Fix the gfx950 numerical errors (#2911)

* Update grouped_gemm example and pipeline

* find the root cause error in did not enable the transpose in gfx950 correctly

* Fix v3 pipeline, row and col major

* Disable f8 datatype tests, it fails on gfx950

* fix the abd test by clear the runtime argument unsupported

---------

Co-authored-by: AviralGoelAMD <aviral.goel@amd.com>
Co-authored-by: Mateusz Ozga <mateusz.ozga@amd.com>

[ROCm/composable_kernel commit: b159841a06]
This commit is contained in:
Thomas Ning
2025-09-23 22:54:52 -07:00
committed by GitHub
parent 651a5dd0b9
commit bdea637a15
9 changed files with 63 additions and 123 deletions

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@@ -183,12 +183,24 @@ int run_grouped_gemm_example_with_layouts(int argc,
if(!valid_input_data(group_count, Ms, Ns, Ks, stride_As, stride_Bs, stride_Cs))
{
std::cout << "Please check the input data. Default values will be used." << std::endl;
// Clear existing (invalid) data before adding defaults
Ms.clear();
Ns.clear();
Ks.clear();
stride_As.clear();
stride_Bs.clear();
stride_Cs.clear();
stride_AQs.clear();
stride_BQs.clear();
for(int i = 0; i < group_count; i++)
{
Ms.push_back(256 + 256 * i);
Ns.push_back(256 + 512 * i);
Ks.push_back(512 + 128 * i);
// Let get_default_stride calculate based on layout
stride_As.push_back(0);
stride_Bs.push_back(0);
stride_Cs.push_back(0);

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@@ -172,15 +172,25 @@ int run_grouped_gemm_example_with_layouts(int argc,
std::cout << "Default values: Ms (256, 512, 768, 1024..), Ns (256, 768, 1280..), Ks (512, "
"896, 1280..)"
<< std::endl;
// Clear existing (invalid) data before adding defaults
Ms.clear();
Ns.clear();
Ks.clear();
stride_As.clear();
stride_Bs.clear();
stride_Cs.clear();
for(int i = 0; i < group_count; i++)
{
Ms.push_back(256 + 256 * i);
Ns.push_back(256 + 512 * i);
Ks.push_back(512 + 384 * i);
stride_As.push_back(Ks[i]);
stride_Bs.push_back(Ks[i]);
stride_Cs.push_back(Ns[i]);
// Set default strides based on layout later using get_default_stride
stride_As.push_back(0);
stride_Bs.push_back(0);
stride_Cs.push_back(0);
}
}

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@@ -132,6 +132,10 @@ struct GemmKernelMultiABD
static constexpr index_t NumBTensor = BsDataType::size();
static constexpr index_t NumDTensor = DsDataType::size();
using ADataType = remove_cvref_t<std::tuple_element_t<0, AsDataType>>;
using BDataType = remove_cvref_t<std::tuple_element_t<0, BsDataType>>;
using DDataType = remove_cvref_t<std::tuple_element_t<0, DsDataType>>;
CK_TILE_HOST static auto GetName() -> const std::string
{
return UniversalGemmKernel::GetName();
@@ -181,6 +185,14 @@ struct GemmKernelMultiABD
{
return false;
}
// Currently MultiABD kernel doesn't support F8 data type
if(ck_tile::get_device_name() == "gfx950" &&
(std::is_same<ck_tile::fp8_t, ADataType>::value ||
std::is_same<ck_tile::fp8_t, BDataType>::value ||
std::is_same<ck_tile::fp8_t, DDataType>::value))
{
return false;
}
return UniversalGemmKernel::IsSupportedArgument(kargs);
}

