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Conv:TF32: add more instances - 1 (#2867)

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* conv:tf32:add more instances
* add instances of device_grouped_conv_fwd_xdl_f32_comp_instances
* add instances of device_grouped_conv_fwd_xdl_f32_tf32_mem_instances
* add instances of device_grouped_conv_fwd_xdl_large_tensor_f32_tf32_instances
* remove gnhwc/ngchw/ngcdhw instances

[ROCm/composable_kernel commit: df97a286d5]
This commit is contained in:
yinglu
2025-09-25 09:27:18 +08:00
committed by GitHub
parent 0c45597a4e
commit c5fdba5a96
92 changed files with 4273 additions and 443 deletions
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7
include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_base.hpp
View File

@@ -54,6 +54,9 @@ struct BlockwiseGemmXdlops_pipeline_base
static constexpr auto xdlops_gemm =
XdlopsGemm<ComputeDataType, MPerXDL, NPerXDL, KPack, ComputeDataType, TransposeC>{};
using ComputeDataTypeBuf =
conditional_t<std::is_same<ComputeDataType, ck::tf32_t>::value, float, ComputeDataType>;
static constexpr index_t AMmaKStride = KPack;
static constexpr index_t BMmaKStride = KPack;
@@ -376,7 +379,7 @@ struct BlockwiseGemmXdlops_pipeline_base
make_tuple(Number<MRepeat>{}, Number<NRepeat>{}, xdlops_gemm.GetRegSizePerXdlops()));
using AThreadCopy = ThreadwiseTensorSliceTransfer_v4<ADataType,
ComputeDataType,
ComputeDataTypeBuf,
decltype(a_block_desc_m0_m1_m2_k),
decltype(a_thread_desc_),
Sequence<1, 1, 1, KPack>,
@@ -386,7 +389,7 @@ struct BlockwiseGemmXdlops_pipeline_base
A_K1>;
using BThreadCopy = ThreadwiseTensorSliceTransfer_v4<BDataType,
ComputeDataType,
ComputeDataTypeBuf,
decltype(b_block_desc_n0_n1_n2_k),
decltype(b_thread_desc_),
Sequence<1, 1, 1, KPack>,

56
include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v1.hpp
View File

@@ -140,6 +140,8 @@ struct BlockwiseGemmXdlops_pipeline_v1<BlockGemmPipelineScheduler::Intrawave,
using Base::AMmaKStride;
using Base::BMmaKStride;
using ComputeDataTypeBuf = typename Base::ComputeDataTypeBuf;
static constexpr index_t PrefetchStages = 1;
static constexpr index_t PrefillStages = 1;
static constexpr index_t GlobalBufferNum = 1;
@@ -185,9 +187,9 @@ struct BlockwiseGemmXdlops_pipeline_v1<BlockGemmPipelineScheduler::Intrawave,
CThreadBuffer& c_thread_buf,
index_t num_loop) const
{
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
a_thread_desc_.GetElementSpaceSize());
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
b_thread_desc_.GetElementSpaceSize());
// Global prefetch 1
@@ -240,20 +242,20 @@ struct BlockwiseGemmXdlops_pipeline_v1<BlockGemmPipelineScheduler::Intrawave,
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
@@ -301,20 +303,20 @@ struct BlockwiseGemmXdlops_pipeline_v1<BlockGemmPipelineScheduler::Intrawave,
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
typename vector_type<ComputeDataTypeBuf, xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
@@ -439,6 +441,8 @@ struct BlockwiseGemmXdlops_pipeline_v1<BlockGemmPipelineScheduler::Interwave,
using Base::a_block_desc_m0_m1_m2_k;
using Base::b_block_desc_n0_n1_n2_k;
using ComputeDataTypeBuf = typename Base::ComputeDataTypeBuf;
static constexpr index_t NumMacClusters = CK_EXPERIMENTAL_INTER_WAVE_SCHEDULING_MAC_CLUSTERS;
static constexpr index_t KPerInnerLoop = math::max(KPerThread / NumMacClusters, KPack);
static constexpr index_t KRepeat = KPerThread / KPerInnerLoop;
@@ -486,9 +490,9 @@ struct BlockwiseGemmXdlops_pipeline_v1<BlockGemmPipelineScheduler::Interwave,
CThreadBuffer& c_thread_buf,
index_t num_loop) const
{
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
a_thread_desc_.GetElementSpaceSize());
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
b_thread_desc_.GetElementSpaceSize());
// Global prefetch 1
@@ -551,20 +555,20 @@ struct BlockwiseGemmXdlops_pipeline_v1<BlockGemmPipelineScheduler::Interwave,
static_for<0, KPerInnerLoop, KPack>{}([&](auto k_) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, k_ + ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, k_ + ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
@@ -640,20 +644,20 @@ struct BlockwiseGemmXdlops_pipeline_v1<BlockGemmPipelineScheduler::Interwave,
static_for<0, KPerInnerLoop, KPack>{}([&](auto k_) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, k_ + ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, k_ + ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
@@ -704,7 +708,7 @@ struct BlockwiseGemmXdlops_pipeline_v1<BlockGemmPipelineScheduler::Interwave,
I1));
using AThreadCopy = ThreadwiseTensorSliceTransfer_v4<ADataType,
ComputeDataType,
ComputeDataTypeBuf,
decltype(a_block_desc_m0_m1_m2_k),
decltype(a_thread_desc_),
Sequence<1, 1, 1, KPerInnerLoop>,
@@ -714,7 +718,7 @@ struct BlockwiseGemmXdlops_pipeline_v1<BlockGemmPipelineScheduler::Interwave,
A_K1>;
using BThreadCopy = ThreadwiseTensorSliceTransfer_v4<BDataType,
ComputeDataType,
ComputeDataTypeBuf,
decltype(b_block_desc_n0_n1_n2_k),
decltype(b_thread_desc_),
Sequence<1, 1, 1, KPerInnerLoop>,

60
include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v1_ab_scale.hpp
View File

@@ -144,6 +144,8 @@ struct BlockwiseGemmXdlops_pipeline_v1_ab_scale<BlockGemmPipelineScheduler::Intr
using Base::a_block_desc_m0_m1_m2_k;
using Base::b_block_desc_n0_n1_n2_k;
using ComputeDataTypeBuf = typename Base::ComputeDataTypeBuf;
static constexpr index_t AMmaKStride = xdlops_gemm.K0PerXdlops * KPack;
static constexpr index_t BMmaKStride = xdlops_gemm.K0PerXdlops * KPack;
@@ -222,10 +224,12 @@ struct BlockwiseGemmXdlops_pipeline_v1_ab_scale<BlockGemmPipelineScheduler::Intr
// stage 1
// Separate this part?
// constexpr auto num_mfma_per_ds_read = sizeof(ComputeDataType) / sizeof(ADataType) >
// sizeof(ComputeDataType) / sizeof(BDataType)
// ? sizeof(ComputeDataType) / sizeof(ADataType)
// : sizeof(ComputeDataType) / sizeof(BDataType);
// constexpr auto num_mfma_per_ds_read = sizeof(ComputeDataTypeBuf) / sizeof(ADataType) >
// sizeof(ComputeDataTypeBuf) /
// sizeof(BDataType)
// ? sizeof(ComputeDataTypeBuf) /
// sizeof(ADataType) : sizeof(ComputeDataTypeBuf)
// / sizeof(BDataType);
constexpr auto num_mfma_stage1 = num_mfma_inst - (num_dsread_a_mfma + num_dsread_b_mfma);
constexpr auto num_mfma_per_issue =
num_mfma_stage1 / (num_buffer_load_inst_a + num_buffer_load_inst_b);
@@ -351,9 +355,9 @@ struct BlockwiseGemmXdlops_pipeline_v1_ab_scale<BlockGemmPipelineScheduler::Intr
{
__builtin_amdgcn_sched_barrier(0);
// assume kperblock = scaleblockk
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
a_thread_desc_.GetElementSpaceSize());
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
b_thread_desc_.GetElementSpaceSize());
auto a_scale_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, AccDataType>(
a_scale_thread_desc.GetElementSpaceSize());
@@ -516,17 +520,17 @@ struct BlockwiseGemmXdlops_pipeline_v1_ab_scale<BlockGemmPipelineScheduler::Intr
.template AsType<AccDataType>()(Number<t>{}) = 0;
});
static_for<0, KRepeat / num_scale_k_block, 1>{}([&](auto k0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0,
I0,
kscale0 * KRepeat / num_scale_k_block + k0,
ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0,
I0,
@@ -535,7 +539,7 @@ struct BlockwiseGemmXdlops_pipeline_v1_ab_scale<BlockGemmPipelineScheduler::Intr
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
xdlops_gemm.template Run<>(
@@ -646,17 +650,17 @@ struct BlockwiseGemmXdlops_pipeline_v1_ab_scale<BlockGemmPipelineScheduler::Intr
.template AsType<AccDataType>()(Number<t>{}) = 0;
});
static_for<0, KRepeat / num_scale_k_block, 1>{}([&](auto k0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0,
I0,
kscale0 * KRepeat / num_scale_k_block + k0,
ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0,
I0,
@@ -665,7 +669,7 @@ struct BlockwiseGemmXdlops_pipeline_v1_ab_scale<BlockGemmPipelineScheduler::Intr
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
xdlops_gemm.template Run<>(
@@ -737,17 +741,17 @@ struct BlockwiseGemmXdlops_pipeline_v1_ab_scale<BlockGemmPipelineScheduler::Intr
.template AsType<AccDataType>()(Number<t>{}) = 0;
});
static_for<0, KRepeat / num_scale_k_block, 1>{}([&](auto k0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0,
I0,
kscale0 * KRepeat / num_scale_k_block + k0,
ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0,
I0,
@@ -756,7 +760,7 @@ struct BlockwiseGemmXdlops_pipeline_v1_ab_scale<BlockGemmPipelineScheduler::Intr
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
xdlops_gemm.template Run<>(
@@ -791,17 +795,17 @@ struct BlockwiseGemmXdlops_pipeline_v1_ab_scale<BlockGemmPipelineScheduler::Intr
.template AsType<AccDataType>()(Number<t>{}) = 0;
});
static_for<0, KRepeat / num_scale_k_block, 1>{}([&](auto k0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0,
I0,
kscale0 * KRepeat / num_scale_k_block + k0,
ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0,
I0,
@@ -810,7 +814,7 @@ struct BlockwiseGemmXdlops_pipeline_v1_ab_scale<BlockGemmPipelineScheduler::Intr
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
xdlops_gemm.template Run<>(
@@ -842,7 +846,7 @@ struct BlockwiseGemmXdlops_pipeline_v1_ab_scale<BlockGemmPipelineScheduler::Intr
using Base::b_thread_desc_;
using Base::c_thread_desc_;
using AThreadCopy = ThreadwiseTensorSliceTransfer_v4<ADataType,
ComputeDataType,
ComputeDataTypeBuf,
decltype(a_block_desc_m0_m1_m2_k),
decltype(a_thread_desc_),
Sequence<1, 1, 1, KPack>,
@@ -852,7 +856,7 @@ struct BlockwiseGemmXdlops_pipeline_v1_ab_scale<BlockGemmPipelineScheduler::Intr
A_K1>;
using BThreadCopy = ThreadwiseTensorSliceTransfer_v4<BDataType,
ComputeDataType,
ComputeDataTypeBuf,
decltype(b_block_desc_n0_n1_n2_k),
decltype(b_thread_desc_),
Sequence<1, 1, 1, KPack>,

28
include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v1_b_scale.hpp
View File

@@ -140,6 +140,8 @@ struct BlockwiseGemmXdlops_pipeline_v1_b_scale<BlockGemmPipelineScheduler::Intra
using Base::AMmaKStride;
using Base::BMmaKStride;
using ComputeDataTypeBuf = typename Base::ComputeDataTypeBuf;
static constexpr index_t PrefetchStages = 1;
static constexpr index_t PrefillStages = 1;
static constexpr index_t GlobalBufferNum = 1;
@@ -205,12 +207,12 @@ struct BlockwiseGemmXdlops_pipeline_v1_b_scale<BlockGemmPipelineScheduler::Intra
{
// assume kperblock = scaleblockk
ignore = num_loop_per_scale;
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
a_thread_desc_.GetElementSpaceSize());
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
b_thread_desc_.GetElementSpaceSize());
auto b_scale_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto b_scale_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
b_scale_thread_desc.GetElementSpaceSize());
// Global prefetch 1
@@ -279,20 +281,20 @@ struct BlockwiseGemmXdlops_pipeline_v1_b_scale<BlockGemmPipelineScheduler::Intra
static_for<0, NRepeat, 1>{}([&](auto n0) {
c_thread_buf_per_scale.Clear();
static_for<0, KRepeat, 1>{}([&](auto k0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
xdlops_gemm.template Run<>(
@@ -360,20 +362,20 @@ struct BlockwiseGemmXdlops_pipeline_v1_b_scale<BlockGemmPipelineScheduler::Intra
static_for<0, NRepeat, 1>{}([&](auto n0) {
c_thread_buf_per_scale.Clear();
static_for<0, KRepeat, 1>{}([&](auto k0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
typename vector_type<ComputeDataTypeBuf, xdlops_gemm.K1PerXdlops>::type;
xdlops_gemm.template Run<>(
a_thread_vec.template AsType<mfma_input_type>(),

