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Merge branch 'develop' into jakpiase/gemm_pipeline_mem_skip_lds

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jakpiase
2025-04-09 12:20:35 +02:00
committed by GitHub
parent ff3aa68835 03ce8729fd
commit b4dc448e37
12 changed files with 141 additions and 60 deletions
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16
Jenkinsfile vendored
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@@ -331,8 +331,10 @@ def cmake_build(Map conf=[:]){
}
}
else{
// run unit tests
sh "make check"
// run unit tests unless building library for all targets
if (!params.BUILD_INSTANCES_ONLY){
sh "make check"
}
}
}
}
@@ -604,12 +606,9 @@ def Build_CK(Map conf=[:]){
else if ( arch_type == 6 ){
// run standard tests on gfx908
echo "Run performance tests"
sh "./run_performance_tests.sh 0 CI_${params.COMPILER_VERSION} ${env.BRANCH_NAME} ${NODE_NAME}"
archiveArtifacts "perf_gemm_gfx908.log"
sh "./run_gemm_performance_tests.sh 0 CI_${params.COMPILER_VERSION} ${env.BRANCH_NAME} ${NODE_NAME} gfx908"
archiveArtifacts "perf_onnx_gemm_gfx908.log"
archiveArtifacts "perf_resnet50_N256_gfx908.log"
archiveArtifacts "perf_resnet50_N4_gfx908.log"
stash includes: "perf_**.log", name: "perf_log_gfx908"
stash includes: "perf_onnx_gemm_gfx908.log", name: "perf_log_gfx908"
}
}
}
@@ -746,8 +745,7 @@ def process_results(Map conf=[:]){
//launch develop branch daily at 23:00 UT in FULL_QA mode and at 19:00 UT with latest staging compiler version
CRON_SETTINGS = BRANCH_NAME == "develop" ? '''0 23 * * * % RUN_FULL_QA=true;DISABLE_DL_KERNELS=true;ROCMVERSION=6.3;RUN_CK_TILE_FMHA_TESTS=true;RUN_CK_TILE_GEMM_TESTS=true
0 22 * * * % ROCMVERSION=6.3;BUILD_GFX908=true;BUILD_GFX12=false;RUN_PERFORMANCE_TESTS=false
0 21 * * * % ROCMVERSION=6.3;hipTensor_test=true;RUN_CODEGEN_TESTS=true
0 21 * * * % ROCMVERSION=6.3;hipTensor_test=true;RUN_CODEGEN_TESTS=true;BUILD_GFX908=true;
0 19 * * * % BUILD_DOCKER=true;COMPILER_VERSION=amd-staging;BUILD_COMPILER=/llvm-project/build/bin/clang++;USE_SCCACHE=false;NINJA_BUILD_TRACE=true
0 17 * * * % BUILD_DOCKER=true;COMPILER_VERSION=amd-mainline;BUILD_COMPILER=/llvm-project/build/bin/clang++;USE_SCCACHE=false;NINJA_BUILD_TRACE=true
0 15 * * * % BUILD_INSTANCES_ONLY=true;RUN_PERFORMANCE_TESTS=false;USE_SCCACHE=false

2
docs/index.rst
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@@ -10,7 +10,7 @@ Composable Kernel User Guide
The Composable Kernel library provides a programming model for writing performance critical kernels for machine learning workloads across multiple architectures including GPUs and CPUs, through general purpose kernel languages such as `HIP C++ <https://rocm.docs.amd.com/projects/HIP/en/latest/index.html>`_.
The Composable Kernel repository is located at `https://github.com/ROCm/composable-kernel <https://github.com/ROCm/composable-kernel>`_.
The Composable Kernel repository is located at `https://github.com/ROCm/composable_kernel <https://github.com/ROCm/composable_kernel>`_.
.. grid:: 2
:gutter: 3

2
docs/sphinx/requirements.in
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@@ -1,2 +1,2 @@
rocm-docs-core==1.18.1
rocm-docs-core==1.18.2
sphinxcontrib-bibtex==2.6.3

2
docs/sphinx/requirements.txt
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@@ -199,7 +199,7 @@ requests==2.32.3
# via
# pygithub
# sphinx
rocm-docs-core==1.18.1
rocm-docs-core==1.18.2
# via -r requirements.in
rpds-py==0.22.3
# via

6
include/ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp
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@@ -357,6 +357,12 @@ struct PassThrough
y = type_convert<half_t>(x);
}
template <>
__host__ __device__ void operator()<float, int32_t>(float& y, const int32_t& x) const
{
y = type_convert<float>(x);
}
template <>
__host__ __device__ void operator()<bhalf_t, bhalf_t>(bhalf_t& y, const bhalf_t& x) const
{