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@@ -530,7 +530,8 @@ struct GemmPipelineAgBgCrCompV3 : public BaseGemmPipelineAgBgCrCompV3<Problem>
move_tile_window(b_copy_dram_window, b_dram_tile_window_step);
block_sync_lds();
block_gemm.LocalPrefetch(a_lds_gemm_window, b_lds_gemm_window);
block_gemm.LocalPrefetch(
a_lds_gemm_window, b_lds_gemm_window, is_a_load_tr_v, is_b_load_tr_v);
__builtin_amdgcn_sched_barrier(0);
@@ -542,7 +543,7 @@ struct GemmPipelineAgBgCrCompV3 : public BaseGemmPipelineAgBgCrCompV3<Problem>
{
block_sync_lds();
if constexpr(is_a_col_major)
if constexpr(is_a_col_major && !is_a_load_tr_v())
{
auto a_shuffle_tmp = make_static_distributed_tensor<ADataType>(
Policy::template MakeShuffledARegTileDistribution<Problem>());
@@ -553,7 +554,7 @@ struct GemmPipelineAgBgCrCompV3 : public BaseGemmPipelineAgBgCrCompV3<Problem>
{
Base::LocalPrefill(a_copy_lds_window, elementwise_As_res);
}
if constexpr(is_b_row_major)
if constexpr(is_b_row_major && !is_b_load_tr_v())
{
auto b_shuffle_tmp = make_static_distributed_tensor<BDataType>(
Policy::template MakeShuffledBRegTileDistribution<Problem>());
@@ -577,7 +578,8 @@ struct GemmPipelineAgBgCrCompV3 : public BaseGemmPipelineAgBgCrCompV3<Problem>
block_sync_lds();
block_gemm.LocalPrefetch(a_lds_gemm_window, b_lds_gemm_window);
block_gemm.LocalPrefetch(
a_lds_gemm_window, b_lds_gemm_window, is_a_load_tr_v, is_b_load_tr_v);
HotLoopScheduler();
__builtin_amdgcn_sched_barrier(0);
@@ -596,7 +598,7 @@ struct GemmPipelineAgBgCrCompV3 : public BaseGemmPipelineAgBgCrCompV3<Problem>
block_gemm(c_block_tile, a_lds_gemm_window, b_lds_gemm_window);
block_sync_lds();
if constexpr(is_a_col_major)
if constexpr(is_a_col_major && !is_a_load_tr_v())
{
auto a_shuffle_tmp = make_static_distributed_tensor<ADataType>(
Policy::template MakeShuffledARegTileDistribution<Problem>());
@@ -607,7 +609,7 @@ struct GemmPipelineAgBgCrCompV3 : public BaseGemmPipelineAgBgCrCompV3<Problem>
{
Base::LocalPrefill(a_copy_lds_window, elementwise_As_res);
}
if constexpr(is_b_row_major)
if constexpr(is_b_row_major && !is_b_load_tr_v())
{
auto b_shuffle_tmp = make_static_distributed_tensor<BDataType>(
Policy::template MakeShuffledBRegTileDistribution<Problem>());
@@ -619,7 +621,8 @@ struct GemmPipelineAgBgCrCompV3 : public BaseGemmPipelineAgBgCrCompV3<Problem>
Base::LocalPrefill(b_copy_lds_window, elementwise_Bs_res);
}
block_sync_lds();
block_gemm.LocalPrefetch(a_lds_gemm_window, b_lds_gemm_window);
block_gemm.LocalPrefetch(
a_lds_gemm_window, b_lds_gemm_window, is_a_load_tr_v, is_b_load_tr_v);
block_gemm(c_block_tile, a_lds_gemm_window, b_lds_gemm_window);
}
// __builtin_amdgcn_sched_barrier(0);