96
include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v2.hpp
View File

@@ -141,6 +141,8 @@ struct BlockwiseGemmXdlops_pipeline_v2<BlockGemmPipelineScheduler::Intrawave,
using Base::BMmaKStride;
using Base::WaveSize;
using ComputeDataTypeBuf = typename Base::ComputeDataTypeBuf;
static constexpr index_t WgpPerCU =
(4 * WaveSize / BlockSize) >= 1 ? 4 * WaveSize / BlockSize : 1;
static constexpr index_t FullMemBandPrefetchStages = math::integer_divide_ceil(
@@ -225,9 +227,9 @@ struct BlockwiseGemmXdlops_pipeline_v2<BlockGemmPipelineScheduler::Intrawave,
CThreadBuffer& c_thread_buf,
index_t num_loop) const
{
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
a_thread_desc_.GetElementSpaceSize());
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
b_thread_desc_.GetElementSpaceSize());
// Global prefetch 1
@@ -284,20 +286,20 @@ struct BlockwiseGemmXdlops_pipeline_v2<BlockGemmPipelineScheduler::Intrawave,
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
@@ -355,20 +357,20 @@ struct BlockwiseGemmXdlops_pipeline_v2<BlockGemmPipelineScheduler::Intrawave,
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
@@ -410,20 +412,20 @@ struct BlockwiseGemmXdlops_pipeline_v2<BlockGemmPipelineScheduler::Intrawave,
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
typename vector_type<ComputeDataTypeBuf, xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
@@ -461,20 +463,20 @@ struct BlockwiseGemmXdlops_pipeline_v2<BlockGemmPipelineScheduler::Intrawave,
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
typename vector_type<ComputeDataTypeBuf, xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
@@ -628,6 +630,8 @@ struct BlockwiseGemmXdlops_pipeline_v2<BlockGemmPipelineScheduler::Interwave,
using Base::b_block_desc_n0_n1_n2_k;
using Base::WaveSize;
using ComputeDataTypeBuf = typename Base::ComputeDataTypeBuf;
static constexpr index_t NumMacClusters = CK_EXPERIMENTAL_INTER_WAVE_SCHEDULING_MAC_CLUSTERS;
static constexpr index_t KPerInnerLoop = math::max(KPerThread / NumMacClusters, KPack);
static constexpr index_t KRepeat = KPerThread / KPerInnerLoop;
@@ -716,9 +720,9 @@ struct BlockwiseGemmXdlops_pipeline_v2<BlockGemmPipelineScheduler::Interwave,
CThreadBuffer& c_thread_buf,
index_t num_loop) const
{
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
a_thread_desc_.GetElementSpaceSize());
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
b_thread_desc_.GetElementSpaceSize());
// Global prefetch 1
@@ -786,20 +790,20 @@ struct BlockwiseGemmXdlops_pipeline_v2<BlockGemmPipelineScheduler::Interwave,
static_for<0, KPerInnerLoop, KPack>{}([&](auto k_) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, k_ + ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, k_ + ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
@@ -885,20 +889,20 @@ struct BlockwiseGemmXdlops_pipeline_v2<BlockGemmPipelineScheduler::Interwave,
static_for<0, KPerInnerLoop, KPack>{}([&](auto k_) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, k_ + ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, k_ + ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
@@ -961,20 +965,20 @@ struct BlockwiseGemmXdlops_pipeline_v2<BlockGemmPipelineScheduler::Interwave,
static_for<0, KPerInnerLoop, KPack>{}([&](auto k_) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, k_ + ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, k_ + ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
@@ -1037,20 +1041,20 @@ struct BlockwiseGemmXdlops_pipeline_v2<BlockGemmPipelineScheduler::Interwave,
static_for<0, KPerInnerLoop, KPack>{}([&](auto k_) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, k_ + ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, k_ + ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
@@ -1129,7 +1133,7 @@ struct BlockwiseGemmXdlops_pipeline_v2<BlockGemmPipelineScheduler::Interwave,
I1));
using AThreadCopy = ThreadwiseTensorSliceTransfer_v4<ADataType,
ComputeDataType,
ComputeDataTypeBuf,
decltype(a_block_desc_m0_m1_m2_k),
decltype(a_thread_desc_),
Sequence<1, 1, 1, KPerInnerLoop>,
@@ -1139,7 +1143,7 @@ struct BlockwiseGemmXdlops_pipeline_v2<BlockGemmPipelineScheduler::Interwave,
A_K1>;
using BThreadCopy = ThreadwiseTensorSliceTransfer_v4<BDataType,
ComputeDataType,
ComputeDataTypeBuf,
decltype(b_block_desc_n0_n1_n2_k),
decltype(b_thread_desc_),
Sequence<1, 1, 1, KPerInnerLoop>,

46
include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v2_ab_scale.hpp
View File

@@ -143,6 +143,8 @@ struct BlockwiseGemmXdlops_pipeline_v2_ab_scale<BlockGemmPipelineScheduler::Intr
using Base::BMmaKStride;
using Base::WaveSize;
using ComputeDataTypeBuf = typename Base::ComputeDataTypeBuf;
static constexpr index_t WgpPerCU =
(4 * WaveSize / BlockSize) >= 1 ? 4 * WaveSize / BlockSize : 1;
static constexpr index_t FullMemBandPrefetchStages = math::integer_divide_ceil(
@@ -257,9 +259,9 @@ struct BlockwiseGemmXdlops_pipeline_v2_ab_scale<BlockGemmPipelineScheduler::Intr
{
// assume kperblock = scaleblockk
ignore = num_loop_per_scale;
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
a_thread_desc_.GetElementSpaceSize());
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
b_thread_desc_.GetElementSpaceSize());
auto a_scale_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, AccDataType>(
a_scale_thread_desc.GetElementSpaceSize());
@@ -351,20 +353,20 @@ struct BlockwiseGemmXdlops_pipeline_v2_ab_scale<BlockGemmPipelineScheduler::Intr
static_for<0, NRepeat, 1>{}([&](auto n0) {
c_thread_buf_per_scale.Clear();
static_for<0, KRepeat, 1>{}([&](auto k0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
xdlops_gemm.template Run<>(
@@ -457,20 +459,20 @@ struct BlockwiseGemmXdlops_pipeline_v2_ab_scale<BlockGemmPipelineScheduler::Intr
static_for<0, NRepeat, 1>{}([&](auto n0) {
c_thread_buf_per_scale.Clear();
static_for<0, KRepeat, 1>{}([&](auto k0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
xdlops_gemm.template Run<>(
@@ -547,20 +549,20 @@ struct BlockwiseGemmXdlops_pipeline_v2_ab_scale<BlockGemmPipelineScheduler::Intr
static_for<0, NRepeat, 1>{}([&](auto n0) {
c_thread_buf_per_scale.Clear();
static_for<0, KRepeat, 1>{}([&](auto k0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
typename vector_type<ComputeDataTypeBuf, xdlops_gemm.K1PerXdlops>::type;
xdlops_gemm.template Run<>(
a_thread_vec.template AsType<mfma_input_type>(),
@@ -605,20 +607,20 @@ struct BlockwiseGemmXdlops_pipeline_v2_ab_scale<BlockGemmPipelineScheduler::Intr
static_for<0, NRepeat, 1>{}([&](auto n0) {
c_thread_buf_per_scale.Clear();
static_for<0, KRepeat, 1>{}([&](auto k0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
typename vector_type<ComputeDataTypeBuf, xdlops_gemm.K1PerXdlops>::type;
xdlops_gemm.template Run<>(
a_thread_vec.template AsType<mfma_input_type>(),

98
include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v2_b_scale.hpp
View File

@@ -141,6 +141,8 @@ struct BlockwiseGemmXdlops_pipeline_v2_b_scale<BlockGemmPipelineScheduler::Intra
using Base::BMmaKStride;
using Base::WaveSize;
using ComputeDataTypeBuf = typename Base::ComputeDataTypeBuf;
static constexpr index_t WgpPerCU =
(4 * WaveSize / BlockSize) >= 1 ? 4 * WaveSize / BlockSize : 1;
static constexpr index_t FullMemBandPrefetchStages = math::integer_divide_ceil(
@@ -225,9 +227,9 @@ struct BlockwiseGemmXdlops_pipeline_v2_b_scale<BlockGemmPipelineScheduler::Intra
CThreadBuffer& c_thread_buf,
index_t num_loop) const
{
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
a_thread_desc_.GetElementSpaceSize());
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
b_thread_desc_.GetElementSpaceSize());
// Global prefetch 1
@@ -285,20 +287,20 @@ struct BlockwiseGemmXdlops_pipeline_v2_b_scale<BlockGemmPipelineScheduler::Intra
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
@@ -356,20 +358,20 @@ struct BlockwiseGemmXdlops_pipeline_v2_b_scale<BlockGemmPipelineScheduler::Intra
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
@@ -411,20 +413,20 @@ struct BlockwiseGemmXdlops_pipeline_v2_b_scale<BlockGemmPipelineScheduler::Intra
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
typename vector_type<ComputeDataTypeBuf, xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
@@ -462,20 +464,20 @@ struct BlockwiseGemmXdlops_pipeline_v2_b_scale<BlockGemmPipelineScheduler::Intra
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
typename vector_type<ComputeDataTypeBuf, xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
@@ -629,6 +631,8 @@ struct BlockwiseGemmXdlops_pipeline_v2_b_scale<BlockGemmPipelineScheduler::Inter
using Base::b_block_desc_n0_n1_n2_k;
using Base::WaveSize;
using ComputeDataTypeBuf = typename Base::ComputeDataTypeBuf;
static constexpr index_t NumMacClusters = CK_EXPERIMENTAL_INTER_WAVE_SCHEDULING_MAC_CLUSTERS;
static constexpr index_t KPerInnerLoop = math::max(KPerThread / NumMacClusters, KPack);
static constexpr index_t KRepeat = KPerThread / KPerInnerLoop;
@@ -732,12 +736,12 @@ struct BlockwiseGemmXdlops_pipeline_v2_b_scale<BlockGemmPipelineScheduler::Inter
{
ignore = num_loop_per_scale;
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
a_thread_desc_.GetElementSpaceSize());
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
b_thread_desc_.GetElementSpaceSize());
auto b_scale_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto b_scale_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
b_scale_thread_desc.GetElementSpaceSize());
// Global prefetch 1
@@ -821,20 +825,20 @@ struct BlockwiseGemmXdlops_pipeline_v2_b_scale<BlockGemmPipelineScheduler::Inter
static_for<0, KPerInnerLoop, KPack>{}([&](auto k_) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, k_ + ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, k_ + ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
@@ -942,20 +946,20 @@ struct BlockwiseGemmXdlops_pipeline_v2_b_scale<BlockGemmPipelineScheduler::Inter
static_for<0, KPerInnerLoop, KPack>{}([&](auto k_) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, k_ + ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, k_ + ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
@@ -1039,20 +1043,20 @@ struct BlockwiseGemmXdlops_pipeline_v2_b_scale<BlockGemmPipelineScheduler::Inter
static_for<0, KPerInnerLoop, KPack>{}([&](auto k_) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, k_ + ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, k_ + ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
@@ -1123,20 +1127,20 @@ struct BlockwiseGemmXdlops_pipeline_v2_b_scale<BlockGemmPipelineScheduler::Inter
static_for<0, KPerInnerLoop, KPack>{}([&](auto k_) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, k_ + ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, k_ + ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
@@ -1223,7 +1227,7 @@ struct BlockwiseGemmXdlops_pipeline_v2_b_scale<BlockGemmPipelineScheduler::Inter
I1));
using AThreadCopy = ThreadwiseTensorSliceTransfer_v4<ADataType,
ComputeDataType,
ComputeDataTypeBuf,
decltype(a_block_desc_m0_m1_m2_k),
decltype(a_thread_desc_),
Sequence<1, 1, 1, KPerInnerLoop>,
@@ -1233,7 +1237,7 @@ struct BlockwiseGemmXdlops_pipeline_v2_b_scale<BlockGemmPipelineScheduler::Inter
A_K1>;
using BThreadCopy = ThreadwiseTensorSliceTransfer_v4<BDataType,
ComputeDataType,
ComputeDataTypeBuf,
decltype(b_block_desc_n0_n1_n2_k),
decltype(b_thread_desc_),
Sequence<1, 1, 1, KPerInnerLoop>,