15
include/ck/utility/sequence.hpp
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@@ -184,6 +184,21 @@ struct Sequence
}
};
namespace impl {
template <typename T, T... Ints>
struct __integer_sequence;
template <index_t... Ints>
struct __integer_sequence<index_t, Ints...>
{
using seq_type = Sequence<Ints...>;
};
} // namespace impl
template <index_t N>
using make_index_sequence =
typename __make_integer_seq<impl::__integer_sequence, index_t, N>::seq_type;
// merge sequence
template <typename Seq, typename... Seqs>
struct sequence_merge

9
include/ck/utility/tuple_helper.hpp
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@@ -11,11 +11,16 @@
namespace ck {
template <typename F, index_t... ids>
__host__ __device__ constexpr auto generate_tuple_for(F&& f, Sequence<ids...>)
{
return make_tuple(f(Number<ids>{})...);
}
template <typename F, index_t N>
__host__ __device__ constexpr auto generate_tuple(F&& f, Number<N>)
{
return unpack([&f](auto&&... xs) { return make_tuple(f(xs)...); },
typename arithmetic_sequence_gen<0, N, 1>::type{});
return generate_tuple_for(f, make_index_sequence<N>{});
}
template <typename F, index_t N>

9
include/ck_tile/core/container/tuple.hpp
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@@ -396,11 +396,16 @@ struct tuple_array_impl<T, 1>
};
} // namespace impl
template <typename F, index_t... ids>
CK_TILE_HOST_DEVICE constexpr auto generate_tuple_for(F&& f, sequence<ids...>)
{
return make_tuple(f(number<ids>{})...);
}
template <typename F, index_t N>
CK_TILE_HOST_DEVICE constexpr auto generate_tuple(F&& f, number<N>)
{
return unpack([&f](auto&&... is) { return make_tuple(f(is)...); },
typename arithmetic_sequence_gen<0, N, 1>::type{});
return generate_tuple_for(f, make_index_sequence<N>{});
}
template <typename F, index_t N>

12
include/ck_tile/core/tensor/tensor_view.hpp
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@@ -411,18 +411,21 @@ struct null_tensor_view
};
template <address_space_enum BufferAddressSpace = address_space_enum::generic,
amd_buffer_coherence_enum Coherence = amd_buffer_coherence_enum::coherence_default,
typename DataType,
typename... Ts>
CK_TILE_HOST_DEVICE constexpr auto make_tensor_view(DataType* p,
const tensor_descriptor<Ts...>& desc)
{
auto buffer_view = make_buffer_view<BufferAddressSpace>(p, desc.get_element_space_size());
auto buffer_view =
make_buffer_view<BufferAddressSpace, Coherence>(p, desc.get_element_space_size());
return tensor_view<decltype(buffer_view), decltype(desc)>{buffer_view, desc};
}
template <address_space_enum BufferAddressSpace = address_space_enum::generic,
memory_operation_enum DstInMemOp = memory_operation_enum::set,
amd_buffer_coherence_enum Coherence = amd_buffer_coherence_enum::coherence_default,
typename DataType,
typename... Lengths,
typename... Strides,
@@ -441,12 +444,14 @@ make_naive_tensor_view(DataType* p,
number<GuaranteedLastDimensionVectorLength>{},
number<GuaranteedLastDimensionVectorStride>{});
auto buffer_view = make_buffer_view<BufferAddressSpace>(p, desc.get_element_space_size());
auto buffer_view =
make_buffer_view<BufferAddressSpace, Coherence>(p, desc.get_element_space_size());
return tensor_view<decltype(buffer_view), decltype(desc), DstInMemOp>{buffer_view, desc};
}
template <address_space_enum BufferAddressSpace = address_space_enum::generic,
amd_buffer_coherence_enum Coherence = amd_buffer_coherence_enum::coherence_default,
typename DataType,
typename... Lengths,
index_t GuaranteedLastDimensionVectorLength = -1>
@@ -458,7 +463,8 @@ make_naive_tensor_view_packed(DataType* p,
auto desc =
make_naive_tensor_descriptor_packed(lengths, number<GuaranteedLastDimensionVectorLength>{});
auto buffer_view = make_buffer_view<BufferAddressSpace>(p, desc.get_element_space_size());
auto buffer_view =
make_buffer_view<BufferAddressSpace, Coherence>(p, desc.get_element_space_size());
return tensor_view<decltype(buffer_view), decltype(desc)>{buffer_view, desc};
}

