) // 0: for no specific RMSNorm
{
return run<ck_tile::half_t, 0>(arg_parser) ? 0 : -2;
}
else if(use_model_sensitive_rmsnorm == 1) // 1: for T5-like RMSNorm
{
return run<ck_tile::half_t, 1>(arg_parser) ? 0 : -2;
}
}
return -3;
}

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// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#include "ck_tile/host.hpp"
#include "rmsnorm2d_fwd.hpp"
#include <cstring>
#include "ck_tile/utility/json_dump.hpp"
// different threshold for different dtype
template <typename DataType>
auto get_elimit()
{
double rtol = 1e-2;
double atol = 1e-2;
return ck_tile::make_tuple(rtol, atol);
}
template <>
auto get_elimit<ck_tile::bf16_t>()
{
double rtol = 1e-2;
double atol = 1e-2;
return ck_tile::make_tuple(rtol, atol);
}
template <>
auto get_elimit<ck_tile::int8_t>()
{
double rtol = 1e-02;
double atol = 1.0;
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", "x row_stride, if -1 then equal to n")
.insert("xr_stride", "-1", "x residule row_stride, if -1 then equal to n")
.insert("y_stride", "-1", "y row_stride, if -1 then equal to n")
.insert("yr_stride", "-1", "y residule row_stride, if -1 then equal to n")
.insert("e", "1e-5", "epsilon")
.insert("save_rms", "0", "save rms(invrms) or not. set to 1 in training case")
.insert("save_unquant", "0", "save result before quant")
.insert("v", "1", "cpu validation or not")
.insert("kname", "1", "print kernel name or not")
.insert("prec_i", "fp16", "input precision")
.insert("prec_o", "auto", "output precision, set auto will be the same as input")
.insert("prec_sm",
"auto",
"output quant scale type, set auto will use fp32. used when fquant=1")
.insert("prec_sy",
"auto",
"output quant scale type, set auto will use fp32. used when fquant=1 or 2")
.insert("fadd", "0", "fused-add, 0:no fused add, 1:preadd+store, 2:preadd only")
.insert("fquant", "0", "fused-quant, 0:no, 1:smooth-dynamic-quant, 2:dynamic-quant")
.insert("warmup", "5", "cold iter")
.insert("repeat", "20", "hot iter")
.insert("s", "0", "sensitive model mode, 0: for no specific model, 1: for T5-like model")
.insert("json", "0", "0: No Json, 1: Dump Results in Json format")
.insert("jsonfile", "rmsnorm2d_fwd.json", "json file name to dump results");
bool result = arg_parser.parse(argc, argv);
return std::make_tuple(result, arg_parser);
}
template <typename InDataType,
typename OutDataType,
typename SmoothScaleDataType,
typename YScaleDataType,
bool SaveRms,
bool SaveUnquant>
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");
float epsilon = arg_parser.get_float("e");
int kname = arg_parser.get_int("kname");
int do_validation = arg_parser.get_int("v");
int fused_add = arg_parser.get_int("fadd");
int fused_quant = arg_parser.get_int("fquant");
int warmup = arg_parser.get_int("warmup");
int repeat = arg_parser.get_int("repeat");
int use_model_sensitive_rmsnorm = arg_parser.get_int("s");
ck_tile::index_t x_stride = arg_parser.get_int("x_stride");
if(x_stride < 0)
x_stride = n;
ck_tile::index_t xr_stride = arg_parser.get_int("xr_stride");
if(xr_stride < 0)
xr_stride = n;
ck_tile::index_t y_stride = arg_parser.get_int("y_stride");
if(y_stride < 0)
y_stride = n;
ck_tile::index_t yr_stride = arg_parser.get_int("yr_stride");
if(yr_stride < 0)
yr_stride = n;
assert(x_stride >= n);
std::string prec_i = arg_parser.get_str("prec_i");
std::string prec_o = arg_parser.get_str("prec_o");
std::string prec_sm = arg_parser.get_str("prec_sm");
std::string prec_sy = arg_parser.get_str("prec_sy");
if(prec_o == "auto")
{
prec_o = prec_i;
}
if(prec_sm == "auto")
{
prec_sm = "fp32";
}
if(prec_sy == "auto")
{
prec_sy = "fp32";
}
if((fused_quant == 1 || fused_quant == 2) && prec_o != "int8" && prec_o != "fp8")
{
std::cout
<< "if fused_quant is 1 or 2, only support \"-prec_o=int8\" or \"-prec_o=fp8\" cases."