34
include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v3.hpp
View File

@@ -142,6 +142,8 @@ struct BlockwiseGemmXdlops_pipeline_v3<BlockGemmPipelineScheduler::Intrawave,
using Base::AMmaKStride;
using Base::BMmaKStride;
using ComputeDataTypeBuf = typename Base::ComputeDataTypeBuf;
static constexpr index_t PrefetchStages = 2;
static constexpr index_t PrefillStages = 1;
static constexpr index_t GlobalBufferNum = 1;
@@ -196,10 +198,10 @@ struct BlockwiseGemmXdlops_pipeline_v3<BlockGemmPipelineScheduler::Intrawave,
// stage 1
// Separate this part?
// constexpr auto num_mfma_per_ds_read = sizeof(ComputeDataType) / sizeof(ADataType) >
// sizeof(ComputeDataType) / sizeof(BDataType)
// ? sizeof(ComputeDataType) / sizeof(ADataType)
// : sizeof(ComputeDataType) / sizeof(BDataType);
// constexpr auto num_mfma_per_ds_read = sizeof(ComputeDataTypeBuf) / sizeof(ADataType) >
// sizeof(ComputeDataTypeBuf) / sizeof(BDataType)
// ? sizeof(ComputeDataTypeBuf) / sizeof(ADataType)
// : sizeof(ComputeDataTypeBuf) / sizeof(BDataType);
constexpr auto num_mfma_stage1 = num_mfma_inst - (num_dsread_a_mfma + num_dsread_b_mfma);
constexpr auto num_mfma_per_issue =
num_mfma_stage1 / (num_buffer_load_inst_a + num_buffer_load_inst_b);
@@ -295,9 +297,9 @@ struct BlockwiseGemmXdlops_pipeline_v3<BlockGemmPipelineScheduler::Intrawave,
index_t num_loop) const
{
__builtin_amdgcn_sched_barrier(0);
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
a_thread_desc_.GetElementSpaceSize());
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
b_thread_desc_.GetElementSpaceSize());
// Global prefetch 1
@@ -364,20 +366,20 @@ struct BlockwiseGemmXdlops_pipeline_v3<BlockGemmPipelineScheduler::Intrawave,
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
@@ -424,20 +426,20 @@ struct BlockwiseGemmXdlops_pipeline_v3<BlockGemmPipelineScheduler::Intrawave,
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
typename vector_type<ComputeDataTypeBuf, xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));

34
include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v3_ab_scale.hpp
View File

@@ -143,6 +143,8 @@ struct BlockwiseGemmXdlops_pipeline_v3_ab_scale<BlockGemmPipelineScheduler::Intr
using Base::AMmaKStride;
using Base::BMmaKStride;
using ComputeDataTypeBuf = typename Base::ComputeDataTypeBuf;
static constexpr index_t PrefetchStages = 2;
static constexpr index_t PrefillStages = 1;
static constexpr index_t GlobalBufferNum = 1;
@@ -196,10 +198,10 @@ struct BlockwiseGemmXdlops_pipeline_v3_ab_scale<BlockGemmPipelineScheduler::Intr
// stage 1
// Separate this part?
// constexpr auto num_mfma_per_ds_read = sizeof(ComputeDataType) / sizeof(ADataType) >
// sizeof(ComputeDataType) / sizeof(BDataType)
// ? sizeof(ComputeDataType) / sizeof(ADataType)
// : sizeof(ComputeDataType) / sizeof(BDataType);
// constexpr auto num_mfma_per_ds_read = sizeof(ComputeDataTypeBuf) / sizeof(ADataType) >
// sizeof(ComputeDataTypeBuf) / sizeof(BDataType)
// ? sizeof(ComputeDataTypeBuf) / sizeof(ADataType)
// : sizeof(ComputeDataTypeBuf) / sizeof(BDataType);
constexpr auto num_mfma_stage1 = num_mfma_inst - (num_dsread_a_mfma + num_dsread_b_mfma);
constexpr auto num_mfma_per_issue =
num_mfma_stage1 / (num_buffer_load_inst_a + num_buffer_load_inst_b);
@@ -329,9 +331,9 @@ struct BlockwiseGemmXdlops_pipeline_v3_ab_scale<BlockGemmPipelineScheduler::Intr
static_assert(CScaleThreadDesc{}.GetLength(Number<2>{}) == 1,
"Pipeline v3 only support scaleblocksliceN=1");
// assume kperblock = scaleblockk
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
a_thread_desc_.GetElementSpaceSize());
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
b_thread_desc_.GetElementSpaceSize());
auto a_scale_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, AccDataType>(
a_scale_thread_desc.GetElementSpaceSize());
@@ -476,20 +478,20 @@ struct BlockwiseGemmXdlops_pipeline_v3_ab_scale<BlockGemmPipelineScheduler::Intr
.template AsType<AccDataType>()(Number<t>{}) = 0;
});
static_for<0, KRepeat, 1>{}([&](auto k0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
xdlops_gemm.template Run<>(
@@ -578,20 +580,20 @@ struct BlockwiseGemmXdlops_pipeline_v3_ab_scale<BlockGemmPipelineScheduler::Intr
.template AsType<AccDataType>()(Number<t>{}) = 0;
});
static_for<0, KRepeat, 1>{}([&](auto k0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
typename vector_type<ComputeDataTypeBuf, xdlops_gemm.K1PerXdlops>::type;
xdlops_gemm.template Run<>(
a_thread_vec.template AsType<mfma_input_type>(),

36
include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v3_b_scale.hpp
View File

@@ -142,6 +142,8 @@ struct BlockwiseGemmXdlops_pipeline_v3_b_scale<BlockGemmPipelineScheduler::Intra
using Base::AMmaKStride;
using Base::BMmaKStride;
using ComputeDataTypeBuf = typename Base::ComputeDataTypeBuf;
static constexpr index_t PrefetchStages = 2;
static constexpr index_t PrefillStages = 1;
static constexpr index_t GlobalBufferNum = 1;
@@ -195,10 +197,10 @@ struct BlockwiseGemmXdlops_pipeline_v3_b_scale<BlockGemmPipelineScheduler::Intra
// stage 1
// Separate this part?
// constexpr auto num_mfma_per_ds_read = sizeof(ComputeDataType) / sizeof(ADataType) >
// sizeof(ComputeDataType) / sizeof(BDataType)
// ? sizeof(ComputeDataType) / sizeof(ADataType)
// : sizeof(ComputeDataType) / sizeof(BDataType);
// constexpr auto num_mfma_per_ds_read = sizeof(ComputeDataTypeBuf) / sizeof(ADataType) >
// sizeof(ComputeDataTypeBuf) / sizeof(BDataType)
// ? sizeof(ComputeDataTypeBuf) / sizeof(ADataType)
// : sizeof(ComputeDataTypeBuf) / sizeof(BDataType);
constexpr auto num_mfma_stage1 = num_mfma_inst - (num_dsread_a_mfma + num_dsread_b_mfma);
constexpr auto num_mfma_per_issue =
num_mfma_stage1 / (num_buffer_load_inst_a + num_buffer_load_inst_b);
@@ -307,13 +309,13 @@ struct BlockwiseGemmXdlops_pipeline_v3_b_scale<BlockGemmPipelineScheduler::Intra
{
__builtin_amdgcn_sched_barrier(0);
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
a_thread_desc_.GetElementSpaceSize());
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
b_thread_desc_.GetElementSpaceSize());
// B scale buffer
auto b_scale_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto b_scale_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
b_scale_thread_desc.GetElementSpaceSize());
// Global prefetch 1
@@ -429,20 +431,20 @@ struct BlockwiseGemmXdlops_pipeline_v3_b_scale<BlockGemmPipelineScheduler::Intra
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
@@ -491,20 +493,20 @@ struct BlockwiseGemmXdlops_pipeline_v3_b_scale<BlockGemmPipelineScheduler::Intra
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
typename vector_type<ComputeDataTypeBuf, xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));

46
include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v4.hpp
View File

@@ -142,6 +142,8 @@ struct BlockwiseGemmXdlops_pipeline_v4<BlockGemmPipelineScheduler::Intrawave,
using Base::AMmaKStride;
using Base::BMmaKStride;
using ComputeDataTypeBuf = typename Base::ComputeDataTypeBuf;
static constexpr index_t PrefetchStages = 3;
static constexpr index_t PrefillStages = 2;
static constexpr index_t GlobalBufferNum = 1;
@@ -264,9 +266,9 @@ struct BlockwiseGemmXdlops_pipeline_v4<BlockGemmPipelineScheduler::Intrawave,
CThreadBuffer& c_thread_buf,
index_t num_loop) const
{
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
a_thread_desc_.GetElementSpaceSize());
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
b_thread_desc_.GetElementSpaceSize());
StaticallyIndexedArray<decltype(a_thread_buf), Number<2>{}> a_thread_bufs;
@@ -369,22 +371,22 @@ struct BlockwiseGemmXdlops_pipeline_v4<BlockGemmPipelineScheduler::Intrawave,
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_bufs[mfma_reg_buf]
[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_bufs[mfma_reg_buf]
[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
@@ -439,20 +441,20 @@ struct BlockwiseGemmXdlops_pipeline_v4<BlockGemmPipelineScheduler::Intrawave,
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_bufs[mfma_reg_buf][Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_bufs[mfma_reg_buf][Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
typename vector_type<ComputeDataTypeBuf, xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
@@ -492,20 +494,20 @@ struct BlockwiseGemmXdlops_pipeline_v4<BlockGemmPipelineScheduler::Intrawave,
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_bufs[mfma_reg_buf][Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_bufs[mfma_reg_buf][Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
typename vector_type<ComputeDataTypeBuf, xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
@@ -524,20 +526,20 @@ struct BlockwiseGemmXdlops_pipeline_v4<BlockGemmPipelineScheduler::Intrawave,
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_bufs[mfma_reg_buf][Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_bufs[mfma_reg_buf][Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
typename vector_type<ComputeDataTypeBuf, xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));

48
include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v4_b_scale.hpp
View File

@@ -142,6 +142,8 @@ struct BlockwiseGemmXdlops_pipeline_v4_b_scale<BlockGemmPipelineScheduler::Intra
using Base::AMmaKStride;
using Base::BMmaKStride;
using ComputeDataTypeBuf = typename Base::ComputeDataTypeBuf;
static constexpr index_t PrefetchStages = 3;
static constexpr index_t PrefillStages = 2;
static constexpr index_t GlobalBufferNum = 1;
@@ -277,13 +279,13 @@ struct BlockwiseGemmXdlops_pipeline_v4_b_scale<BlockGemmPipelineScheduler::Intra
index_t num_loop,
index_t num_loop_per_scale) const
{
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
a_thread_desc_.GetElementSpaceSize());
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
b_thread_desc_.GetElementSpaceSize());
// B scale buffer
auto b_scale_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto b_scale_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
b_scale_thread_desc.GetElementSpaceSize());
StaticallyIndexedArray<decltype(a_thread_buf), Number<2>{}> a_thread_bufs;
@@ -478,22 +480,22 @@ struct BlockwiseGemmXdlops_pipeline_v4_b_scale<BlockGemmPipelineScheduler::Intra
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_bufs[mfma_reg_buf]
[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_bufs[mfma_reg_buf]
[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
@@ -549,20 +551,20 @@ struct BlockwiseGemmXdlops_pipeline_v4_b_scale<BlockGemmPipelineScheduler::Intra
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_bufs[mfma_reg_buf][Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_bufs[mfma_reg_buf][Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
typename vector_type<ComputeDataTypeBuf, xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
@@ -603,20 +605,20 @@ struct BlockwiseGemmXdlops_pipeline_v4_b_scale<BlockGemmPipelineScheduler::Intra
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_bufs[mfma_reg_buf][Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_bufs[mfma_reg_buf][Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
typename vector_type<ComputeDataTypeBuf, xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
@@ -635,20 +637,20 @@ struct BlockwiseGemmXdlops_pipeline_v4_b_scale<BlockGemmPipelineScheduler::Intra
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_bufs[mfma_reg_buf][Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, k0, ik))>{}];
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_bufs[mfma_reg_buf][Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, k0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
typename vector_type<ComputeDataTypeBuf, xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));

