[CK_TILE] Migrate CK Tile examples to Tests to autorun on CI (#2421)

[CK_TILE] Add new ck tile unit test

* Add new ck tile unit test smoke-gemm-universal
* Add new ck tile unit test smoke-gemm-basic
* Add new ck tile unit test topk_softmax
* Add new ck tile unit test add_rmsnorm2d_rdquant_fwd
This commit is contained in:
Cong Ma
2025-07-22 08:15:18 -06:00
committed by GitHub
parent c9886109b4
commit f102eedfb3
61 changed files with 6298 additions and 0 deletions

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function(add_tile_topk_softmax_test SUFFIX)
set(TEST_NAME "test_ck_tile_topk_softmax_${SUFFIX}")
add_test_executable(${TEST_NAME} test_topk_softmax_${SUFFIX}.cpp test_topk_softmax_api.cpp)
target_include_directories(${TEST_NAME} PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/)
set(TEST_TOPK_SOFTMAX_COMPILE_OPTIONS)
# NOTE: we turn off undefined-func-template to let source compile without explicit declare function specializations
list(APPEND TEST_TOPK_SOFTMAX_COMPILE_OPTIONS -Wno-undefined-func-template -Wno-float-equal)
# list(APPEND TEST_TOPK_SOFTMAX_COMPILE_OPTIONS -v --save-temps -Wno-gnu-line-marker)
target_compile_options(${TEST_NAME} PRIVATE ${TEST_TOPK_SOFTMAX_COMPILE_OPTIONS})
endfunction()
# Currently ck_tile is only built on gfx9
if(GPU_TARGETS MATCHES "gfx9")
add_tile_topk_softmax_test(fp16)
add_tile_topk_softmax_test(bf16)
else()
message(DEBUG "Skipping tile topk_softmax tests for current target")
endif()