<< std::endl;
return false;
}
if((fused_quant == 0) && SaveUnquant)
{
std::cout
<< "save_unquant should be 0 if quant output is not enabled because it is meaningless. "
<< "Output Y is what wanted." << std::endl;
return false;
}
using TypeConfig =
RmsnormTypeConfig<InDataType, OutDataType, SmoothScaleDataType, YScaleDataType>;
using XDataType = typename TypeConfig::XDataType;
using YDataType = typename TypeConfig::YDataType;
using GammaDataType = typename TypeConfig::GammaDataType;
using XResidualDataType = XDataType;
using YResidualDataType = XDataType;
using InvRmsDataType =
std::conditional_t<SaveRms, typename TypeConfig::InvRmsDataType, ck_tile::null_type>;
using UnquantYDataType =
std::conditional_t<SaveUnquant, typename TypeConfig::UnquantYDataType, ck_tile::null_type>;
using ComputeDataType = typename TypeConfig::ComputeDataType;
// host verify
ck_tile::HostTensor<XDataType> x_host({m, n}, {x_stride, 1});
ck_tile::HostTensor<GammaDataType> gamma_host({n});
ck_tile::HostTensor<SmoothScaleDataType> sm_scale_host({n});
ck_tile::HostTensor<SmoothScaleDataType> sm_scale_host_dev({n});
ck_tile::HostTensor<XResidualDataType> x_residual_host({m, n}, {xr_stride, 1});
ck_tile::HostTensor<YResidualDataType> y_residual_host({m, n}, {yr_stride, 1});
ck_tile::HostTensor<YDataType> y_host_ref({m, n}, {y_stride, 1});
ck_tile::HostTensor<YDataType> y_host_dev({m, n}, {y_stride, 1});
ck_tile::HostTensor<YScaleDataType> y_scale_host_ref({m});
ck_tile::HostTensor<YScaleDataType> y_scale_host_dev({m});
ck_tile::HostTensor<InvRmsDataType> invRms_host_ref({m});
ck_tile::HostTensor<UnquantYDataType> unquant_y_host_ref({m, n}, {y_stride, 1});
ck_tile::HostTensor<UnquantYDataType> unquant_y_host_dev({m, n}, {y_stride, 1});
ck_tile::HostTensor<ck_tile::null_type> unquant_y_null({1});
ck_tile::FillUniformDistribution<XDataType>{-.5f, .5f}(x_host);
ck_tile::FillUniformDistribution<XResidualDataType>{-.5f, .5f}(x_residual_host);
ck_tile::FillUniformDistribution<SmoothScaleDataType>{-1.f, 1.f}(sm_scale_host);
ck_tile::FillUniformDistribution<GammaDataType>{-.5f, .5f}(gamma_host);
ck_tile::DeviceMem x_buf(x_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem gamma_buf(gamma_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem y_buf(y_host_dev.get_element_space_size_in_bytes());
ck_tile::DeviceMem y_scale_buf(y_scale_host_dev.get_element_space_size_in_bytes());
ck_tile::DeviceMem sm_scale_buf(sm_scale_host_dev.get_element_space_size_in_bytes());
ck_tile::DeviceMem x_residual_buf(x_residual_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem y_residual_buf(y_residual_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem unquant_y_buf(unquant_y_host_dev.get_element_space_size_in_bytes());
x_buf.ToDevice(x_host.data());
gamma_buf.ToDevice(gamma_host.data());
x_residual_buf.ToDevice(x_residual_host.data());
sm_scale_buf.ToDevice(sm_scale_host.data());
auto prec_str = [&]() {
auto base_str = prec_i;
if(prec_i != prec_o)
{
base_str += "|" + prec_o;
}
if(fused_quant == 1)
{
base_str += std::string("(") + prec_sy + ")";
}
return base_str;
}();
if(n > 8192)
{
use_model_sensitive_rmsnorm = 0;
}
std::cout << "[" << prec_str << "]" << " m:" << m << ", n:" << n << ", x_stride:" << x_stride
<< ", xr_stride:" << xr_stride << ", y_stride:" << y_stride