46
include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v5.hpp
View File

@@ -144,6 +144,8 @@ struct BlockwiseGemmXdlops_pipeline_v5<BlockGemmPipelineScheduler::Intrawave,
using Base::AMmaKStride;
using Base::BMmaKStride;
using ComputeDataTypeBuf = typename Base::ComputeDataTypeBuf;
static constexpr index_t PrefetchStages = 3;
static constexpr index_t PrefillStages = 1;
static constexpr index_t GlobalBufferNum = 2;
@@ -346,9 +348,9 @@ struct BlockwiseGemmXdlops_pipeline_v5<BlockGemmPipelineScheduler::Intrawave,
CThreadBuffer& c_thread_buf,
index_t num_loop) const
{
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
a_thread_desc_.GetElementSpaceSize());
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataTypeBuf>(
b_thread_desc_.GetElementSpaceSize());
// Global prefetch 1
@@ -405,8 +407,8 @@ struct BlockwiseGemmXdlops_pipeline_v5<BlockGemmPipelineScheduler::Intrawave,
do
{
auto LoopFunc = [&](auto vmem_buf) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KRepeat, 1>{}([&](auto k0) {
if constexpr(k0 == (KRepeat - 1))
@@ -427,18 +429,18 @@ struct BlockwiseGemmXdlops_pipeline_v5<BlockGemmPipelineScheduler::Intrawave,
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, I0, ik))>{}];
});
static_for<0, KPack, 1>{}([&](auto ik) {
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, I0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType,
typename vector_type<ComputeDataTypeBuf,
xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
@@ -481,8 +483,8 @@ struct BlockwiseGemmXdlops_pipeline_v5<BlockGemmPipelineScheduler::Intrawave,
}
// tail
auto ReadWriteCompFunc = [&](auto vmem_buf) {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KRepeat, 1>{}([&](auto k0) {
if constexpr(k0 == (KRepeat - 1))
@@ -497,18 +499,18 @@ struct BlockwiseGemmXdlops_pipeline_v5<BlockGemmPipelineScheduler::Intrawave,
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, I0, ik))>{}];
});
static_for<0, KPack, 1>{}([&](auto ik) {
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, I0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
typename vector_type<ComputeDataTypeBuf, xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
@@ -540,25 +542,25 @@ struct BlockwiseGemmXdlops_pipeline_v5<BlockGemmPipelineScheduler::Intrawave,
HotLoopScheduler();
};
auto ReadCompFunc = [&]() {
vector_type<ComputeDataType, KPack> a_thread_vec;
vector_type<ComputeDataType, KPack> b_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> a_thread_vec;
vector_type<ComputeDataTypeBuf, KPack> b_thread_vec;
static_for<0, KRepeat - 1, 1>{}([&](auto k0) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) =
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(m0, I0, I0, ik))>{}];
});
static_for<0, KPack, 1>{}([&](auto ik) {
b_thread_vec.template AsType<ComputeDataType>()(ik) =
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(n0, I0, I0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
typename vector_type<ComputeDataTypeBuf, xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
@@ -591,16 +593,16 @@ struct BlockwiseGemmXdlops_pipeline_v5<BlockGemmPipelineScheduler::Intrawave,
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
static_for<0, KPack, 1>{}([&](auto ik) {
a_thread_vec.template AsType<ComputeDataType>()(ik) = a_thread_buf
a_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) = a_thread_buf
[Number<a_thread_desc_.CalculateOffset(make_tuple(m0, I0, I0, ik))>{}];
});
static_for<0, KPack, 1>{}([&](auto ik) {
b_thread_vec.template AsType<ComputeDataType>()(ik) = b_thread_buf
b_thread_vec.template AsType<ComputeDataTypeBuf>()(ik) = b_thread_buf
[Number<b_thread_desc_.CalculateOffset(make_tuple(n0, I0, I0, ik))>{}];
});
using mfma_input_type =
typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
typename vector_type<ComputeDataTypeBuf, xdlops_gemm.K1PerXdlops>::type;
constexpr index_t c_offset =
c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
@@ -637,7 +639,7 @@ struct BlockwiseGemmXdlops_pipeline_v5<BlockGemmPipelineScheduler::Intrawave,
make_naive_tensor_descriptor_packed(make_tuple(Number<NRepeat>{}, I1, I1, Number<KPack>{}));
using AThreadCopy = ThreadwiseTensorSliceTransfer_v4<ADataType,
ComputeDataType,
ComputeDataTypeBuf,
decltype(a_block_desc_m0_m1_m2_k),
decltype(a_thread_desc_),
Sequence<1, 1, 1, KPack>,
@@ -647,7 +649,7 @@ struct BlockwiseGemmXdlops_pipeline_v5<BlockGemmPipelineScheduler::Intrawave,
A_K1>;
using BThreadCopy = ThreadwiseTensorSliceTransfer_v4<BDataType,
ComputeDataType,
ComputeDataTypeBuf,
decltype(b_block_desc_n0_n1_n2_k),
decltype(b_thread_desc_),
Sequence<1, 1, 1, KPack>,

40
include/ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_abd_xdl_cshuffle.hpp
View File

@@ -107,8 +107,11 @@ struct GridwiseGemmMultipleABD_xdl_cshuffle
using BComputeDataType =
conditional_t<is_same_v<BComputeDataType_, ck::half_t>, ck::bhalf_t, BComputeDataType_>;
#else
using AComputeDataType = AComputeDataType_;
using BComputeDataType = BComputeDataType_;
// Element data type is used in LDS and registers. ComputeDataType_ is inside mfma, eg tf32.
using AElementDataType =
conditional_t<is_same_v<AComputeDataType_, ck::tf32_t>, float, AComputeDataType_>;
using BElementDataType =
conditional_t<is_same_v<BComputeDataType_, ck::tf32_t>, float, BComputeDataType_>;
#endif
__host__ __device__ static constexpr auto GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1()
@@ -199,8 +202,8 @@ struct GridwiseGemmMultipleABD_xdl_cshuffle
constexpr auto c_block_size =
c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize();
return math::max(a_block_space_size_aligned * sizeof(AComputeDataType) +
b_block_space_size_aligned * sizeof(BComputeDataType),
return math::max(a_block_space_size_aligned * sizeof(AElementDataType) +
b_block_space_size_aligned * sizeof(BElementDataType),
c_block_size * sizeof(CShuffleDataType));
}
@@ -621,7 +624,7 @@ struct GridwiseGemmMultipleABD_xdl_cshuffle
auto a_blockwise_copy = ThreadGroupTensorSliceTransfer_v7r2<
ThisThreadBlock,
AsDataType,
Tuple<AComputeDataType>,
Tuple<AElementDataType>,
decltype(as_grid_desc_ak0_m_ak1),
decltype(tie(a_block_desc_ak0_m_ak1)),
AElementwiseOperation,
@@ -649,7 +652,7 @@ struct GridwiseGemmMultipleABD_xdl_cshuffle
auto b_blockwise_copy = ThreadGroupTensorSliceTransfer_v7r2<
ThisThreadBlock,
BsDataType,
Tuple<BComputeDataType>,
Tuple<BElementDataType>,
decltype(bs_grid_desc_bk0_n_bk1),
decltype(tie(b_block_desc_bk0_n_bk1)),
BElementwiseOperation,
@@ -679,27 +682,28 @@ struct GridwiseGemmMultipleABD_xdl_cshuffle
// sanity check
constexpr auto lcm_AK1_BK1 = math::lcm(AK1, BK1);
constexpr bool is_single_rate_mfma =
(((is_same<AComputeDataType, half_t>::value ||
is_same<AComputeDataType, bhalf_t>::value) &&
(((is_same<AComputeDataType_, half_t>::value ||
is_same<AComputeDataType_, bhalf_t>::value) &&
lcm_AK1_BK1 <= 4) ||
(is_same<AComputeDataType, int8_t>::value && lcm_AK1_BK1 <= 8) ||
((is_same<AComputeDataType, f8_t>::value || is_same<AComputeDataType, bf8_t>::value) &&
(is_same<AComputeDataType_, int8_t>::value && lcm_AK1_BK1 <= 8) ||
((is_same<AComputeDataType_, f8_t>::value ||
is_same<AComputeDataType_, bf8_t>::value) &&
lcm_AK1_BK1 < 32))
? true
: false;
static constexpr auto is_scale_mfma = false;
constexpr index_t KPack = math::max(lcm_AK1_BK1,
MfmaSelector<AComputeDataType,
MfmaSelector<AComputeDataType_,
MPerXdl,
NPerXdl,
BComputeDataType,
BComputeDataType_,
is_single_rate_mfma,
is_scale_mfma>::selected_mfma.k_per_blk);
auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_Selector<
BlockSize,
AComputeDataType,
BComputeDataType,
AElementDataType,
BElementDataType,
AccDataType,
decltype(a_block_desc_ak0_m_ak1),
decltype(b_block_desc_bk0_n_bk1),
@@ -709,8 +713,8 @@ struct GridwiseGemmMultipleABD_xdl_cshuffle
NXdlPerWave,
KPack,
LoopSched,
AComputeDataType,
BComputeDataType>();
AComputeDataType_,
BComputeDataType_>();
auto c_thread_buf = blockwise_gemm.GetCThreadBuffer();
@@ -719,10 +723,10 @@ struct GridwiseGemmMultipleABD_xdl_cshuffle
a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
static_cast<AComputeDataType*>(p_shared), a_block_desc_ak0_m_ak1.GetElementSpaceSize());
static_cast<AElementDataType*>(p_shared), a_block_desc_ak0_m_ak1.GetElementSpaceSize());
auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
static_cast<BComputeDataType*>(p_shared) + a_block_space_size_aligned,
static_cast<BElementDataType*>(p_shared) + a_block_space_size_aligned,
b_block_desc_bk0_n_bk1.GetElementSpaceSize());
constexpr auto a_block_slice_copy_step = make_multi_index(KPerBlock / AK1, 0, 0);

45
include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v2r3.hpp
View File

@@ -164,6 +164,8 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
using ThisThreadBlock = ThisThreadBlock<BlockSize>;
using ElementDataTypeAB = conditional_t<is_same_v<FloatAB, ck::tf32_t>, float, FloatAB>;
__host__ static auto CalculateGridSize(index_t M, index_t N)
{
return std::make_tuple(Block2CTileMap::CalculateGridSize(M, N), 1, 1);
@@ -236,8 +238,8 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
// Argument
struct Argument : public Problem, public tensor_operation::device::BaseArgument
{
__host__ Argument(const FloatAB* p_a_grid_,
const FloatAB* p_b_grid_,
__host__ Argument(const ElementDataTypeAB* p_a_grid_,
const ElementDataTypeAB* p_b_grid_,
FloatC* p_c_grid_,
index_t M_,
index_t N_,
@@ -252,8 +254,8 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
{
}
const FloatAB* p_a_grid;
const FloatAB* p_b_grid;
const ElementDataTypeAB* p_a_grid;
const ElementDataTypeAB* p_b_grid;
FloatC* p_c_grid;
};
@@ -329,7 +331,8 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
constexpr auto b_block_space_size_aligned =
math::integer_least_multiple(b_block_desc_k0_n_k1.GetElementSpaceSize(), max_lds_align);
return (a_block_space_size_aligned + b_block_space_size_aligned) * sizeof(FloatAB);
return (a_block_space_size_aligned + b_block_space_size_aligned) *
sizeof(ElementDataTypeAB);
}
template <
@@ -450,8 +453,8 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
using BlockwiseGemm =
BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1<BlockSize,
FloatABAdjusted,
FloatABAdjusted,
ElementDataTypeAB,
ElementDataTypeAB,
FloatAcc,
decltype(a_block_desc_k0_m_k1),
decltype(b_block_desc_k0_n_k1),
@@ -459,7 +462,9 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
NPerXdl,
MXdlPerWave,
NXdlPerWave,
K1>;
K1,
FloatABAdjusted,
FloatABAdjusted>;
return BlockwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(c_grid_desc_m_n);
}
@@ -471,8 +476,8 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
typename AGridDesc_K0_M_K1,
typename BGridDesc_K0_N_K1,
typename CGridDesc_M_N>
__device__ static void Run(const FloatAB* p_a_grid,
const FloatAB* p_b_grid,
__device__ static void Run(const ElementDataTypeAB* p_a_grid,
const ElementDataTypeAB* p_b_grid,
FloatC* p_c_grid,
void* __restrict__ p_shared,
const AGridDesc_K0_M_K1& a_grid_desc_k0_m_k1,
@@ -533,8 +538,8 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
Sequence<K0PerBlock, MPerBlock, K1>,
ABlockTransferThreadClusterLengths_K0_M_K1,
ABlockTransferThreadClusterArrangeOrder,
FloatAB,
FloatABAdjusted,
ElementDataTypeAB,
ElementDataTypeAB,
decltype(a_grid_desc_k0_m_k1),
decltype(a_block_desc_k0_m_k1),
ABlockTransferSrcAccessOrder,
@@ -564,8 +569,8 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
Sequence<K0PerBlock, NPerBlock, K1>,
BBlockTransferThreadClusterLengths_K0_N_K1,
BBlockTransferThreadClusterArrangeOrder,
FloatAB,
FloatABAdjusted,
ElementDataTypeAB,
ElementDataTypeAB,
decltype(b_grid_desc_k0_n_k1),
decltype(b_block_desc_k0_n_k1),
BBlockTransferSrcAccessOrder,
@@ -595,8 +600,8 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
// sanity check
auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_Selector<
BlockSize,
FloatABAdjusted,
FloatABAdjusted,
ElementDataTypeAB,
ElementDataTypeAB,
FloatAcc,
decltype(a_block_desc_k0_m_k1),
decltype(b_block_desc_k0_n_k1),
@@ -605,7 +610,9 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
MXdlPerWave,
NXdlPerWave,
K1,
LoopSched>();
LoopSched,
FloatABAdjusted,
FloatABAdjusted>();
auto c_thread_buf = blockwise_gemm.GetCThreadBuffer();
@@ -614,10 +621,10 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
math::integer_least_multiple(a_block_desc_k0_m_k1.GetElementSpaceSize(), max_lds_align);
auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
static_cast<FloatABAdjusted*>(p_shared), a_block_desc_k0_m_k1.GetElementSpaceSize());
static_cast<ElementDataTypeAB*>(p_shared), a_block_desc_k0_m_k1.GetElementSpaceSize());
auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
static_cast<FloatABAdjusted*>(p_shared) + a_block_space_size_aligned,
static_cast<ElementDataTypeAB*>(p_shared) + a_block_space_size_aligned,
b_block_desc_k0_n_k1.GetElementSpaceSize());
constexpr auto a_block_slice_copy_step = make_multi_index(K0PerBlock, 0, 0);