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@@ -100,7 +100,7 @@ struct GemmPipelineProblemBase
{
constexpr index_t PackedSize =
ck_tile::numeric_traits<remove_cvref_t<ADataType>>::PackedSize;
if constexpr(std::is_same_v<AsLayout, ck_tile::tensor_layout::gemm::ColumnMajor>)
if constexpr(std::is_same_v<ALayout, ck_tile::tensor_layout::gemm::ColumnMajor>)
{
constexpr index_t pixels_per_thread =
BlockGemmShape::kM * BlockGemmShape::kK / kBlockSize;
@@ -118,7 +118,7 @@ struct GemmPipelineProblemBase
{
constexpr index_t PackedSize =
ck_tile::numeric_traits<remove_cvref_t<BDataType>>::PackedSize;
if constexpr(std::is_same_v<BsLayout, ck_tile::tensor_layout::gemm::RowMajor>)
if constexpr(std::is_same_v<BLayout, ck_tile::tensor_layout::gemm::RowMajor>)
{
constexpr index_t pixels_per_thread =
BlockGemmShape::kN * BlockGemmShape::kK / kBlockSize;

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@@ -5,8 +5,8 @@ if(CK_USE_OCP_FP8)
endif()
if(GPU_TARGETS MATCHES "gfx94" OR GPU_TARGETS MATCHES "gfx95")
add_gtest_executable(test_gemm_multi_abd_cshuffle test_gemm_multi_abd_cshuffle.cpp)
add_gtest_executable(test_gemm_multi_abd_default2d test_gemm_multi_abd_default2d.cpp)
target_compile_definitions(test_gemm_multi_abd_cshuffle PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
target_compile_definitions(test_gemm_multi_abd_default2d PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
add_gtest_executable(test_ck_tile_gemm_multi_abd_cshuffle test_gemm_multi_abd_cshuffle.cpp)
add_gtest_executable(test_ck_tile_gemm_multi_abd_default2d test_gemm_multi_abd_default2d.cpp)
target_compile_definitions(test_ck_tile_gemm_multi_abd_cshuffle PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
target_compile_definitions(test_ck_tile_gemm_multi_abd_default2d PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
endif()

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@@ -24,14 +24,16 @@ using KernelTypes = ::testing::Types<
std::tuple< Row, Row, Col, Col, Row, Row, Row, F16, F16, F16, F16, F32, F32, F32, F16, AddScale, AddScale, ElementWiseAddAdd, std::true_type>,
std::tuple< Row, Row, Col, Col, Row, Row, Row, F16, F16, F16, F16, F32, F32, F32, F16, AddScale, AddScale, ElementWiseAddAdd, std::true_type>,
std::tuple< Row, Row, Col, Col, Row, Row, Row, F8, F8, F8, F8, BF16, BF16, F32, F32, AddScale, AddScale, ElementWiseAddAdd, std::true_type>,
std::tuple< Row, Row, Col, Col, Row, Row, Row, F8, F8, F8, F8, F8, F8, F32, F32, AddScale, AddScale, ElementWiseAddAdd, std::true_type>,
std::tuple< Row, Row, Col, Col, Row, Row, Row, F16, F16, F16, F16, F16, F16, F32, F16, AddScale, AddScale, MultiplyMultiply, std::true_type>,
std::tuple< Row, Row, Col, Col, Row, Row, Row, F16, F16, F16, F16, BF16, BF16, F32, F32, AddScale, AddScale, MultiplyMultiply, std::true_type>,
std::tuple< Row, Row, Col, Col, Row, Row, Row, F16, F16, F16, F16, F32, F32, F32, F32, AddScale, AddScale, MultiplyMultiply, std::true_type>,
std::tuple< Row, Row, Col, Col, Row, Row, Row, F16, F16, F16, F16, F32, F32, F32, F16, AddScale, AddScale, MultiplyMultiply, std::true_type>,
std::tuple< Row, Row, Col, Col, Row, Row, Row, F8, F8, F8, F8, BF16, BF16, F32, F32, AddScale, AddScale, MultiplyMultiply, std::true_type>,
std::tuple< Row, Row, Col, Col, Row, Row, Row, F8, F8, F8, F8, F8, F8, F32, F32, AddScale, AddScale, MultiplyMultiply, std::true_type>
std::tuple< Row, Row, Col, Col, Row, Row, Row, F8, F8, F8, F8, BF16, BF16, F32, F32, AddScale, AddScale, MultiplyMultiply, std::true_type>
// Currently MultiABD kernel doesn't support F8 data type
//std::tuple< Row, Row, Col, Col, Row, Row, Row, F8, F8, F8, F8, F8, F8, F32, F32, AddScale, AddScale, ElementWiseAddAdd, std::true_type>,
//std::tuple< Row, Row, Col, Col, Row, Row, Row, F8, F8, F8, F8, F8, F8, F32, F32, AddScale, AddScale, MultiplyMultiply, std::true_type>,
>;
// clang-format on