<< ", yr_stride:" << yr_stride << ", s:" << use_model_sensitive_rmsnorm << std::flush;
rmsnorm2d_fwd_traits traits{prec_i,
prec_o,
prec_sm,
prec_sy,
SaveRms,
SaveUnquant,
fused_add,
fused_quant,
use_model_sensitive_rmsnorm};
rmsnorm2d_fwd_args args{x_buf.GetDeviceBuffer(),
fused_add != 0 ? x_residual_buf.GetDeviceBuffer() : nullptr,
fused_quant == 1 ? sm_scale_buf.GetDeviceBuffer() : nullptr,
gamma_buf.GetDeviceBuffer(),
y_buf.GetDeviceBuffer(),
fused_add == 1 ? y_residual_buf.GetDeviceBuffer() : nullptr,
fused_quant != 0 ? y_scale_buf.GetDeviceBuffer() : nullptr,
nullptr, // p_invRms, unsupported yet
SaveUnquant ? unquant_y_buf.GetDeviceBuffer() : nullptr,
epsilon,
m,
n,
x_stride, // x row_stride
xr_stride, // x residule row stride
y_stride, // y row stride
yr_stride}; // y residule row stride
float ave_time = rmsnorm2d_fwd(
traits, args, ck_tile::stream_config{nullptr, true, kname ? 1 : 0, warmup, repeat});
std::size_t num_byte =
sizeof(XDataType) * m * n + sizeof(GammaDataType) * n + sizeof(YDataType) * m * n;
num_byte += SaveRms ? sizeof(InvRmsDataType) * m * n : 0;
num_byte += SaveUnquant ? sizeof(UnquantYDataType) * m * n : 0;
num_byte += fused_add ? sizeof(XResidualDataType) * m * n : 0;
num_byte += ((fused_quant == 1) || (fused_quant == 2)) ? sizeof(YScaleDataType) * m : 0;
num_byte += (fused_quant == 1) ? sizeof(SmoothScaleDataType) * n : 0;
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)
{
// reference
if(fused_add != 0)
{
// fused pre_add/pre_add_store
// TODO we accumulate directly to x_host for simplcity here...
std::transform(x_host.mData.cbegin(),
x_host.mData.cend(),
x_residual_host.mData.cbegin(),
x_host.mData.begin(),
[](auto x_, auto r_) {
auto o_ = ck_tile::type_convert<ComputeDataType>(x_) +
ck_tile::type_convert<ComputeDataType>(r_);
return ck_tile::type_convert<XDataType>(o_);
});
}
if(fused_quant != 0)
{
auto dquant_functor = [&](int m_, auto& o_, auto& acc_) {
int N_ = acc_.mDesc.get_lengths()[1];
if(fused_quant == 1)
{
for(int n_ = 0; n_ < N_; n_++)
{
// input smooth outlier
acc_(m_, n_) = acc_(m_, n_) *
ck_tile::type_convert<ComputeDataType>(sm_scale_host(n_));
}
}
ComputeDataType absmax = static_cast<ComputeDataType>(0);
for(int n_ = 0; n_ < N_; n_++)
{
const auto a = ck_tile::abs(acc_(m_, n_));
absmax = a > absmax ? a : absmax;
}
// printf("cpu:absmax:%f\n", absmax);
constexpr ComputeDataType kMaxY =
std::is_same<YDataType, ck_tile::fp8_t>::value ? 240.0
: std::is_same<YDataType, ck_tile::int8_t>::value ? 127.0
: 0.0;
ComputeDataType y_scale = absmax / kMaxY;
y_scale_host_ref(m_) = ck_tile::type_convert<YScaleDataType>(y_scale);
for(int n_ = 0; n_ < N_; n_++)
{
o_(m_, n_) = ck_tile::type_convert<YDataType>(acc_(m_, n_) / y_scale);
}
};
auto default_and_dquant_functor = [&](int m_, auto& o_unquant_, auto& o_, auto& acc_) {
const int N = acc_.mDesc.get_lengths()[1];
for(int n_ = 0; n_ < N; ++n_)
{
o_unquant_(m_, n_) = ck_tile::type_convert<UnquantYDataType>(acc_(m_, n_));
}
dquant_functor(m_, o_, acc_);
};
if constexpr(SaveUnquant)
{
ck_tile::reference_rmsnorm2d_fwd<XDataType,
GammaDataType,
ComputeDataType,
YDataType,
InvRmsDataType,
UnquantYDataType>(x_host,
gamma_host,
y_host_ref,
invRms_host_ref,
unquant_y_host_ref,
epsilon,
default_and_dquant_functor,
use_model_sensitive_rmsnorm);
}
else
{