4
include/ck/utility/amd_xdlops.hpp
View File

@@ -1647,8 +1647,8 @@ struct intrin_mfma_f32_16x16x8xf32<16, 16>
__device__ static void Run(const float2_t& reg_a, const float2_t& reg_b, FloatC& reg_c)
{
#if defined(__gfx94__)
reg_c.template AsType<float16_t>()(Number<0>{}) = __builtin_amdgcn_mfma_f32_16x16x8_xf32(
reg_a, reg_b, reg_c.template AsType<float16_t>()[Number<0>{}], 0, 0, 0);
reg_c.template AsType<float4_t>()(Number<0>{}) = __builtin_amdgcn_mfma_f32_16x16x8_xf32(
reg_a, reg_b, reg_c.template AsType<float4_t>()[Number<0>{}], 0, 0, 0);
#else
ignore = reg_a;
ignore = reg_b;

35
library/include/ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_bilinear_instance.hpp
View File

@@ -16,6 +16,7 @@ namespace instance {
using BF16 = ck::bhalf_t;
using F16 = ck::half_t;
using F32 = float;
using TF32 = ck::tf32_t;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
@@ -139,6 +140,40 @@ using device_grouped_conv_fwd_xdl_bilinear_f32_instances = std::tuple<
// clang-format on
>;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionForwardSpecialization ConvSpec>
using device_grouped_conv_fwd_xdl_bilinear_f32_tf32_instances = std::tuple<
// clang-format off
//########################################| NumDim| A| B| Ds| E| AData| BData| AccData| CShuffle| Ds| EData| A| B| CDE| ConvForward| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//########################################| Spatial| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Specialization| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//########################################| | | | | | | | | | | | Operation| Operation| Operation| | | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<F32>, F32, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 64, 64, 64, 16, 4, 4, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, 1, 1, S<1, 8, 1, 8>, 1, TF32, TF32>,
// instances for small conv.K and conv.C
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<F32>, F32, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 64, 64, 32, 16, 4, 4, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 8>, 1, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<F32>, F32, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 256, 128, 128, 16, 4, 4, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<F32>, F32, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 256, 256, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<F32>, F32, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 256, 128, 256, 16, 4, 4, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<F32>, F32, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 128, 128, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 16>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<F32>, F32, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 256, 128, 128, 16, 4, 4, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<F32>, F32, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 128, 128, 64, 16, 4, 4, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 8>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<F32>, F32, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 128, 64, 128, 16, 4, 4, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 16>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<F32>, F32, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 64, 64, 64, 16, 4, 4, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 8>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<F32>, F32, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 256, 128, 64, 16, 4, 4, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<F32>, F32, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 256, 64, 128, 16, 4, 4, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<F32>, F32, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 128, 128, 32, 16, 4, 4, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 8>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<F32>, F32, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 128, 32, 128, 16, 4, 4, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 16>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<F32>, F32, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 64, 64, 32, 16, 4, 4, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 8>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<F32>, F32, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 64, 32, 64, 16, 4, 4, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 8>, 4, TF32, TF32>
// clang-format on
>;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,

22
library/include/ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_comp_instance.hpp
View File

@@ -24,6 +24,7 @@ using BF8 = ck::bf8_t;
using BF16 = ck::bhalf_t;
using F16 = ck::half_t;
using F32 = float;
using TF32 = ck::tf32_t;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
@@ -205,6 +206,27 @@ using device_grouped_conv_fwd_xdl_f32_comp_instances = std::tuple<
// clang-format on
>;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionForwardSpecialization ConvSpec,
typename DsDataTypes = Tuple<>,
typename OutElementOp = PassThrough>
using device_grouped_conv_fwd_xdl_f32_tf32_comp_instances = std::tuple<
// clang-format off
//########################################| NumDim| A| B| Ds| E| AData| BData| AccData| CShuffle| Ds| EData| A| B| CDE| ConvForward| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//########################################| Spatial| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Specialization| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//########################################| | | | | | | | | | | | Operation| Operation| Operation| | | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsDataTypes, F32, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, 1, 1, S<1, 32, 1, 8>, 8, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsDataTypes, F32, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 256, 128, 128, 64, 8, 8, 32, 32, 2, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, 1, 1, S<1, 32, 1, 8>, 8, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsDataTypes, F32, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 256, 128, 128, 64, 8, 8, 32, 32, 2, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, 1, 1, S<1, 32, 1, 8>, 8, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v5, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsDataTypes, F32, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 256, 128, 128, 64, 8, 8, 32, 32, 2, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, 1, 1, S<1, 32, 1, 8>, 8, BlockGemmPipelineScheduler::Interwave, BlockGemmPipelineVersion::v1, TF32, TF32>
// clang-format on
>;
// double rate mfma instances on gfx950
template <index_t NDimSpatial,
typename ALayout,

19
library/include/ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_instance.hpp
View File

@@ -253,6 +253,25 @@ using device_grouped_conv_fwd_xdl_f32_generic_instances = std::tuple<
// clang-format on
>;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionForwardSpecialization ConvSpec,
typename DsDataTypes = Tuple<>,
typename OutElementOp = PassThrough>
using device_grouped_conv_fwd_xdl_f32_tf32_generic_instances = std::tuple<
// clang-format off
//########################################| NumDim| A| B| Ds| E| AData| BData| AccData| CShuffle| Ds| EData| A| B| CDE| ConvForward| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| AComputeType| BComputeType|
//########################################| Spatial| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Specialization| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector| DATATYPE | DATATYPE |
//########################################| | | | | | | | | | | | Operation| Operation| Operation| | | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl |
//########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsDataTypes, F32, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 1, 64, 64, 64, 16, 4, 4, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, 1, 1, S<1, 8, 1, 8>, 1, TF32, TF32>
// clang-format on
>;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,

22
library/include/ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_large_tensor_instance.hpp
View File

@@ -16,6 +16,7 @@ namespace instance {
using BF16 = ck::bhalf_t;
using F16 = ck::half_t;
using F32 = float;
using TF32 = ck::tf32_t;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
@@ -99,6 +100,27 @@ using device_grouped_conv_fwd_xdl_large_tensor_f32_instances = std::tuple<
// clang-format on
>;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionForwardSpecialization ConvSpec,
typename DsDataTypes = Tuple<>,
typename OutElementOp = PassThrough>
using device_grouped_conv_fwd_xdl_large_tensor_f32_tf32_instances = std::tuple<
// clang-format off
//########################################| NumDim| A| B| Ds| E| AData| BData| AccData| CShuffle| Ds| EData| A| B| CDE| ConvForward| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//########################################| Spatial| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Specialization| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//########################################| | | | | | | | | | | | Operation| Operation| Operation| | | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvFwdMultipleD_Xdl_CShuffle_Large_Tensor<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsDataTypes, F32, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 1, 64, 64, 64, 16, 4, 4, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, 1, 1, S<1, 8, 1, 8>, 1, TF32, TF32>,
DeviceGroupedConvFwdMultipleD_Xdl_CShuffle_Large_Tensor<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsDataTypes, F32, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 1, 256, 256, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, TF32, TF32>
// clang-format on
>;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,

38
library/include/ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_mem_instance.hpp
View File

@@ -24,6 +24,7 @@ using BF8 = ck::bf8_t;
using BF16 = ck::bhalf_t;
using F16 = ck::half_t;
using F32 = float;
using TF32 = ck::tf32_t;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
@@ -64,7 +65,7 @@ using device_grouped_conv_fwd_xdl_bf16_mem_instances = std::tuple<
//########################################| Spatial| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Specialization| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//########################################| | | | | | | | | | | | Operation| Operation| Operation| | | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// Latency friendly
// Latency friendly
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, BF16, BF16, F32, BF16, DsDataTypes, BF16, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 128, 32, 16, 64, 8, 8, 16, 16, 1, 1, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, 1, 1, S<1, 16, 1, 8>, 2, BlkGemmPipeSched, BlockGemmPipelineVersion::v1>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, BF16, BF16, F32, BF16, DsDataTypes, BF16, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 64, 16, 16, 128, 8, 8, 16, 16, 1, 1, S<16, 4, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, S<16, 4, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, 1, 1, S<1, 16, 1, 4>, 4, BlkGemmPipeSched, BlockGemmPipelineVersion::v1>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, BF16, BF16, F32, BF16, DsDataTypes, BF16, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 64, 16, 16, 64, 8, 8, 16, 16, 1, 1, S<8, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, S<8, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, 1, 1, S<1, 16, 1, 4>, 4, BlkGemmPipeSched, BlockGemmPipelineVersion::v1>,
@@ -163,6 +164,41 @@ using device_grouped_conv_fwd_xdl_f32_mem_instances = std::tuple<
// clang-format on
>;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionForwardSpecialization ConvSpec,
BlockGemmPipelineScheduler BlkGemmPipeSched,
typename DsDataTypes = Tuple<>,
typename OutElementOp = PassThrough>
using device_grouped_conv_fwd_xdl_f32_tf32_mem_instances = std::tuple<
// clang-format off
//########################################| NumDim| A| B| Ds| E| AData| BData| AccData| CShuffle| Ds| EData| A| B| CDE| ConvForward| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//########################################| Spatial| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Specialization| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//########################################| | | | | | | | | | | | Operation| Operation| Operation| | | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsDataTypes, F32, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 128, 32, 16, 64, 8, 8, 16, 16, 1, 1, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, 1, 1, S<1, 16, 1, 8>, 2, BlkGemmPipeSched, BlockGemmPipelineVersion::v1, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsDataTypes, F32, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 64, 16, 16, 128, 8, 8, 16, 16, 1, 1, S<16, 4, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, S<16, 4, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, 1, 1, S<1, 16, 1, 4>, 4, BlkGemmPipeSched, BlockGemmPipelineVersion::v1, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsDataTypes, F32, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 64, 16, 16, 64, 8, 8, 16, 16, 1, 1, S<8, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, S<8, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, 1, 1, S<1, 16, 1, 4>, 4, BlkGemmPipeSched, BlockGemmPipelineVersion::v1, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsDataTypes, F32, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 128, 16, 32, 64, 8, 8, 16, 16, 1, 1, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, 1, 1, S<1, 16, 1, 8>, 4, BlkGemmPipeSched, BlockGemmPipelineVersion::v1, TF32, TF32>,
// Memory friendly
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsDataTypes, F32, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 128, 128, 32, 64, 8, 8, 32, 32, 2, 1, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, 1, 1, S<1, 16, 1, 8>, 4, BlkGemmPipeSched, BlockGemmPipelineVersion::v2, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsDataTypes, F32, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 128, 128, 16, 64, 8, 8, 16, 16, 4, 1, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, 1, 1, S<1, 16, 1, 8>, 2, BlkGemmPipeSched, BlockGemmPipelineVersion::v2, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsDataTypes, F32, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 128, 64, 32, 64, 8, 8, 32, 32, 1, 1, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, 1, 1, S<1, 16, 1, 8>, 4, BlkGemmPipeSched, BlockGemmPipelineVersion::v2, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsDataTypes, F32, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 128, 64, 16, 64, 8, 8, 16, 16, 2, 1, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, 1, 1, S<1, 16, 1, 8>, 2, BlkGemmPipeSched, BlockGemmPipelineVersion::v2, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsDataTypes, F32, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 128, 32, 16, 64, 8, 8, 16, 16, 1, 1, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, 1, 1, S<1, 16, 1, 8>, 2, BlkGemmPipeSched, BlockGemmPipelineVersion::v2, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsDataTypes, F32, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 64, 16, 16, 128, 8, 8, 16, 16, 1, 1, S<16, 4, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, S<16, 4, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, 1, 1, S<1, 16, 1, 4>, 4, BlkGemmPipeSched, BlockGemmPipelineVersion::v2, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsDataTypes, F32, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 64, 16, 16, 64, 8, 8, 16, 16, 1, 1, S<8, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, S<8, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, 1, 1, S<1, 16, 1, 4>, 4, BlkGemmPipeSched, BlockGemmPipelineVersion::v2, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsDataTypes, F32, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 128, 16, 32, 64, 8, 8, 16, 16, 1, 1, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, 1, 1, S<1, 16, 1, 8>, 4, BlkGemmPipeSched, BlockGemmPipelineVersion::v2, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsDataTypes, F32, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 128, 16, 64, 64, 8, 8, 16, 16, 1, 2, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, 1, 1, S<1, 16, 1, 8>, 4, BlkGemmPipeSched, BlockGemmPipelineVersion::v2, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsDataTypes, F32, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 128, 32, 64, 64, 8, 8, 32, 32, 1, 1, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, 1, 1, S<1, 16, 1, 8>, 8, BlkGemmPipeSched, BlockGemmPipelineVersion::v2, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsDataTypes, F32, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 128, 16, 128, 64, 8, 8, 16, 16, 1, 4, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, 1, 1, S<1, 16, 1, 8>, 4, BlkGemmPipeSched, BlockGemmPipelineVersion::v2, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsDataTypes, F32, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 128, 32, 128, 64, 8, 8, 32, 32, 1, 2, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 0, 1, 1, S<1, 16, 1, 8>, 8, BlkGemmPipeSched, BlockGemmPipelineVersion::v2, TF32, TF32>
// clang-format on
>;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,