108
include/ck_tile/core/tensor/transpose_tile.hpp
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@@ -83,9 +83,6 @@ CK_TILE_DEVICE void transpose_tile2d_impl_in_thread(OutTensor& out_tensor,
constexpr index_t num_vec_in = vec_length_out;
constexpr index_t num_vec_out = vec_length_in;
using InVec = array<DataType, vec_length_in>;
using OutVec = array<DataType, vec_length_out>;
// SFC
constexpr auto scalars_per_access_arr = generate_array(
[&](auto i) { return (i == y_dim_vec_in or i == y_dim_vec_out) ? y_lengths[i] : 1; },
@@ -101,51 +98,84 @@ CK_TILE_DEVICE void transpose_tile2d_impl_in_thread(OutTensor& out_tensor,
static_assert(num_access > 0, "wrong! num_access should be larger than 0");
// in/out vectors to be transposed
thread_buffer<InVec, num_vec_in> in_vectors;
thread_buffer<OutVec, num_vec_out> out_vectors;
if constexpr(num_vec_in == 1 || num_vec_out == 1)
{
// loop over SFC
static_for<0, num_access, 1>{}([&](auto iAccess) {
// data index [y0, y1, ...] in the order of input tensor
constexpr auto idx_y = SFC_Y::get_index(iAccess);
// loop over SFC and do transpose
static_for<0, num_access, 1>{}([&](auto iAccess) {
// data index [y0, y1, ...] in the order of input tensor
constexpr auto idx_y_start = SFC_Y::get_index(iAccess);
constexpr index_t in_offset = y_in_desc.calculate_offset(idx_y);
constexpr index_t out_offset = y_out_desc.calculate_offset(idx_y);
// get input vectors
static_for<0, num_vec_in, 1>{}([&](auto i) {
constexpr auto idx_y_in = generate_tuple(
[&](auto ii) {
return ii == y_dim_vec_out ? idx_y_start[ii] + i : idx_y_start[ii];
},
number<NDimY>{});
constexpr index_t in_offset = y_in_desc.calculate_offset(idx_y_in);
static_assert(in_offset % vec_length_in == 0);
in_vectors(i).template get_as<InVec>()(I0) =
in_tensor.get_thread_buffer()
.template get_as<InVec>()[number<in_offset / vec_length_in>{}];
if constexpr(vec_length_in == 1)
{
out_tensor.get_thread_buffer()[number<out_offset>{}] =
in_tensor.get_thread_buffer()[number<in_offset>{}];
}
else
{
using Vec = array<DataType, vec_length_in>;
out_tensor.get_thread_buffer().template get_as<Vec>(
number<out_offset / vec_length_in>{}) =
in_tensor.get_thread_buffer().template get_as<Vec>(
number<in_offset / vec_length_in>{});
}
});
}
else
{
using InVec = array<DataType, vec_length_in>;
using OutVec = array<DataType, vec_length_out>;
// transpose
transpose_vectors<DataType, num_vec_in, num_vec_out>{}(in_vectors, out_vectors);
// in/out vectors to be transposed
thread_buffer<InVec, num_vec_in> in_vectors;
thread_buffer<OutVec, num_vec_out> out_vectors;
// set output vectors
static_for<0, num_vec_out, 1>{}([&](auto i) {
constexpr auto idx_y_out_tmp = generate_array(
[&](auto ii) { return ii == y_dim_vec_in ? idx_y_start[ii] + i : idx_y_start[ii]; },
number<NDimY>{});
// loop over SFC and do transpose
static_for<0, num_access, 1>{}([&](auto iAccess) {
// data index [y0, y1, ...] in the order of input tensor
constexpr auto idx_y_start = SFC_Y::get_index(iAccess);
constexpr auto idx_y_out =
container_reorder_given_new2old(idx_y_out_tmp, y_dim_out_to_in);
// get input vectors
static_for<0, num_vec_in, 1>{}([&](auto i) {
constexpr auto idx_y_in = generate_tuple(
[&](auto ii) {
return ii == y_dim_vec_out ? idx_y_start[ii] + i : idx_y_start[ii];
},
number<NDimY>{});
constexpr index_t out_offset = y_out_desc.calculate_offset(idx_y_out);
static_assert(out_offset % vec_length_out == 0);
constexpr index_t in_offset = y_in_desc.calculate_offset(idx_y_in);
static_assert(in_offset % vec_length_in == 0);
out_tensor.get_thread_buffer().template set_as<OutVec>(
number<out_offset / vec_length_out>{},
out_vectors[i].template get_as<OutVec>()[I0]);
in_vectors(i).template get_as<InVec>()(I0) =
in_tensor.get_thread_buffer()
.template get_as<InVec>()[number<in_offset / vec_length_in>{}];
});
// transpose
transpose_vectors<DataType, num_vec_in, num_vec_out>{}(in_vectors, out_vectors);
// set output vectors
static_for<0, num_vec_out, 1>{}([&](auto i) {
constexpr auto idx_y_out_tmp = generate_array(
[&](auto ii) {
return ii == y_dim_vec_in ? idx_y_start[ii] + i : idx_y_start[ii];
},
number<NDimY>{});
constexpr auto idx_y_out =
container_reorder_given_new2old(idx_y_out_tmp, y_dim_out_to_in);
constexpr index_t out_offset = y_out_desc.calculate_offset(idx_y_out);
static_assert(out_offset % vec_length_out == 0);
out_tensor.get_thread_buffer().template set_as<OutVec>(
number<out_offset / vec_length_out>{},
out_vectors[i].template get_as<OutVec>()[I0]);
});
});
});
}
}
} // namespace detail