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// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include <cassert>
#include <cstdlib>
#include <time.h>
#include "test_topk_softmax_api.hpp"
// CPU reference
template <typename InputType, typename WeightType, typename IndexType = ck_tile::index_t>
auto reference_topk_softmax(const ck_tile::HostTensor<InputType>& x,
ck_tile::index_t k,
ck_tile::index_t dim = -1,
bool largest = true,
bool sorted = true)
{
using namespace ck_tile;
auto y = reference_softmax<InputType, WeightType, WeightType>(x, dim);
auto [y_values, y_indices] = reference_topk(y, k, dim, largest, sorted);
return ck_tile::make_tuple(y_values, y_indices);
}
template <typename InputType, typename WeightType, typename IndexType = ck_tile::index_t>
auto reference_topk_softmax(const ck_tile::HostTensor<InputType>& x,
ck_tile::HostTensor<WeightType>& y_values,
ck_tile::HostTensor<IndexType>& y_indices,
ck_tile::index_t k,
ck_tile::index_t dim = -1,
bool largest = true,
bool sorted = true)
{
using namespace ck_tile;
auto y = reference_softmax<InputType, WeightType, WeightType>(x, dim);
reference_topk(y, y_values, y_indices, k, dim, largest, sorted);
}
// different threshold for different dtype
template <typename DataType>
auto get_elimit(std::string /*init_method*/)
{
double rtol = 1e-3;
double atol = 1e-3;
return ck_tile::make_tuple(rtol, atol);
}
template <>
auto get_elimit<ck_tile::bf16_t>(std::string /*init_method*/)
{
double rtol = 1e-2;
double atol = 1e-2;
return ck_tile::make_tuple(rtol, atol);
}
template <>
auto get_elimit<ck_tile::fp8_t>(std::string init_method)
{
if(init_method == "ui" || init_method == "ni")
{
unsigned max_rounding_point_distance = 0;
double atol = 2e-3;
return ck_tile::make_tuple(max_rounding_point_distance, atol);
}
else
{
unsigned max_rounding_point_distance = 1;
double atol = 0.0625;
return ck_tile::make_tuple(max_rounding_point_distance, atol);
}
}
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", "fp16", "input data type. fp16/fp32 (representing 8/16/32 bit data)")
.insert("pr_w", "fp32", "output weight data type(currently only fp32 supported now)")
.insert("t", "32", "number of input tokens")
.insert("e", "8", "number of experts")
.insert("k", "2", "topk")
.insert("st_i", "-1", "row stride of input, -1 means same as experts")
.insert("st_o", "-1", "row stride of output/indices, -1 means same as topk")
.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 InputType, typename WeightType, typename IndexType = ck_tile::index_t>
bool test_topk_softmax(ck_tile::ArgParser args)
{
int validate = args.get_int("v");
std::string input_prec = args.get_str("pr_i");
std::string weight_prec = args.get_str("pr_w");
int tokens = args.get_int("t");
int experts = args.get_int("e");
int topk = args.get_int("k");
int seed = args.get_int("seed");
int stride_input = args.get_int("st_i");
int stride_output = args.get_int("st_o");
int kname = args.get_int("kname");
int warmup = args.get_int("warmup");
int repeat = args.get_int("repeat");
if(stride_input < 0)
{
stride_input = experts;
}
if(stride_output < 0)
{
stride_output = topk;
}
assert(stride_input >= experts);
assert(stride_output >= topk);
if(seed < 0)
{
seed = std::time(nullptr);
}
if(topk > experts)
{
printf("topk:%d value should be smaller than, or equal to number of experts:%d\n",
topk,
experts);
return false;
}
// tokens already considered batch size
ck_tile::HostTensor<InputType> x_host({tokens, experts}, {stride_input, 1});
ck_tile::HostTensor<WeightType> value_host({tokens, topk}, {stride_output, 1});
ck_tile::HostTensor<IndexType> index_host({tokens, topk}, {stride_output, 1});
{
// random require per-row unique
auto rand_gen = ck_tile::FillUniformDistribution_Unique<InputType>{
-5.f, 5.f, static_cast<uint32_t>(seed)};
for(int i_t = 0; i_t < tokens; i_t++)
{
ck_tile::HostTensor<InputType> x_row({experts});
rand_gen(x_row);
std::copy(x_row.begin(), x_row.end(), x_host.begin() + i_t * stride_input);
rand_gen.clear();
}
}
ck_tile::DeviceMem x_dev(x_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem value_dev(value_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem index_dev(index_host.get_element_space_size_in_bytes());
x_dev.ToDevice(x_host.data());
topk_softmax_trait trait{input_prec, weight_prec, experts};
topk_softmax_kargs karg{x_dev.GetDeviceBuffer(),
value_dev.GetDeviceBuffer(),
index_dev.GetDeviceBuffer(),
tokens,
experts,
topk,
stride_input,
stride_output};
ck_tile::stream_config sc{nullptr,
true,
/* log_level = */ (kname ? 1 : 0),
warmup,
repeat};
auto ms = topk_softmax(trait, karg, sc);
printf("[%s|%s]tokens:%d, experts:%d, topk:%d, st_i:%d, st_o:%d, ms:%f, ",
input_prec.c_str(),
weight_prec.c_str(),
tokens,
experts,
topk,
stride_input,
stride_output,
ms);
if(ms < 0)
printf("not supported\n");
fflush(stdout);
if(ms < 0)
{
return false;
}
value_dev.FromDevice(value_host.data());
index_dev.FromDevice(index_host.data());
bool rtn = true;
if(validate)
{
ck_tile::HostTensor<WeightType> value_ref({tokens, topk}, {stride_output, 1});
ck_tile::HostTensor<IndexType> index_ref({tokens, topk}, {stride_output, 1});
reference_topk_softmax<InputType, WeightType, IndexType>(
x_host, value_ref, index_ref, topk);
auto [rtol, atol] = get_elimit<InputType>("");
for(int i_t = 0; i_t < tokens; i_t++)
{
auto s_begin = std::vector<size_t>{static_cast<size_t>(i_t), static_cast<size_t>(0)};
auto s_end =
std::vector<size_t>{static_cast<size_t>(i_t + 1), static_cast<size_t>(topk)};
auto s_value_host = value_host.slice(s_begin, s_end);
auto s_value_ref = value_ref.slice(s_begin, s_end);
rtn &= ck_tile::check_err(s_value_host,
s_value_ref,
std::string("[") + std::to_string(i_t) +
std::string("] Value Error:"),
rtol,
atol);
auto s_index_host = index_host.slice(s_begin, s_end);
auto s_index_ref = index_ref.slice(s_begin, s_end);
rtn &= ck_tile::check_err(s_index_host,
s_index_ref,
std::string("[") + std::to_string(i_t) +
std::string("] Index Error:"),
rtol,
atol);
}
}
printf("valid:%s\n", rtn ? "y" : "n");
fflush(stdout);
return rtn;
}
template <typename T>
int run_gemm_combinations(std::string const& data_type)
{
char bufs[7][64];
char* argv[7] = {bufs[0], bufs[1], bufs[2], bufs[3], bufs[4], bufs[5], bufs[6]};
std::vector<std::vector<std::string>> params = {
{"-t=80", "-e=17"},
{"-t=111", "-e=117"},
{"-t=1000", "-e=55"},
{"-t=99", "-e=180"},
{"-t=175", "-e=64", "-k=8"},
{"-t=65", "-e=8", "-k=2"},
{"-t=1", "-e=25"},
{"-t=31", "-e=19", "-k=15"},
{"-t=81", "-e=37", "-k=7"},
{"-t=199", "-e=128", "-k=13"},
{"-t=23", "-e=1", "-k=1"},
{"-t=127", "-e=99", "-k=19", "-st_i=233", "-st_o=31"},
{"-t=71", "-e=11", "-k=11", "-st_i=30", "-st_o=12"},
{"-t=1", "-e=1", "-k=1"},
{"-t=99", "-e=2", "-k=1", "-st_i=11", "-st_o=5"},
{"-t=333", "-e=99", "-k=13", "-st_i=191", "-st_o=17"}};
bool result = true;
std::string pr_i = "-pr_i=" + data_type;
strncpy(bufs[0], "test_topk_softmax_bf16", 64);
strncpy(bufs[1], pr_i.c_str(), 64);
for(size_t i = 0; i < params.size(); i++)
{
for(size_t j = 0; j < params[i].size(); j++)
{
strncpy(bufs[j + 2], params[i][j].c_str(), 64);
}
int argc = params[i].size() + 2;
auto [good_args, args] = create_args(argc, argv);
if(!good_args)
{
result = false;
}
result = test_topk_softmax<T, float, ck_tile::index_t>(args) && result;
}
return result ? 0 : -1;
}