ck_tile::reference_rmsnorm2d_fwd<XDataType,
GammaDataType,
ComputeDataType,
YDataType,
InvRmsDataType,
UnquantYDataType>(x_host,
gamma_host,
y_host_ref,
invRms_host_ref,
unquant_y_host_ref,
epsilon,
dquant_functor,
use_model_sensitive_rmsnorm);
}
}
else
{
assert(SaveUnquant == false);
ck_tile::reference_rmsnorm2d_fwd<XDataType,
GammaDataType,
ComputeDataType,
YDataType,
InvRmsDataType,
ck_tile::null_type>(
x_host,
gamma_host,
y_host_ref,
invRms_host_ref,
unquant_y_null,
epsilon,
ck_tile::reference_rmsnorm2d_default_epilogue{},
use_model_sensitive_rmsnorm);
}
y_buf.FromDevice(y_host_dev.data());
ck_tile::HostTensor<YResidualDataType> y_residual_host_dev({m, n}, {yr_stride, 1});
if(fused_add == 1)
{
y_residual_buf.FromDevice(y_residual_host_dev.data());
}
if constexpr(SaveUnquant)
{
unquant_y_buf.FromDevice(unquant_y_host_dev.data());
}
auto [rtol, atol] = get_elimit<YDataType>();
if(x_stride == n)
{
pass = ck_tile::check_err(
y_host_dev, y_host_ref, std::string("\nOUT Error: Incorrect results!"), rtol, atol);
if constexpr(SaveUnquant)
{
pass &= ck_tile::check_err(unquant_y_host_dev,
unquant_y_host_ref,
std::string("\n OUT ERROR: Incorrect unquant results!"),
rtol,
atol);
}
if(fused_add == 1)
{
pass &= ck_tile::check_err(y_residual_host_dev,
x_host,
std::string("\nADD Error: Incorrect results!"),
rtol,
atol);
}
}
else
{
for(int i_r = 0; i_r < m; i_r++)
{
std::vector<YDataType> y_host_dev_row(y_host_dev.begin() + i_r * y_stride,
y_host_dev.begin() + i_r * y_stride + n);
std::vector<YDataType> y_host_ref_row(y_host_ref.begin() + i_r * y_stride,
y_host_ref.begin() + i_r * y_stride + n);
pass &= ck_tile::check_err(y_host_dev_row,
y_host_ref_row,
std::string("\nOUT[") + std::to_string(i_r) +
std::string("] Error: Incorrect results!"),
rtol,
atol);
if(fused_add == 1)
{
std::vector<YResidualDataType> y_residual_host_dev_row(
y_residual_host_dev.begin() + i_r * yr_stride,
y_residual_host_dev.begin() + i_r * yr_stride + n);
std::vector<YResidualDataType> y_residual_host_ref_row(
x_host.begin() + i_r * yr_stride, x_host.begin() + i_r * yr_stride + n);
pass &= ck_tile::check_err(y_residual_host_dev_row,
y_residual_host_ref_row,
std::string("\nADD[") + std::to_string(i_r) +
std::string("] Error: Incorrect results!"),
rtol,
atol);
}
if constexpr(SaveUnquant)
{
std::vector<UnquantYDataType> unquant_y_host_dev_row(
unquant_y_host_dev.begin() + i_r * y_stride,
unquant_y_host_dev.begin() + i_r * y_stride + n);
std::vector<UnquantYDataType> unquant_y_host_ref_row(
unquant_y_host_ref.begin() + i_r * y_stride,
unquant_y_host_ref.begin() + i_r * y_stride + n);
pass &=
ck_tile::check_err(unquant_y_host_dev_row,
unquant_y_host_ref_row,
std::string("\nOUT[") + std::to_string(i_r) +
std::string("] Error: Incorrect unquant y results!"),
rtol,
atol);
}
}
}
if(fused_quant == 1)
{
y_scale_buf.FromDevice(y_scale_host_dev.data());
pass &= ck_tile::check_err(y_scale_host_dev,
y_scale_host_ref,
std::string("\nSCALE Error: Incorrect results!"),
rtol,
atol);
}
std::cout << ", valid:" << (pass ? "y" : "n") << std::flush << std::endl;
}
if(arg_parser.get_int("json") == 1)
{
dump_rmsnorm2d_fwd_json(arg_parser.get_str("jsonfile"),
prec_str,
m,
n,
x_stride,
xr_stride,
y_stride,
yr_stride,
use_model_sensitive_rmsnorm,
ave_time,
0,
gb_per_sec,
pass);
}
return pass;
}