22
library/include/ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_merged_groups_instance.hpp
View File

@@ -16,6 +16,7 @@ namespace instance {
using BF16 = ck::bhalf_t;
using F16 = ck::half_t;
using F32 = float;
using TF32 = ck::tf32_t;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
@@ -142,6 +143,27 @@ using device_grouped_conv_fwd_xdl_merged_groups_f32_instances = std::tuple<
// clang-format on
>;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionForwardSpecialization ConvSpec,
typename DsDataTypes = Tuple<>,
typename OutElementOp = PassThrough>
using device_grouped_conv_fwd_xdl_merged_groups_f32_tf32_instances = std::tuple<
// clang-format off
//########################################| NumDim| A| B| Ds| E| AData| BData| AccData| CShuffle| Ds| EData| A| B| CDE| ConvForward| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//########################################| Spatial| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Specialization| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//########################################| | | | | | | | | | | | Operation| Operation| Operation| | | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// Instances with NumGroupsPerBatch > 1
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsDataTypes, F32, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 1, 64, 64, 16, 16, 4, 4, 16, 16, 4, 1, S< 4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, S< 4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, 1, 1, S<1, 16, 1, 4>, 1, TF32, TF32, LoopScheduler::Default, 8>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsDataTypes, F32, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 1, 64, 64, 16, 16, 4, 4, 16, 16, 4, 1, S< 4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, S< 4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, 1, 1, S<1, 16, 1, 4>, 1, TF32, TF32, LoopScheduler::Default, 16>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsDataTypes, F32, PassThrough, PassThrough, OutElementOp, ConvSpec, GemmMNKPadding, 1, 64, 64, 16, 16, 4, 4, 16, 16, 4, 1, S< 4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, S< 4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, 1, 1, S<1, 16, 1, 4>, 1, TF32, TF32, LoopScheduler::Default, 32>
// clang-format on
>;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,

35
library/include/ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_scale_instance.hpp
View File

@@ -16,6 +16,7 @@ namespace instance {
using BF16 = ck::bhalf_t;
using F16 = ck::half_t;
using F32 = float;
using TF32 = ck::tf32_t;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
@@ -139,6 +140,40 @@ using device_grouped_conv_fwd_xdl_scale_f32_instances = std::tuple<
// clang-format on
>;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionForwardSpecialization ConvSpec>
using device_grouped_conv_fwd_xdl_scale_f32_tf32_instances = std::tuple<
// clang-format off
//########################################| NumDim| A| B| Ds| E| AData| BData| AccData| CShuffle| Ds| EData| A| B| CDE| ConvForward| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//########################################| Spatial| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Specialization| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//########################################| | | | | | | | | | | | Operation| Operation| Operation| | | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<>, F32, PassThrough, PassThrough, Scale, ConvSpec, GemmMNKPadding, 1, 64, 64, 64, 16, 4, 4, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, 1, 1, S<1, 8, 1, 8>, 1, TF32, TF32>,
// instances for small conv.K and conv.C
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<>, F32, PassThrough, PassThrough, Scale, ConvSpec, GemmMNKPadding, 1, 64, 64, 32, 16, 4, 4, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 8>, 1, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<>, F32, PassThrough, PassThrough, Scale, ConvSpec, GemmMNKPadding, 1, 256, 128, 128, 16, 4, 4, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<>, F32, PassThrough, PassThrough, Scale, ConvSpec, GemmMNKPadding, 1, 256, 256, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<>, F32, PassThrough, PassThrough, Scale, ConvSpec, GemmMNKPadding, 1, 256, 128, 256, 16, 4, 4, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<>, F32, PassThrough, PassThrough, Scale, ConvSpec, GemmMNKPadding, 1, 128, 128, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 16>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<>, F32, PassThrough, PassThrough, Scale, ConvSpec, GemmMNKPadding, 1, 256, 128, 128, 16, 4, 4, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<>, F32, PassThrough, PassThrough, Scale, ConvSpec, GemmMNKPadding, 1, 128, 128, 64, 16, 4, 4, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 8>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<>, F32, PassThrough, PassThrough, Scale, ConvSpec, GemmMNKPadding, 1, 128, 64, 128, 16, 4, 4, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 16>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<>, F32, PassThrough, PassThrough, Scale, ConvSpec, GemmMNKPadding, 1, 64, 64, 64, 16, 4, 4, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 8>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<>, F32, PassThrough, PassThrough, Scale, ConvSpec, GemmMNKPadding, 1, 256, 128, 64, 16, 4, 4, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<>, F32, PassThrough, PassThrough, Scale, ConvSpec, GemmMNKPadding, 1, 256, 64, 128, 16, 4, 4, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<>, F32, PassThrough, PassThrough, Scale, ConvSpec, GemmMNKPadding, 1, 128, 128, 32, 16, 4, 4, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 8>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<>, F32, PassThrough, PassThrough, Scale, ConvSpec, GemmMNKPadding, 1, 128, 32, 128, 16, 4, 4, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 16>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<>, F32, PassThrough, PassThrough, Scale, ConvSpec, GemmMNKPadding, 1, 64, 64, 32, 16, 4, 4, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 8>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, Tuple<>, F32, PassThrough, PassThrough, Scale, ConvSpec, GemmMNKPadding, 1, 64, 32, 64, 16, 4, 4, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 8>, 4, TF32, TF32>
// clang-format on
>;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,

24
library/include/ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_scaleadd_ab_instance.hpp
View File

@@ -16,6 +16,7 @@ namespace instance {
using BF16 = ck::bhalf_t;
using F16 = ck::half_t;
using F32 = float;
using TF32 = ck::tf32_t;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
@@ -89,7 +90,7 @@ using device_grouped_conv_fwd_xdl_scaleadd_ab_f32_instances = std::tuple<
//########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, ck::Tuple<>,ELayout, ck::Tuple<F32, F32>, ck::Tuple<F32, F32>, F32, F32, ck::Tuple<>, F32, ScaleAdd, ScaleAdd, PassThrough, ConvSpec, GemmMNKPadding, 1, 64, 64, 64, 16, 4, 4, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 8, 1, 8>, 1>,
// instances for small conv.K and conv.C
// instances for small conv.K and conv.C
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, ck::Tuple<>,ELayout, ck::Tuple<F32, F32>, ck::Tuple<F32, F32>, F32, F32, ck::Tuple<>, F32, ScaleAdd, ScaleAdd, PassThrough, ConvSpec, GemmMNKPadding, 1, 64, 64, 32, 16, 4, 4, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 8>, 1>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, ck::Tuple<>,ELayout, ck::Tuple<F32, F32>, ck::Tuple<F32, F32>, F32, F32, ck::Tuple<>, F32, ScaleAdd, ScaleAdd, PassThrough, ConvSpec, GemmMNKPadding, 1, 256, 128, 128, 16, 4, 4, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 16, 1, 16>, 4>,
@@ -97,6 +98,27 @@ using device_grouped_conv_fwd_xdl_scaleadd_ab_f32_instances = std::tuple<
// clang-format on
>;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename ELayout,
ConvolutionForwardSpecialization ConvSpec>
using device_grouped_conv_fwd_xdl_scaleadd_ab_f32_tf32_instances = std::tuple<
// clang-format off
//########################################| NumDim| A| B| Ds| E| AData| BData| AccData| CShuffle| Ds| EData| A| B| CDE| ConvForward| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//########################################| Spatial| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Specialization| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//########################################| | | | | | | | | | | | Operation| Operation| Operation| | | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, ck::Tuple<>,ELayout, ck::Tuple<F32, F32>, ck::Tuple<F32, F32>, F32, F32, ck::Tuple<>, F32, ScaleAdd, ScaleAdd, PassThrough, ConvSpec, GemmMNKPadding, 1, 64, 64, 64, 16, 4, 4, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 8, 1, 8>, 1, TF32, TF32>,
// instances for small conv.K and conv.C
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, ck::Tuple<>,ELayout, ck::Tuple<F32, F32>, ck::Tuple<F32, F32>, F32, F32, ck::Tuple<>, F32, ScaleAdd, ScaleAdd, PassThrough, ConvSpec, GemmMNKPadding, 1, 64, 64, 32, 16, 4, 4, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 8>, 1, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, ck::Tuple<>,ELayout, ck::Tuple<F32, F32>, ck::Tuple<F32, F32>, F32, F32, ck::Tuple<>, F32, ScaleAdd, ScaleAdd, PassThrough, ConvSpec, GemmMNKPadding, 1, 256, 128, 128, 16, 4, 4, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 16, 1, 16>, 4, TF32, TF32>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, ck::Tuple<>,ELayout, ck::Tuple<F32, F32>, ck::Tuple<F32, F32>, F32, F32, ck::Tuple<>, F32, ScaleAdd, ScaleAdd, PassThrough, ConvSpec, GemmMNKPadding, 1, 256, 256, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, TF32, TF32>
// clang-format on
>;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,

51
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_forward.hpp
View File

@@ -428,26 +428,39 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
{
#ifdef CK_ENABLE_FP32
if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, float> && is_same_v<AComputeType, float> &&
is_same_v<BComputeType, float>)
is_same_v<OutDataType, float>)
{
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instances(op_ptrs);
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_16x16_instances(op_ptrs);
add_device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_comp_instances(op_ptrs);
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_mem_intra_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_mem_inter_instances(
op_ptrs);
}
if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, float> && is_same_v<AComputeType, TF32> &&
is_same_v<BComputeType, TF32>)
{
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(op_ptrs);
if constexpr(is_same_v<AComputeType, BComputeType> && is_same_v<BComputeType, TF32>)
{
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_comp_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_inter_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_intra_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
op_ptrs);
}
else
{
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instances(op_ptrs);
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_16x16_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_comp_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_mem_intra_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_mem_inter_instances(
op_ptrs);
}
}
#endif

110
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_bias_clamp.hpp
View File

@@ -127,24 +127,44 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
#endif
#ifdef CK_ENABLE_FP32
if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, float> && is_same_v<AComputeType, float> &&
is_same_v<BComputeType, float>)
is_same_v<OutDataType, float>)
{
add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_16x16_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_bias_clamp_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_bias_clamp_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_comp_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_mem_intra_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_mem_inter_instances(
op_ptrs);
static_assert(is_same_v<AComputeType, BComputeType>,
"Error: AComputeType and BComputeType should be the same");
if constexpr(is_same_v<AComputeType, TF32>)
{
add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_bias_clamp_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_tf32_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_comp_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_inter_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_intra_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_bias_clamp_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_tf32_instances(
op_ptrs);
}
else
{
add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_16x16_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_bias_clamp_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_bias_clamp_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_comp_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_mem_intra_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_mem_inter_instances(
op_ptrs);
}
}
#endif
}
// layout NDHWGC/GKZYXC/NDHWGK
@@ -197,32 +217,44 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
#endif
#ifdef CK_ENABLE_FP32
if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, float> && is_same_v<AComputeType, float> &&
is_same_v<BComputeType, float>)
is_same_v<OutDataType, float>)
{
add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_16x16_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_bias_clamp_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_bias_clamp_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_comp_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_mem_intra_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_mem_inter_instances(
op_ptrs);
static_assert(is_same_v<AComputeType, BComputeType>,
"Error: AComputeType and BComputeType should be the same");
if constexpr(is_same_v<AComputeType, TF32>)
{
add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_bias_clamp_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_comp_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_inter_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_intra_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_bias_clamp_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
op_ptrs);
}
else
{
add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_16x16_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_bias_clamp_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_bias_clamp_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_comp_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_mem_intra_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_mem_inter_instances(
op_ptrs);
}
}
if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, float> && is_same_v<AComputeType, TF32> &&
is_same_v<BComputeType, TF32>)
{
add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
op_ptrs);
}
#endif
}
#endif // CK_USE_XDL