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// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "test_topk_softmax_api.hpp"
#define TOPK_SOFTMAX_DISPATCH(experts_) \
constexpr ck_tile::index_t ts_experts = experts_; \
using ts_problem = ck_tile:: \
TopkSoftmaxWarpPerRowProblem<ts_input_type, ts_weight_type, ts_index_type, ts_experts>; \
using ts_pipeline = ck_tile::TopkSoftmaxWarpPerRowPipeline<ts_problem>; \
\
using kernel = ck_tile::TopkSoftmaxKernel<ts_pipeline>; \
\
auto kargs = kernel::MakeKargs(a); \
\
const dim3 grids = kernel::GridSize(a); \
constexpr dim3 blocks = kernel::BlockSize(); \
\
float ave_time = ck_tile::launch_kernel( \
s, ck_tile::make_kernel<blocks.x, 1>(kernel{}, grids, blocks, 0, kargs)); \
\
return ave_time;
float topk_softmax(topk_softmax_trait t, topk_softmax_kargs a, ck_tile::stream_config s)
{
if(t.input_type == "fp16" && t.weight_type == "fp32")
{
using ts_input_type = ck_tile::fp16_t;
using ts_weight_type = float;
using ts_index_type = ck_tile::index_t;
#if 1
if(t.experts <= 8)
{
TOPK_SOFTMAX_DISPATCH(8)
}
else if(t.experts <= 16)
{
TOPK_SOFTMAX_DISPATCH(16)
}
else if(t.experts <= 32)
{
TOPK_SOFTMAX_DISPATCH(32)
}
else if(t.experts <= 64)
{
TOPK_SOFTMAX_DISPATCH(64)
}
else if(t.experts <= 128)
{
TOPK_SOFTMAX_DISPATCH(128)
}
else if(t.experts <= 192)
{
TOPK_SOFTMAX_DISPATCH(192)
}
#else
if(t.experts <= 128)
{
TOPK_SOFTMAX_DISPATCH(128)
}
#endif
}
else if(t.input_type == "bf16" && t.weight_type == "fp32")
{
#if 1
using ts_input_type = ck_tile::bf16_t;
using ts_weight_type = float;
using ts_index_type = ck_tile::index_t;
if(t.experts <= 8)
{
TOPK_SOFTMAX_DISPATCH(8)
}
else if(t.experts <= 16)
{
TOPK_SOFTMAX_DISPATCH(16)
}
else if(t.experts <= 32)
{
TOPK_SOFTMAX_DISPATCH(32)
}
else if(t.experts <= 64)
{
TOPK_SOFTMAX_DISPATCH(64)
}
else if(t.experts <= 128)
{
TOPK_SOFTMAX_DISPATCH(128)
}
else if(t.experts <= 192)
{
TOPK_SOFTMAX_DISPATCH(192)
}
#endif
}
return -1;
}

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// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/host.hpp"
#include "ck_tile/ops/topk_softmax.hpp"
#include <string>
struct topk_softmax_trait
{
std::string input_type;
std::string weight_type; // currently always float
int experts;
};
struct topk_softmax_kargs : public ck_tile::TopkSoftmaxHostArgs
{
};
float topk_softmax(topk_softmax_trait t, topk_softmax_kargs a, ck_tile::stream_config s);

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// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "test_topk_softmax.hpp"
int main() { return run_gemm_combinations<ck_tile::bf16_t>("bf16"); }

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// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "test_topk_softmax.hpp"
int main() { return run_gemm_combinations<ck_tile::fp16_t>("fp16"); }