[rocm-libraries] ROCm/rocm-libraries#4299 (commit 668cd49)

173 implement device grouped gemm fixed nk for rdna4
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## Proposed changes

This PR adds an RDNA4 implementation of the device_grouped_gemm_fixed_nk
instance library using for WMMA.

The implementation is based on the existing
DeviceGroupedGemm_Xdl_Fixed_NK design and reuses the same high-level
structure, but replaces the XDL kernel with a WMMA-based one. It uses
the GridwiseGemm_wmma_cshuffle_v3 kernel.

At this stage, the focus is functional correctness and compatibility,
not performance tuning.

## Technical Details

- Device struct for grouped gemm fixed NK
- Example code for the WMMA version
- Unit tests for both new wmma implementation and the reference XDL code
(previously missing)
- Generic ck profiler interface with the purpose of calling unit tests.

## Checklist

Please put an into the boxes that apply. You can also fill these out
after creating the PR. If you're not sure, please don't hesitate to ask.

- [x] I have added tests relevant to the introduced functionality, and
the unit tests are passing locally
- [x] I have added the test to REGRESSION_TESTS list defined at the top
of CMakeLists.txt in tests/CMakeLists.txt, **IF** the test takes more
than 30 seconds to run.
- [ ] I have added inline documentation which enables the maintainers
with understanding the motivation
- [ ] I have removed the stale documentation which is no longer relevant
after this pull request
- [x] (If this change is user-facing) I have added release notes which
provide the end users with a brief summary of the improvement from this
pull request
- [x] I have run  on all changed files
- [x] Any dependent changes have been merged

## Discussion

If this is a relatively large or complex change, feel free to start a
discussion by explaining why you chose the solution you did and what
alternatives you considered
This commit is contained in:
Márton Bidlek
2026-02-19 08:13:46 +00:00
committed by assistant-librarian[bot]
parent c5ce5eee5b
commit 7b97e197ef
32 changed files with 2819 additions and 163 deletions

View File

@@ -18,6 +18,12 @@ if (CK_USE_XDL OR CK_USE_WMMA)
target_link_libraries(test_grouped_gemm_fastgelu PRIVATE utility device_grouped_gemm_fastgelu_instance)
add_dependencies(test_grouped_gemm test_grouped_gemm_fastgelu)
endif()
add_gtest_executable(test_grouped_gemm_fixed_nk test_grouped_gemm_fixed_nk.cpp)
if(result EQUAL 0)
target_link_libraries(test_grouped_gemm_fixed_nk PRIVATE utility device_grouped_gemm_fixed_nk_instance)
add_dependencies(test_grouped_gemm test_grouped_gemm_fixed_nk)
endif()
endif()
add_gtest_executable(test_grouped_gemm_interface test_grouped_gemm_interface_xdl.cpp)

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@@ -0,0 +1,84 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#include <tuple>
#include <vector>
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/utility/data_type.hpp"
#include "ck/utility/tuple.hpp"
#include "gtest/gtest.h"
#include "test_grouped_gemm_util.hpp"
ck::index_t param_mask = 0xffffff;
ck::index_t instance_index = -1;
using F16 = ck::half_t;
using BF16 = ck::bhalf_t;
using F8 = ck::f8_t;
using I8 = int8_t;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
template <typename Tuple>
class TestGroupedGemm
: public ck::test::TestGroupedGemm<Tuple, false, ck::test::FixedNKGroupedGemmProfiler>
{
public:
void SetUp() override
{
ck::test::TestGroupedGemm<Tuple, false, ck::test::FixedNKGroupedGemmProfiler>::SetUp();
#if defined(CK_USE_WMMA)
// The old XDL tests didn't fail if instances were not supported, so we want to keep that
// behaviour When compiling WMMA instances and WMMA is supported, then we'll fail if a
// specific case is not supported
this->fail_if_no_supported_instances_ =
ck::is_gfx11_supported() || ck::is_gfx12_supported();
#endif
}
};
// clang-format off
using KernelTypes = ::testing::Types<
#if CK_USE_OCP_FP8 || CK_USE_FNUZ_FP8 || defined(CK_USE_FP8_ON_UNSUPPORTED_ARCH) || \
defined(CK_USE_WMMA_FP8)
ck::Tuple<Row, Row, Row, F16, F8, F16>,
ck::Tuple<Row, Col, Row, F16, F8, F16>,
#endif
ck::Tuple<Row, Row, Row, F16, F16, F16>,
ck::Tuple<Row, Col, Row, F16, F16, F16>,
ck::Tuple<Row, Row, Row, BF16, BF16, BF16>,
ck::Tuple<Row, Col, Row, BF16, BF16, BF16>,
ck::Tuple<Row, Row, Row, BF16, I8, BF16>,
ck::Tuple<Row, Col, Row, BF16, I8, BF16>,
ck::Tuple<Row, Row, Row, F16, I8, F16>,
ck::Tuple<Row, Col, Row, F16, I8, F16>
>;
// clang-format on
TYPED_TEST_SUITE(TestGroupedGemm, KernelTypes);
#include "test_grouped_gemm_fixed_nk_cases.inc"
int main(int argc, char** argv)
{
testing::InitGoogleTest(&argc, argv);
if(argc == 1) {}
else if(argc == 3)
{
param_mask = strtol(argv[1], nullptr, 0);
instance_index = atoi(argv[2]);
}
else
{
std::cout << "Usage of " << argv[0] << std::endl;
std::cout << "Arg1,2: param_mask instance_index(-1 means all)" << std::endl;
}
return RUN_ALL_TESTS();
}