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@@ -22,17 +22,17 @@ using KernelTypes = ::testing::Types<
// A0Layout, A1Layout, B0Layout, B1Layout CLayout, D0Layout, D1Layout, A0DataType, A01DataType B0DataType, B0DataType, D0DataType, D1DataType, AccDataType, EDataType, AElementWiseFn, BElementWiseFn, CDElementWiseFn, UseCshuffleEpilog
std::tuple< Row, Row, Col, Col, Row, Row, Row, F16, F16, F16, F16, F32, F32, F32, F16, AddScale, AddScale, ElementWiseAddAdd, std::false_type>,
std::tuple< Row, Row, Col, Col, Row, Row, Row, F16, F16, F16, F16, F32, F32, F32, F16, AddScale, AddScale, ElementWiseAddAdd, std::false_type>,
std::tuple< Row, Row, Col, Col, Row, Row, Row, F8, F8, F8, F8, BF16, BF16, F32, F32, AddScale, AddScale, ElementWiseAddAdd, std::false_type>,
std::tuple< Row, Row, Col, Col, Row, Row, Row, F16, F16, F16, F16, F32, F32, F32, F32, AddScale, AddScale, ElementWiseAddAdd, std::false_type>,
std::tuple< Row, Row, Col, Col, Row, Row, Row, F16, F16, F16, F16, BF16, BF16, F32, BF16, AddScale, AddScale, ElementWiseAddAdd, std::false_type>,
std::tuple< Row, Row, Col, Col, Row, Row, Row, F8, F8, F8, F8, BF16, BF16, F32, BF16, AddScale, AddScale, ElementWiseAddAdd, std::false_type>,
std::tuple< Row, Row, Col, Col, Row, Row, Row, F16, F16, F16, F16, F16, F16, F32, F16, AddScale, AddScale, MultiplyMultiply, std::false_type>,
std::tuple< Row, Row, Col, Col, Row, Row, Row, F16, F16, F16, F16, F32, F32, F32, F16, AddScale, AddScale, MultiplyMultiply, std::false_type>,
std::tuple< Row, Row, Col, Col, Row, Row, Row, F8, F8, F8, F8, BF16, BF16, F32, F32, AddScale, AddScale, MultiplyMultiply, std::false_type>,
std::tuple< Row, Row, Col, Col, Row, Row, Row, F16, F16, F16, F16, F32, F32, F32, F32, AddScale, AddScale, MultiplyMultiply, std::false_type>,
std::tuple< Row, Row, Col, Col, Row, Row, Row, F16, F16, F16, F16, BF16, BF16, F32, BF16, AddScale, AddScale, MultiplyMultiply, std::false_type>,
std::tuple< Row, Row, Col, Col, Row, Row, Row, F8, F8, F8, F8, BF16, BF16, F32, BF16, AddScale, AddScale, MultiplyMultiply, std::false_type>
std::tuple< Row, Row, Col, Col, Row, Row, Row, F16, F16, F16, F16, BF16, BF16, F32, BF16, AddScale, AddScale, MultiplyMultiply, std::false_type>
// Currently MultiABD kernel doesn't support F8 data type
//std::tuple< Row, Row, Col, Col, Row, Row, Row, F8, F8, F8, F8, BF16, BF16, F32, BF16, AddScale, AddScale, ElementWiseAddAdd, std::false_type>,
//std::tuple< Row, Row, Col, Col, Row, Row, Row, F8, F8, F8, F8, BF16, BF16, F32, BF16, AddScale, AddScale, MultiplyMultiply, std::false_type>,
>;
// clang-format on