bool is_quant_data_type(const std::string& prec) { return (prec == "int8") || (prec == "fp8"); }
int main(int argc, char* argv[])
{
auto [result, arg_parser] = create_args(argc, argv);
if(!result)
return -1;
std::string prec_i = arg_parser.get_str("prec_i");
std::string prec_o = arg_parser.get_str("prec_o");
std::string prec_sm = arg_parser.get_str("prec_sm");
std::string prec_sy = arg_parser.get_str("prec_sy");
if(prec_o == "auto")
{
prec_o = prec_i;
}
if(prec_sm == "auto")
{
prec_sm = "fp32";
}
if(prec_sy == "auto")
{
prec_sy = "fp32";
}
int save_rms = arg_parser.get_int("save_rms");
int fused_quant = arg_parser.get_int("fquant");
int save_unquant =
arg_parser.get_int("save_unquant") && is_quant_data_type(prec_o) && (fused_quant != 0);
if(prec_i == "fp16" && prec_o == "fp16" && prec_sm == "fp32" && prec_sy == "fp32" && save_rms)
{
return run<ck_tile::half_t, ck_tile::half_t, float, float, true, false>(arg_parser) ? 0
: -2;
}
else if(prec_i == "fp16" && prec_o == "fp16" && prec_sm == "fp32" && prec_sy == "fp32" &&
!save_rms)
{
return run<ck_tile::half_t, ck_tile::half_t, float, float, false, false>(arg_parser) ? 0
: -2;
}
else if(prec_i == "bf16" && prec_o == "bf16" && prec_sm == "fp32" && prec_sy == "fp32" &&
save_rms)
{
return run<ck_tile::bf16_t, ck_tile::bf16_t, float, float, true, false>(arg_parser) ? 0
: -2;
}
else if(prec_i == "bf16" && prec_o == "bf16" && prec_sm == "fp32" && prec_sy == "fp32" &&
!save_rms)
{
return run<ck_tile::bf16_t, ck_tile::bf16_t, float, float, false, false>(arg_parser) ? 0
: -2;
}
// dynamic quant case, only in inference
else if(prec_i == "fp16" && prec_o == "int8" && prec_sm == "fp32" && prec_sy == "fp32" &&
!save_rms && !save_unquant)
{
return run<ck_tile::half_t, ck_tile::int8_t, float, float, true, false>(arg_parser) ? 0
: -2;
}
else if(prec_i == "bf16" && prec_o == "int8" && prec_sm == "fp32" && prec_sy == "fp32" &&
!save_rms && !save_unquant)
{
return run<ck_tile::bf16_t, ck_tile::int8_t, float, float, true, false>(arg_parser) ? 0
: -2;
}
else if(prec_i == "fp16" && prec_o == "fp8" && prec_sm == "fp32" && prec_sy == "fp32" &&
!save_rms && !save_unquant)
{
return run<ck_tile::half_t, ck_tile::fp8_t, float, float, false, false>(arg_parser) ? 0
: -2;
}
else if(prec_i == "bf16" && prec_o == "fp8" && prec_sm == "fp32" && prec_sy == "fp32" &&
!save_rms && !save_unquant)
{
return run<ck_tile::bf16_t, ck_tile::fp8_t, float, float, false, false>(arg_parser) ? 0
: -2;
}
else if(prec_i == "fp16" && prec_o == "int8" && prec_sm == "fp32" && prec_sy == "fp32" &&
!save_rms && save_unquant)
{
return run<ck_tile::half_t, ck_tile::int8_t, float, float, true, true>(arg_parser) ? 0 : -2;
}
else if(prec_i == "bf16" && prec_o == "int8" && prec_sm == "fp32" && prec_sy == "fp32" &&
!save_rms && save_unquant)
{
return run<ck_tile::bf16_t, ck_tile::int8_t, float, float, true, true>(arg_parser) ? 0 : -2;
}
else if(prec_i == "fp16" && prec_o == "fp8" && prec_sm == "fp32" && prec_sy == "fp32" &&
!save_rms && save_unquant)
{
return run<ck_tile::half_t, ck_tile::fp8_t, float, float, false, true>(arg_parser) ? 0 : -2;
}
else if(prec_i == "bf16" && prec_o == "fp8" && prec_sm == "fp32" && prec_sy == "fp32" &&
!save_rms && save_unquant)
{
return run<ck_tile::bf16_t, ck_tile::fp8_t, float, float, false, true>(arg_parser) ? 0 : -2;
}
return -3;