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@@ -0,0 +1,84 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#pragma once
TYPED_TEST(TestGroupedGemm, TinyCases)
{
const std::vector<int> Ms{2, 1};
constexpr int N = 768;
constexpr int K = 544;
const std::vector<int> Ns(Ms.size(), N);
const std::vector<int> Ks(Ms.size(), K);
this->Run(Ms, Ns, Ks);
}
TYPED_TEST(TestGroupedGemm, SmallCases)
{
const std::vector<int> Ms{2, 1, 3, 4, 5};
constexpr int N = 768;
constexpr int K = 544;
const std::vector<int> Ns(Ms.size(), N);
const std::vector<int> Ks(Ms.size(), K);
this->Run(Ms, Ns, Ks);
}
TYPED_TEST(TestGroupedGemm, MidCases)
{
const std::vector<int> Ms{167, 183, 177, 153, 139, 204};
constexpr int N = 768;
constexpr int K = 544;
const std::vector<int> Ns(Ms.size(), N);
const std::vector<int> Ks(Ms.size(), K);
this->Run(Ms, Ns, Ks);
}
TYPED_TEST(TestGroupedGemm, Regular)
{
const std::vector<int> Ms{64, 128, 256};
constexpr int N = 768;
constexpr int K = 320;
const std::vector<int> Ns(Ms.size(), N);
const std::vector<int> Ks(Ms.size(), K);
this->Run(Ms, Ns, Ks);
}
TYPED_TEST(TestGroupedGemm, MNKPadded)
{
const std::vector<int> Ms{127, 150, 188, 210};
constexpr int N = 136;
constexpr int K = 280;
const std::vector<int> Ns(Ms.size(), N);
const std::vector<int> Ks(Ms.size(), K);
this->Run(Ms, Ns, Ks);
}
TYPED_TEST(TestGroupedGemm, TestLargeKBatch)
{
// In some cases Split K is not supported. Running this test would fail since no instance will
// be supported, so we skip the test
if(!this->IsSplitKSupported())
GTEST_SKIP() << "Split-K not supported for for the current configuration (FP16/BF16 on "
"GFX11, or using CDE element-wise operation)";
const std::vector<int> Ms{188, 210};
constexpr int N = 768;
constexpr int K = 4096;
const std::vector<int> Ns(Ms.size(), N);
const std::vector<int> Ks(Ms.size(), K);
this->k_batches_ = {32, 64};
this->Run(Ms, Ns, Ks);
}