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@@ -1,104 +1,5 @@
#pragma once
TYPED_TEST(TestCkTileGemmMultiABD, TestCkTileGemmMultiABDKBatch1CShuffle_256x512x256)
{
constexpr int M = 256;
constexpr int N = 512;
constexpr int K = 256;
constexpr int kBatch = 1;
EXPECT_EQ(this->Run(M, N, K, kBatch), true);
}
TYPED_TEST(TestCkTileGemmMultiABD, TestCkTileGemmMultiABDKBatch1CShuffle_512x256x256)
{
constexpr int M = 512;
constexpr int N = 256;
constexpr int K = 256;
constexpr int kBatch = 1;
EXPECT_EQ(this->Run(M, N, K, kBatch), true);
}
TYPED_TEST(TestCkTileGemmMultiABD, TestCkTileGemmMultiABDKBatch1CShuffle_512x512x256)
{
constexpr int M = 512;
constexpr int N = 512;
constexpr int K = 256;
constexpr int kBatch = 1;
EXPECT_EQ(this->Run(M, N, K, kBatch), true);
}
TYPED_TEST(TestCkTileGemmMultiABD, TestCkTileGemmMultiABDKBatch1CShuffle_256x256x256)
{
constexpr int M = 256;
constexpr int N = 256;
constexpr int K = 256;
constexpr int kBatch = 1;
EXPECT_EQ(this->Run(M, N, K, kBatch), true);
}
TYPED_TEST(TestCkTileGemmMultiABD, TestCkTileGemmMultiABDKBatch1CShuffle_512x768x256)
{
constexpr int M = 512;
constexpr int N = 768;
constexpr int K = 256;
constexpr int kBatch = 1;
EXPECT_EQ(this->Run(M, N, K, kBatch), true);
}
TYPED_TEST(TestCkTileGemmMultiABD, TestCkTileGemmMultiABDKBatch1CShuffle_512x1280x256)
{
constexpr int M = 512;
constexpr int N = 1280;
constexpr int K = 256;
constexpr int kBatch = 1;
EXPECT_EQ(this->Run(M, N, K, kBatch), true);
}
TYPED_TEST(TestCkTileGemmMultiABD, TestCkTileGemmMultiABDKBatch1CShuffle_256x1280x256)
{
constexpr int M = 256;
constexpr int N = 1280;
constexpr int K = 256;
constexpr int kBatch = 1;
EXPECT_EQ(this->Run(M, N, K, kBatch), true);
}
TYPED_TEST(TestCkTileGemmMultiABD, TestCkTileGemmMultiABDKBatch1CShuffle_768x512x256)
{
constexpr int M = 768;
constexpr int N = 512;
constexpr int K = 256;
constexpr int kBatch = 1;
EXPECT_EQ(this->Run(M, N, K, kBatch), true);
}
TYPED_TEST(TestCkTileGemmMultiABD, TestCkTileGemmMultiABDKBatch1CShuffle_1280x512x256)
{
constexpr int M = 1280;
constexpr int N = 512;
constexpr int K = 256;
constexpr int kBatch = 1;
EXPECT_EQ(this->Run(M, N, K, kBatch), true);
}
TYPED_TEST(TestCkTileGemmMultiABD, TestCkTileGemmMultiABDKBatch1CShuffle_1280x256x256)
{
constexpr int M = 1280;
constexpr int N = 256;
constexpr int K = 256;
constexpr int kBatch = 1;
EXPECT_EQ(this->Run(M, N, K, kBatch), true);
}
TYPED_TEST(TestCkTileGemmMultiABD, TestCkTileGemmMultiABDKBatch2CShuffle_512x512x512)
{