}

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@@ -0,0 +1,71 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/host/kernel_launch.hpp"
#include "ck_tile/ops/rmsnorm2d.hpp"
#include <string>
template <typename InType,
typename OutType,
typename SmoothScaleDataType_,
typename YScaleDataType_>
struct RmsnormTypeConfig;
template <typename OutType, typename SmoothScaleDataType_, typename YScaleDataType_>
struct RmsnormTypeConfig<ck_tile::half_t, OutType, SmoothScaleDataType_, YScaleDataType_>
{
using XDataType = ck_tile::half_t;
using YDataType = OutType;
using GammaDataType = ck_tile::half_t;
using InvRmsDataType = ck_tile::half_t;
using UnquantYDataType = ck_tile::half_t;
using ComputeDataType = float;
using SmoothScaleDataType = SmoothScaleDataType_;
using YScaleDataType = YScaleDataType_;
};
template <typename OutType, typename SmoothScaleDataType_, typename YScaleDataType_>
struct RmsnormTypeConfig<ck_tile::bf16_t, OutType, SmoothScaleDataType_, YScaleDataType_>
{
using XDataType = ck_tile::bf16_t;
using YDataType = OutType;
using GammaDataType = ck_tile::bf16_t;
using InvRmsDataType = ck_tile::bf16_t;
using UnquantYDataType = ck_tile::bf16_t;
using ComputeDataType = float;
using SmoothScaleDataType = SmoothScaleDataType_;
using YScaleDataType = YScaleDataType_;
};
// runtime args
struct rmsnorm2d_fwd_args : public ck_tile::Rmsnorm2dFwdHostArgs
{
};
template <typename Traits_>
float rmsnorm2d_fwd_(const ck_tile::stream_config& s, rmsnorm2d_fwd_args a);
// This is the public API, will be generated by script
struct rmsnorm2d_fwd_traits
{
std::string prec_i; // input precision
std::string prec_o; // output precision
// if fused_quant == 1, need set prec_sm/prec_sy to proper string, otherwise can set
// arbitrary(will skip check) if fused_quant == 2, need set prec_sy to proper string, otherwise
// can set arbitrary(will skip check)
std::string prec_sm; // x-scale, used for [1*N] input smooth quant
std::string prec_sy; // y-scale, used for [M*1] output for next layer
bool save_rms;
bool save_unquant;
int fused_add; // 0:no-add, 1:pre-add-store, 2:pre-add
int fused_quant; // 0:no-sweep, 1:smooth-dynamic-quant, 2:dynamic-quant
int use_model_sensitive_rmsnorm = 0; // 0: Use default RMSNorm; 1: Use T5-like implementation
};
float rmsnorm2d_fwd(rmsnorm2d_fwd_traits, rmsnorm2d_fwd_args, const ck_tile::stream_config&);

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#!/bin/sh
# Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
# SPDX-License-Identifier: MIT
EXE="$(find . -name tile_rmsnorm2d_fwd -type f | head -n 1)"
# 0: for no specific RMSNorm
$EXE -m=1 -n=1 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=0
$EXE -m=700 -n=80 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=0
$EXE -m=700 -n=128 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=0
$EXE -m=700 -n=144 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=0
$EXE -m=700 -n=168 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=0
$EXE -m=700 -n=184 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=0
$EXE -m=700 -n=256 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=0
$EXE -m=700 -n=288 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=0
$EXE -m=700 -n=344 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=0