176
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_bias_clamp_xdl.inc
View File

@@ -480,6 +480,22 @@ void add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_instance
PassThrough,
AddClamp>>>& instances);
void add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
F32,
F32,
Tuple<F32>,
F32,
PassThrough,
PassThrough,
AddClamp,
TF32,
TF32>>>& instances);
void add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_16x16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
@@ -508,6 +524,22 @@ void add_device_grouped_conv2d_fwd_bias_clamp_xdl_large_tensor_nhwgc_gkyxc_nhwgk
PassThrough,
AddClamp>>>& instances);
void add_device_grouped_conv2d_fwd_bias_clamp_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
F32,
F32,
Tuple<F32>,
F32,
PassThrough,
PassThrough,
AddClamp,
TF32,
TF32>>>& instances);
void add_device_grouped_conv2d_fwd_bias_clamp_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
@@ -522,6 +554,22 @@ void add_device_grouped_conv2d_fwd_bias_clamp_xdl_merged_groups_nhwgc_gkyxc_nhwg
PassThrough,
AddClamp>>>& instances);
void add_device_grouped_conv2d_fwd_bias_clamp_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
F32,
F32,
Tuple<F32>,
F32,
PassThrough,
PassThrough,
AddClamp,
TF32,
TF32>>>& instances);
void add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_comp_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
@@ -536,6 +584,22 @@ void add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_comp_ins
PassThrough,
AddClamp>>>& instances);
void add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_comp_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
F32,
F32,
Tuple<F32>,
F32,
PassThrough,
PassThrough,
AddClamp,
TF32,
TF32>>>& instances);
void add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_mem_intra_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
@@ -550,6 +614,22 @@ void add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_mem_intr
PassThrough,
AddClamp>>>& instances);
void add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_intra_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
F32,
F32,
Tuple<F32>,
F32,
PassThrough,
PassThrough,
AddClamp,
TF32,
TF32>>>& instances);
void add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_mem_inter_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
@@ -564,6 +644,22 @@ void add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_mem_inte
PassThrough,
AddClamp>>>& instances);
void add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_inter_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
F32,
F32,
Tuple<F32>,
F32,
PassThrough,
PassThrough,
AddClamp,
TF32,
TF32>>>& instances);
void add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
@@ -622,6 +718,22 @@ void add_device_grouped_conv3d_fwd_bias_clamp_xdl_large_tensor_ndhwgc_gkzyxc_ndh
PassThrough,
AddClamp>>>& instances);
void add_device_grouped_conv3d_fwd_bias_clamp_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK>,
NDHWGK,
F32,
F32,
Tuple<F32>,
F32,
PassThrough,
PassThrough,
AddClamp,
TF32,
TF32>>>& instances);
void add_device_grouped_conv3d_fwd_bias_clamp_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
@@ -636,6 +748,22 @@ void add_device_grouped_conv3d_fwd_bias_clamp_xdl_merged_groups_ndhwgc_gkzyxc_nd
PassThrough,
AddClamp>>>& instances);
void add_device_grouped_conv3d_fwd_bias_clamp_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK>,
NDHWGK,
F32,
F32,
Tuple<F32>,
F32,
PassThrough,
PassThrough,
AddClamp,
TF32,
TF32>>>& instances);
void add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_comp_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
@@ -650,6 +778,22 @@ void add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_comp_
PassThrough,
AddClamp>>>& instances);
void add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_comp_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK>,
NDHWGK,
F32,
F32,
Tuple<F32>,
F32,
PassThrough,
PassThrough,
AddClamp,
TF32,
TF32>>>& instances);
void add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_mem_intra_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
@@ -664,6 +808,22 @@ void add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_mem_i
PassThrough,
AddClamp>>>& instances);
void add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_intra_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK>,
NDHWGK,
F32,
F32,
Tuple<F32>,
F32,
PassThrough,
PassThrough,
AddClamp,
TF32,
TF32>>>& instances);
void add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_mem_inter_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
@@ -678,6 +838,22 @@ void add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_mem_i
PassThrough,
AddClamp>>>& instances);
void add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_inter_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK>,
NDHWGK,
F32,
F32,
Tuple<F32>,
F32,
PassThrough,
PassThrough,
AddClamp,
TF32,
TF32>>>& instances);
#endif
} // namespace instance

28
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_bilinear.hpp
View File

@@ -68,6 +68,22 @@ void add_device_grouped_conv3d_fwd_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_f32_instanc
PassThrough,
PassThrough,
Bilinear>>>& instances);
void add_device_grouped_conv3d_fwd_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
ck::Tuple<NDHWGK>,
NDHWGK,
F32,
F32,
ck::Tuple<F32>,
F32,
PassThrough,
PassThrough,
Bilinear,
TF32,
TF32>>>& instances);
#endif
#ifdef CK_ENABLE_INT8
@@ -137,8 +153,16 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, float>)
{
add_device_grouped_conv3d_fwd_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_f32_instances(
op_ptrs);
if constexpr(is_same_v<ComputeType, TF32>)
{
add_device_grouped_conv3d_fwd_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
op_ptrs);
}
else
{
add_device_grouped_conv3d_fwd_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_f32_instances(
op_ptrs);
}
}
#endif
#ifdef CK_ENABLE_FP16

109
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_clamp.hpp
View File

@@ -125,23 +125,44 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
#endif
#ifdef CK_ENABLE_FP32
if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, float> && is_same_v<AComputeType, float> &&
is_same_v<BComputeType, float>)
is_same_v<OutDataType, float>)
{
add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_instances(op_ptrs);
add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_16x16_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_clamp_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_clamp_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_comp_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_mem_intra_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_mem_inter_instances(
op_ptrs);
static_assert(is_same_v<AComputeType, BComputeType>,
"Error: AComputeType and BComputeType should be the same");
if constexpr(is_same_v<AComputeType, TF32>)
{
add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_clamp_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_tf32_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_comp_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_inter_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_intra_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_clamp_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_tf32_instances(
op_ptrs);
}
else
{
add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_16x16_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_clamp_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_clamp_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_comp_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_mem_intra_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_mem_inter_instances(
op_ptrs);
}
}
#endif
}
// layout NDHWGC/GKZYXC/NDHWGK
@@ -193,30 +214,42 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
#endif
#ifdef CK_ENABLE_FP32
if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, float> && is_same_v<AComputeType, float> &&
is_same_v<BComputeType, float>)
is_same_v<OutDataType, float>)
{
add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instances(op_ptrs);
add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_16x16_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_clamp_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_clamp_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_comp_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_mem_intra_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_mem_inter_instances(
op_ptrs);
}
if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, float> && is_same_v<AComputeType, TF32> &&
is_same_v<BComputeType, TF32>)
{
add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
op_ptrs);
static_assert(is_same_v<AComputeType, BComputeType>,
"Error: AComputeType and BComputeType should be the same");
if constexpr(is_same_v<AComputeType, TF32>)
{
add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_clamp_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_comp_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_inter_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_intra_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_clamp_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
op_ptrs);
}
else
{
add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_16x16_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_clamp_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_clamp_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_comp_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_mem_intra_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_mem_inter_instances(
op_ptrs);
}
}
#endif
}

176
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_clamp_xdl.inc
View File

@@ -480,6 +480,22 @@ void add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_instances(
PassThrough,
Clamp>>>& instances);
void add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
F32,
F32,
Tuple<>,
F32,
PassThrough,
PassThrough,
Clamp,
TF32,
TF32>>>& instances);
void add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_16x16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
@@ -508,6 +524,22 @@ void add_device_grouped_conv2d_fwd_clamp_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_
PassThrough,
Clamp>>>& instances);
void add_device_grouped_conv2d_fwd_clamp_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
F32,
F32,
Tuple<>,
F32,
PassThrough,
PassThrough,
Clamp,
TF32,
TF32>>>& instances);
void add_device_grouped_conv2d_fwd_clamp_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
@@ -522,6 +554,22 @@ void add_device_grouped_conv2d_fwd_clamp_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32
PassThrough,
Clamp>>>& instances);
void add_device_grouped_conv2d_fwd_clamp_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
F32,
F32,
Tuple<>,
F32,
PassThrough,
PassThrough,
Clamp,
TF32,
TF32>>>& instances);
void add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_comp_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
@@ -536,6 +584,22 @@ void add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_comp_instance
PassThrough,
Clamp>>>& instances);
void add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_comp_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
F32,
F32,
Tuple<>,
F32,
PassThrough,
PassThrough,
Clamp,
TF32,
TF32>>>& instances);
void add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_mem_intra_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
@@ -550,6 +614,22 @@ void add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_mem_intra_ins
PassThrough,
Clamp>>>& instances);
void add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_intra_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
F32,
F32,
Tuple<>,
F32,
PassThrough,
PassThrough,
Clamp,
TF32,
TF32>>>& instances);
void add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_mem_inter_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
@@ -564,6 +644,22 @@ void add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_mem_inter_ins
PassThrough,
Clamp>>>& instances);
void add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_inter_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
F32,
F32,
Tuple<>,
F32,
PassThrough,
PassThrough,
Clamp,
TF32,
TF32>>>& instances);
void add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
@@ -622,6 +718,22 @@ void add_device_grouped_conv3d_fwd_clamp_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f
PassThrough,
Clamp>>>& instances);
void add_device_grouped_conv3d_fwd_clamp_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Tuple<>,
NDHWGK,
F32,
F32,
Tuple<>,
F32,
PassThrough,
PassThrough,
Clamp,
TF32,
TF32>>>& instances);
void add_device_grouped_conv3d_fwd_clamp_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
@@ -636,6 +748,22 @@ void add_device_grouped_conv3d_fwd_clamp_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_
PassThrough,
Clamp>>>& instances);
void add_device_grouped_conv3d_fwd_clamp_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Tuple<>,
NDHWGK,
F32,
F32,
Tuple<>,
F32,
PassThrough,
PassThrough,
Clamp,
TF32,
TF32>>>& instances);
void add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_comp_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
@@ -650,6 +778,22 @@ void add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_comp_insta
PassThrough,
Clamp>>>& instances);
void add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_comp_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Tuple<>,
NDHWGK,
F32,
F32,
Tuple<>,
F32,
PassThrough,
PassThrough,
Clamp,
TF32,
TF32>>>& instances);
void add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_mem_intra_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
@@ -664,6 +808,22 @@ void add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_mem_intra_
PassThrough,
Clamp>>>& instances);
void add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_intra_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Tuple<>,
NDHWGK,
F32,
F32,
Tuple<>,
F32,
PassThrough,
PassThrough,
Clamp,
TF32,
TF32>>>& instances);
void add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_mem_inter_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
@@ -678,6 +838,22 @@ void add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_mem_inter_
PassThrough,
Clamp>>>& instances);
void add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_inter_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Tuple<>,
NDHWGK,
F32,
F32,
Tuple<>,
F32,
PassThrough,
PassThrough,
Clamp,
TF32,
TF32>>>& instances);
#endif
} // namespace instance

31
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_comp_xdl.inc
View File

@@ -111,6 +111,21 @@ void add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_comp_instances(
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_comp_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough,
TF32,
TF32>>>& instances);
#endif
#ifdef CK_ENABLE_INT8
@@ -281,6 +296,22 @@ void add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_comp_instances(
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_comp_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough,
TF32,
TF32>>>& instances);
#endif
// grouped conv3d forward, NGCDHW/GKCZYX/NGKDHW

31
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_mem_inter_xdl.inc
View File

@@ -55,6 +55,21 @@ void add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_mem_inter_instances
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_inter_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough,
TF32,
TF32>>>& instances);
#endif
#ifdef CK_ENABLE_INT8
@@ -169,6 +184,22 @@ void add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_mem_inter_instan
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_inter_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough,
TF32,
TF32>>>& instances);
#endif
// grouped conv3d forward, NGCDHW/GKCZYX/NGKDHW

30
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_mem_intra_xdl.inc
View File

@@ -55,6 +55,21 @@ void add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_mem_intra_instances
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_intra_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough,
TF32,
TF32>>>& instances);
#endif
#ifdef CK_ENABLE_INT8
@@ -169,6 +184,21 @@ void add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_mem_intra_instan
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_intra_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough,
TF32,
TF32>>>& instances);
#endif
// grouped conv3d forward, NGCDHW/GKCZYX/NGKDHW