View File

@@ -16,6 +16,7 @@
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
#include "profiler/profile_grouped_gemm_impl.hpp"
#include "profiler/profile_grouped_gemm_fixed_nk_impl.hpp"
extern ck::index_t param_mask;
extern ck::index_t instance_index;
@@ -23,7 +24,124 @@ extern ck::index_t instance_index;
namespace ck {
namespace test {
template <typename Tuple, bool FailIfNoSupportedInstances = false>
struct DefaultGroupedGemmProfiler
{
template <typename ADataType,
typename BDataType,
typename EDataType,
typename AccDataType,
typename ALayout,
typename BLayout,
typename ELayout,
typename AElementOp,
typename BElementOp,
typename CDEElementOp>
static bool Run(bool verify,
int init_method,
bool log,
bool bench,
const std::vector<int>& Ms,
const std::vector<int>& Ns,
const std::vector<int>& Ks,
const std::vector<int>& StrideAs,
const std::vector<int>& StrideBs,
const std::vector<int>& StrideCs,
const std::vector<int>& kbatches,
int n_warmup,
int n_iter,
int instance_index,
bool fail_if_no_supported_instances)
{
return ck::profiler::profile_grouped_gemm_impl<ADataType,
BDataType,
EDataType,
AccDataType,
ALayout,
BLayout,
ELayout,
AElementOp,
BElementOp,
CDEElementOp>(
verify,
init_method,
log,
bench,
Ms,
Ns,
Ks,
StrideAs,
StrideBs,
StrideCs,
kbatches,
n_warmup,
n_iter,
instance_index,
fail_if_no_supported_instances);
}
};
struct FixedNKGroupedGemmProfiler
{
template <typename ADataType,
typename BDataType,
typename EDataType,
typename AccDataType,
typename ALayout,
typename BLayout,
typename CLayout>
static bool Run(bool verify,
int init_method,
bool log,
bool bench,
const std::vector<int>& Ms,
const std::vector<int>& Ns,
const std::vector<int>& Ks,
const std::vector<int>& StrideAs,
const std::vector<int>& StrideBs,
const std::vector<int>& StrideCs,
const std::vector<int>& kbatches,
int n_warmup,
int n_iter,
int /*instance_index*/,
bool /*fail_if_no_supported_instances*/)
{
bool pass = true;
for(int kbatch : kbatches)
{
try
{
pass &= ck::profiler::profile_grouped_gemm_fixed_nk_impl<ADataType,
BDataType,
EDataType,
AccDataType,
ALayout,
BLayout,
CLayout>(verify,
init_method,
log,
bench,
Ms,
Ns,
Ks,
StrideAs,
StrideBs,
StrideCs,
kbatch,
n_warmup,
n_iter);
}
catch(const std::exception& e)
{
std::cerr << e.what() << std::endl;
}
}
return pass;
}
};
template <typename Tuple,
bool FailIfNoSupportedInstances = false,
typename Profiler = ck::test::DefaultGroupedGemmProfiler>
class TestGroupedGemm : public testing::Test
{
protected:
@@ -76,7 +194,7 @@ class TestGroupedGemm : public testing::Test
}
else
{
k_batches_ = {1, 2, 3, 5, 8};
k_batches_ = {1, 2, 3, 4, 8};
}
}
@@ -146,31 +264,61 @@ class TestGroupedGemm : public testing::Test
const std::vector<int>& StrideCs,
const std::vector<int>& kbatches)
{
bool pass =
ck::profiler::profile_grouped_gemm_impl<ADataType,
BDataType,
EDataType,
float,
ALayout,
BLayout,
ELayout,
AElementOp,
BElementOp,
CDEElementOp>(verify_,
init_method_,
log_,
bench_,
Ms,
Ns,
Ks,
StrideAs,
StrideBs,
StrideCs,
kbatches,
n_warmup_,
n_iter_,
instance_index,
fail_if_no_supported_instances_);
bool pass = false;
using AccDataType = float;
if constexpr(std::is_same_v<Profiler, FixedNKGroupedGemmProfiler>)
{
pass = Profiler::template Run<ADataType,
BDataType,
EDataType,
AccDataType,
ALayout,
BLayout,
ELayout>(verify_,
init_method_,
log_,
bench_,
Ms,
Ns,
Ks,
StrideAs,
StrideBs,
StrideCs,
kbatches,
n_warmup_,
n_iter_,
instance_index,
fail_if_no_supported_instances_);
}
else
{
pass = Profiler::template Run<ADataType,
BDataType,
EDataType,
AccDataType,
ALayout,
BLayout,
ELayout,
AElementOp,
BElementOp,
CDEElementOp>(verify_,
init_method_,
log_,
bench_,
Ms,
Ns,
Ks,
StrideAs,
StrideBs,
StrideCs,
kbatches,
n_warmup_,
n_iter_,
instance_index,
fail_if_no_supported_instances_);
}
EXPECT_TRUE(pass);
}
};