12
library/src/tensor_operation_instance/gpu/gemm_multiply_multiply_wp/f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma_mn.hpp
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@@ -100,7 +100,17 @@ using device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma_mn_
//##########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 16, 64, 512, 16, 16, 16, 16, 1, 1, S<32, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<32, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 16, 1, 16>, S<4, 4, 1>, BlockGemmPipelineScheduler::Intrawave, BlkGemmPipeVer, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 16, 128, 512, 16, 16, 16, 16, 1, 2, S<32, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<32, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 16, 1, 16>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlkGemmPipeVer, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 16, 256, 512, 16, 16, 16, 16, 1, 4, S<32, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<32, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 16, 1, 16>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlkGemmPipeVer, F8>
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 16, 256, 512, 16, 16, 16, 16, 1, 4, S<32, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<32, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 16, 1, 16>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlkGemmPipeVer, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 128, 32, 16, 512, 16, 16, 16, 16, 1, 1, S<32, 4, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<32, 4, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 32, 1, 4>, S<4, 4, 1>, BlockGemmPipelineScheduler::Intrawave, BlkGemmPipeVer, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 128, 16, 32, 128, 16, 16, 16, 16, 1, 1, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 16, 1, 8>, S<4, 4, 1>, BlockGemmPipelineScheduler::Intrawave, BlkGemmPipeVer, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 128, 16, 32, 512, 16, 16, 16, 16, 1, 1, S<32, 4, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<32, 4, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 16, 1, 8>, S<4, 4, 1>, BlockGemmPipelineScheduler::Intrawave, BlkGemmPipeVer, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 16, 64, 512, 16, 16, 16, 16, 1, 1, S<32, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<32, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 16, 1, 16>, S<4, 4, 1>, BlockGemmPipelineScheduler::Intrawave, BlkGemmPipeVer, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 32, 64, 512, 16, 16, 16, 16, 1, 2, S<32, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<32, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlkGemmPipeVer, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 64, 64, 512, 16, 16, 32, 32, 1, 1, S<32, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<32, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlkGemmPipeVer, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 64, 16, 512, 16, 16, 16, 16, 1, 1, S<32, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<32, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 64, 1, 4>, S<4, 4, 1>, BlockGemmPipelineScheduler::Intrawave, BlkGemmPipeVer, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 64, 16, 512, 16, 16, 16, 16, 1, 1, S<32, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<32, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 64, 1, 4>, S<4, 4, 1>, BlockGemmPipelineScheduler::Intrawave, BlkGemmPipeVer, F8>
// clang-format on
>;

8
library/src/tensor_operation_instance/gpu/gemm_multiply_multiply_wp/f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma_mn.hpp
View File

@@ -115,7 +115,13 @@ using device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma_mn_p
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, F16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 16, 64, 256, 16, 16, 16, 16, 1, 1, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 16, 1, 16>, S<4, 4, 1>, BlockGemmPipelineScheduler::Intrawave, BlkGemmPipeVer, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, F16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 16, 128, 256, 16, 16, 16, 16, 1, 2, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 16, 1, 16>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlkGemmPipeVer, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, F16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 16, 256, 256, 16, 16, 16, 16, 1, 4, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 16, 1, 16>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlkGemmPipeVer, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, F16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 16, 512, 256, 16, 16, 16, 16, 1, 8, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 16, 1, 16>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlkGemmPipeVer, F8>
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, F16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 16, 512, 256, 16, 16, 16, 16, 1, 8, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 16, 1, 16>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlkGemmPipeVer, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, F16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 128, 16, 32, 512, 16, 16, 16, 16, 1, 1, S<32, 4, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<32, 4, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 16, 1, 8>, S<4, 4, 1>, BlockGemmPipelineScheduler::Intrawave, BlkGemmPipeVer, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, F16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 128, 16, 32, 128, 16, 16, 16, 16, 1, 1, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 16, 1, 8>, S<4, 4, 1>, BlockGemmPipelineScheduler::Intrawave, BlkGemmPipeVer, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, F16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 128, 16, 32, 256, 16, 16, 16, 16, 1, 1, S<16, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<16, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 16, 1, 8>, S<4, 4, 1>, BlockGemmPipelineScheduler::Intrawave, BlkGemmPipeVer, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, F16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 16, 64, 128, 8, 16, 16, 16, 1, 1, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 16, 1, 16>, S<4, 4, 1>, BlockGemmPipelineScheduler::Intrawave, BlkGemmPipeVer, F8>
// clang-format on
>;