$EXE -m=700 -n=376 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=0
$EXE -m=700 -n=448 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=0
$EXE -m=700 -n=512 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=0
$EXE -m=700 -n=924 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=0
$EXE -m=700 -n=1024 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=0
$EXE -m=700 -n=1078 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=0
$EXE -m=700 -n=1996 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=0
$EXE -m=700 -n=4080 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=0
$EXE -m=700 -n=80 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=0
$EXE -m=700 -n=128 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=0
$EXE -m=700 -n=144 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=0
$EXE -m=700 -n=168 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=0
$EXE -m=700 -n=184 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=0
$EXE -m=700 -n=256 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=0
$EXE -m=700 -n=288 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=0
$EXE -m=700 -n=344 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=0
$EXE -m=700 -n=376 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=0
$EXE -m=700 -n=448 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=0
$EXE -m=700 -n=512 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=0
$EXE -m=700 -n=924 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=0
$EXE -m=700 -n=1024 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=0
$EXE -m=700 -n=1078 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=0
$EXE -m=700 -n=1996 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=0
$EXE -m=700 -n=4080 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=0
# 1: for T5-like RMSNorm
$EXE -m=1 -n=1 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=1
$EXE -m=700 -n=80 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=1
$EXE -m=700 -n=128 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=1
$EXE -m=700 -n=144 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=1
$EXE -m=700 -n=168 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=1
$EXE -m=700 -n=184 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=1
$EXE -m=700 -n=256 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=1
$EXE -m=700 -n=288 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=1
$EXE -m=700 -n=344 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=1
$EXE -m=700 -n=376 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=1
$EXE -m=700 -n=448 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=1
$EXE -m=700 -n=512 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=1
$EXE -m=700 -n=924 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=1
$EXE -m=700 -n=1024 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=1
$EXE -m=700 -n=1078 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=1
$EXE -m=700 -n=1996 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=1
$EXE -m=700 -n=4080 -e=1e-12 -v=1 -prec_i=bf16 -repeat=1000 -s=1
$EXE -m=700 -n=80 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=1
$EXE -m=700 -n=128 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=1
$EXE -m=700 -n=144 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=1
$EXE -m=700 -n=168 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=1
$EXE -m=700 -n=184 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=1
$EXE -m=700 -n=256 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=1
$EXE -m=700 -n=288 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=1
$EXE -m=700 -n=344 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=1
$EXE -m=700 -n=376 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=1
$EXE -m=700 -n=448 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=1
$EXE -m=700 -n=512 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=1
$EXE -m=700 -n=924 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=1
$EXE -m=700 -n=1024 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=1
$EXE -m=700 -n=1078 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=1
$EXE -m=700 -n=1996 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=1
$EXE -m=700 -n=4080 -e=1e-12 -v=1 -prec_i=fp16 -repeat=1000 -s=1

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@@ -0,0 +1,88 @@
#!/bin/bash
# Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
# SPDX-License-Identifier: MIT
EXE="$(find . -name tile_rmsnorm2d_fwd -type f | head -n 1)"
total=0
valid=0
run_case() {
cmd="$EXE -prec_i=$1 -fadd=$2 -s=$3 $4 -m=$5 -n=$6 $7"
echo "[CMD] $cmd"
output=$($cmd 2>&1)
echo "$output"
if echo "$output" | grep -q "valid:y"; then
valid=$((valid + 1))
fi
total=$((total + 1))
}
fquant_list=(
""
"-fquant=1 -prec_o=int8"
"-fquant=2 -prec_o=int8"
"-fquant=1 -prec_o=fp8"
"-fquant=2 -prec_o=fp8"
"-fquant=1 -prec_o=int8 -save_unquant=1"
"-fquant=2 -prec_o=int8 -save_unquant=1"
"-fquant=1 -prec_o=fp8 -save_unquant=1"
"-fquant=2 -prec_o=fp8 -save_unquant=1"
)
m_n_list=(
"99 13" "17 16" "1 100" "4 128" "80 127"
"7 599" "19 512" "11 510" "91 636"
"31 1024" "8 1501" "3 1826" "5 2040"
"7 2734" "1 3182" "9 4096" "3 8192"
)
### Add special stride test ###
m_n_stride_list=(
"22 255 -x_stride=256 -xr_stride=256 -y_stride=256 -yr_stride=256"
"33 313 -x_stride=1000 -xr_stride=1000 -y_stride=1000 -yr_stride=1000"
"171 676 -x_stride=818 -xr_stride=818 -y_stride=818 -yr_stride=818"
"12 768 -x_stride=800 -xr_stride=800 -y_stride=800 -yr_stride=800"
"100 766 -x_stride=812 -xr_stride=812 -y_stride=812 -yr_stride=812"
"64 1000 -x_stride=1004 -xr_stride=1004 -y_stride=1004 -yr_stride=1004"
)
for fquant in "${fquant_list[@]}"; do
for pr_i in "fp16" "bf16"; do
for fadd in "0" "1"; do
for s in "0" "1"; do
for pair in "${m_n_list[@]}"; do
m=$(echo $pair | cut -d ' ' -f1)
n=$(echo $pair | cut -d ' ' -f2)
run_case "$pr_i" "$fadd" "$s" "$fquant" "$m" "$n" ""
done
### Running tests with stride ###
for triple in "${m_n_stride_list[@]}"; do
m=$(echo $triple | cut -d ' ' -f1)
n=$(echo $triple | cut -d ' ' -f2)
stride_args=$(echo $triple | cut -d ' ' -f3-)
run_case "$pr_i" "$fadd" "$s" "$fquant" "$m" "$n" "$stride_args"
done
done
done
done
done
# Special two-pass only
for pr_i in "fp16" "bf16"; do
for fadd in "0" "1"; do
for s in "0" "1"; do
run_case "$pr_i" "$fadd" "$s" "" "1" "10547" ""
done
done
done
# Summary
echo "=============================="
echo "Total cases: $total"
echo "Valid cases: $valid"
accuracy=$(awk "BEGIN {printf \"%.2f\", ($valid / $total) * 100}")
echo "Accuracy: $accuracy%"
echo "=============================="