27
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_scale.hpp
View File

@@ -68,6 +68,22 @@ void add_device_grouped_conv3d_fwd_xdl_scale_ndhwgc_gkzyxc_ndhwgk_f32_instances(
PassThrough,
PassThrough,
Scale>>>& instances);
void add_device_grouped_conv3d_fwd_xdl_scale_ndhwgc_gkzyxc_ndhwgk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
ck::Tuple<>,
NDHWGK,
F32,
F32,
ck::Tuple<>,
F32,
PassThrough,
PassThrough,
Scale,
TF32,
TF32>>>& instances);
#endif
#ifdef CK_ENABLE_INT8
@@ -137,7 +153,16 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, float>)
{
add_device_grouped_conv3d_fwd_xdl_scale_ndhwgc_gkzyxc_ndhwgk_f32_instances(op_ptrs);
if constexpr(is_same_v<ComputeType, TF32>)
{
add_device_grouped_conv3d_fwd_xdl_scale_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
op_ptrs);
}
else
{
add_device_grouped_conv3d_fwd_xdl_scale_ndhwgc_gkzyxc_ndhwgk_f32_instances(
op_ptrs);
}
}
#endif
#ifdef CK_ENABLE_FP16

16
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_xdl.inc
View File

@@ -211,6 +211,22 @@ void add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_16x16_instances(
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough,
TF32,
TF32>>>& instances);
#endif
#ifdef CK_ENABLE_INT8

32
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_xdl_large_tensor.inc
View File

@@ -55,6 +55,22 @@ void add_device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_instan
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough,
TF32,
TF32>>>& instances);
#endif
#ifdef CK_ENABLE_INT8
@@ -120,6 +136,22 @@ void add_device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_ins
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough,
TF32,
TF32>>>& instances);
#endif
} // namespace instance

32
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_xdl_merged_groups.inc
View File

@@ -84,6 +84,22 @@ void add_device_grouped_conv2d_fwd_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_insta
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv2d_fwd_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough,
TF32,
TF32>>>& instances);
void add_device_grouped_conv2d_fwd_xdl_merged_groups_ngchw_gkcyx_ngkhw_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NGCHW,
@@ -176,6 +192,22 @@ void add_device_grouped_conv3d_fwd_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_in
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv3d_fwd_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough,
TF32,
TF32>>>& instances);
#endif
// grouped conv3d forward, NGCDHW/GKCZYX/NGKDHW

6
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd/CMakeLists.txt
View File

@@ -9,6 +9,7 @@ set(GROUPED_CONV2D_FWD
xdl/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_bf16_instance.cpp
xdl/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f16_instance.cpp
xdl/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_instance.cpp
xdl/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_instance.cpp
xdl/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_bf16_16x16_instance.cpp
xdl/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f16_16x16_instance.cpp
xdl/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_16x16_instance.cpp
@@ -28,12 +29,14 @@ set(GROUPED_CONV2D_FWD
xdl/large_tensor/device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_bf16_instance.cpp
xdl/large_tensor/device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f16_instance.cpp
xdl/large_tensor/device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_instance.cpp
xdl/large_tensor/device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_tf32_instance.cpp
xdl/large_tensor/device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_int8_instance.cpp
# merged groups
# NHWGC, GKYXC, NHWGK
xdl/merged_groups/device_grouped_conv2d_fwd_xdl_merged_groups_nhwgc_gkyxc_nhwgk_bf16_instance.cpp
xdl/merged_groups/device_grouped_conv2d_fwd_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f16_instance.cpp
xdl/merged_groups/device_grouped_conv2d_fwd_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_instance.cpp
xdl/merged_groups/device_grouped_conv2d_fwd_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_tf32_instance.cpp
xdl/merged_groups/device_grouped_conv2d_fwd_xdl_merged_groups_nhwgc_gkyxc_nhwgk_int8_instance.cpp
# NGCHW, GKCYX, NGKHW
xdl/merged_groups/device_grouped_conv2d_fwd_xdl_merged_groups_ngchw_gkcyx_ngkhw_bf16_instance.cpp
@@ -44,9 +47,11 @@ set(GROUPED_CONV2D_FWD
xdl/mem/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_bf16_mem_intra_instance.cpp
xdl/mem/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f16_mem_intra_instance.cpp
xdl/mem/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_mem_intra_instance.cpp
xdl/mem/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_intra_instance.cpp
# NHWGC, GKYXC, NHWGK
xdl/mem/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_bf16_mem_inter_instance.cpp
xdl/mem/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f16_mem_inter_instance.cpp
xdl/mem/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_inter_instance.cpp
xdl/mem/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_mem_inter_instance.cpp
# NGCHW, GKCYX, NGKHW
xdl/mem/device_grouped_conv2d_fwd_xdl_ngchw_gkcyx_ngkhw_bf16_mem_intra_instance.cpp
@@ -61,6 +66,7 @@ set(GROUPED_CONV2D_FWD
xdl/comp/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_bf16_comp_instance.cpp
xdl/comp/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f16_comp_instance.cpp
xdl/comp/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_comp_instance.cpp
xdl/comp/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_comp_instance.cpp
xdl/comp/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_int8_comp_instance.cpp
xdl/comp/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_bf16_comp_2x_instance.cpp
xdl/comp/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f16_comp_2x_instance.cpp

68
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd/xdl/comp/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_comp_instance.cpp Normal file
View File

@@ -0,0 +1,68 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_comp_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_comp_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_comp_instances<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
ConvFwdDefault>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_comp_instances<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
ConvFwd1x1P0>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_comp_instances<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
ConvFwd1x1S1P0>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_comp_instances<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
ConvFwdOddC>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

66
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd/xdl/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_instance.cpp Normal file
View File

@@ -0,0 +1,66 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_instances<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
ConvFwdDefault>{});
add_device_operation_instances(instances,
device_grouped_conv_fwd_xdl_f32_tf32_instances<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
ConvFwd1x1P0>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_instances<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
ConvFwd1x1S1P0>{});
add_device_operation_instances(instances,
device_grouped_conv_fwd_xdl_f32_tf32_instances<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
ConvFwdOddC>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

41
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd/xdl/large_tensor/device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_tf32_instance.cpp Normal file
View File

@@ -0,0 +1,41 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_large_tensor_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_large_tensor_f32_tf32_instances<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
ConvFwdDefault>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

70
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd/xdl/mem/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_inter_instance.cpp Normal file
View File

@@ -0,0 +1,70 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_mem_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_inter_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
ConvFwdDefault,
Interwave>{});
add_device_operation_instances(instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
ConvFwd1x1P0,
Interwave>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
ConvFwd1x1S1P0,
Interwave>{});
add_device_operation_instances(instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
ConvFwdOddC,
Interwave>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

70
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd/xdl/mem/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_intra_instance.cpp Normal file
View File

@@ -0,0 +1,70 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_mem_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_intra_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
ConvFwdDefault,
Intrawave>{});
add_device_operation_instances(instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
ConvFwd1x1P0,
Intrawave>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
ConvFwd1x1S1P0,
Intrawave>{});
add_device_operation_instances(instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
ConvFwdOddC,
Intrawave>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

50
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd/xdl/merged_groups/device_grouped_conv2d_fwd_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_tf32_instance.cpp Normal file
View File

@@ -0,0 +1,50 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_merged_groups_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv2d_fwd_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_merged_groups_f32_tf32_instances<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
ConvFwdDefault>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_merged_groups_f32_tf32_instances<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
ConvFwd3x3>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

50
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd_bias_bnorm_clamp/CMakeLists.txt
View File

@@ -85,6 +85,16 @@ generate_sharded_instantiations(
SRC_LIST GROUPED_CONV2D_FWD_BIAS_BNORM_CLAMP
OUTPUT_DIR ${GENERATED_DIR}/xdl/large_tensor
)
set(GENERATED_DIR ${CMAKE_CURRENT_BINARY_DIR}/generated)
generate_sharded_instantiations(
INSTANCES_NAME device_grouped_conv2d_fwd_bias_bn_clamp_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_tf32_instances
TEMPLATE_FILE xdl/large_tensor/device_grouped_conv2d_fwd_bias_bn_clamp_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_tf32_instance.in
NUM_SHARDS 2
SRC_LIST GROUPED_CONV2D_FWD_BIAS_BNORM_CLAMP
OUTPUT_DIR ${GENERATED_DIR}/xdl/large_tensor
)
# merged groups
# NHWGC, GKYXC, NHWGK
@@ -114,6 +124,15 @@ generate_sharded_instantiations(
SRC_LIST GROUPED_CONV2D_FWD_BIAS_BNORM_CLAMP
OUTPUT_DIR ${GENERATED_DIR}/xdl/merged_groups
)
set(GENERATED_DIR ${CMAKE_CURRENT_BINARY_DIR}/generated)
generate_sharded_instantiations(
INSTANCES_NAME device_grouped_conv2d_fwd_bias_bn_clamp_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_tf32_instances
TEMPLATE_FILE xdl/merged_groups/device_grouped_conv2d_fwd_bias_bn_clamp_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_tf32_instance.in
NUM_SHARDS 3
SRC_LIST GROUPED_CONV2D_FWD_BIAS_BNORM_CLAMP
OUTPUT_DIR ${GENERATED_DIR}/xdl/merged_groups
)
#mem
# NHWGC, GKYXC, NHWGK
@@ -143,6 +162,16 @@ generate_sharded_instantiations(
SRC_LIST GROUPED_CONV2D_FWD_BIAS_BNORM_CLAMP
OUTPUT_DIR ${GENERATED_DIR}/xdl/mem
)
set(GENERATED_DIR ${CMAKE_CURRENT_BINARY_DIR}/generated)
generate_sharded_instantiations(
INSTANCES_NAME device_grouped_conv2d_fwd_bias_bn_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_intra_instances
TEMPLATE_FILE xdl/mem/device_grouped_conv2d_fwd_bias_bn_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_intra_instance.in
NUM_SHARDS 16
SRC_LIST GROUPED_CONV2D_FWD_BIAS_BNORM_CLAMP
OUTPUT_DIR ${GENERATED_DIR}/xdl/mem
)
# NHWGC, GKYXC, NHWGK
set(GENERATED_DIR ${CMAKE_CURRENT_BINARY_DIR}/generated)
@@ -171,6 +200,16 @@ generate_sharded_instantiations(
SRC_LIST GROUPED_CONV2D_FWD_BIAS_BNORM_CLAMP
OUTPUT_DIR ${GENERATED_DIR}/xdl/mem
)
set(GENERATED_DIR ${CMAKE_CURRENT_BINARY_DIR}/generated)
generate_sharded_instantiations(
INSTANCES_NAME device_grouped_conv2d_fwd_bias_bn_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_inter_instances
TEMPLATE_FILE xdl/mem/device_grouped_conv2d_fwd_bias_bn_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_inter_instance.in
NUM_SHARDS 16
SRC_LIST GROUPED_CONV2D_FWD_BIAS_BNORM_CLAMP
OUTPUT_DIR ${GENERATED_DIR}/xdl/mem
)
#comp
# NHWGC, GKYXC, NHWGK
@@ -200,7 +239,16 @@ generate_sharded_instantiations(
SRC_LIST GROUPED_CONV2D_FWD_BIAS_BNORM_CLAMP
OUTPUT_DIR ${GENERATED_DIR}/xdl/comp
)
set(GENERATED_DIR ${CMAKE_CURRENT_BINARY_DIR}/generated)
generate_sharded_instantiations(
INSTANCES_NAME device_grouped_conv2d_fwd_bias_bn_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_comp_instances
TEMPLATE_FILE xdl/comp/device_grouped_conv2d_fwd_bias_bn_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_comp_instance.in
NUM_SHARDS 4
SRC_LIST GROUPED_CONV2D_FWD_BIAS_BNORM_CLAMP
OUTPUT_DIR ${GENERATED_DIR}/xdl/comp
)
set(GENERATED_DIR ${CMAKE_CURRENT_BINARY_DIR}/generated)
generate_sharded_instantiations(
INSTANCES_NAME device_grouped_conv2d_fwd_bias_bn_clamp_xdl_nhwgc_gkyxc_nhwgk_bf16_comp_2x_instances

82
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd_bias_bnorm_clamp/xdl/comp/device_grouped_conv2d_fwd_bias_bn_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_comp_instance.in Normal file
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@@ -0,0 +1,82 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_comp_instance.hpp"
#include "ck/utility/filter_tuple.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using device_grouped_conv2d_fwd_bias_bn_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_comp_instances =
std::vector<
std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<NHWGK, NHWGK, NHWGK, NHWGK, NHWGK>,
NHWGK,
F32,
F32,
Tuple<F32, F32, F32, F32, F32>,
F32,
PassThrough,
PassThrough,
BiasNormalizeInInferClamp,
TF32,
TF32>>>;
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
template <int Shards, int ShardIndex>
void add_device_grouped_conv2d_fwd_bias_bn_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_comp_instances_shard(
device_grouped_conv2d_fwd_bias_bn_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_comp_instances&
instances)
{
add_device_operation_instances(
instances,
ck::util::filter_tuple_by_modulo_t<device_grouped_conv_fwd_xdl_f32_tf32_comp_instances<
2,
NHWGC,
GKYXC,
Tuple<NHWGK, NHWGK, NHWGK, NHWGK, NHWGK>,
NHWGK,
ConvFwdDefault,
Tuple<F32, F32, F32, F32, F32>,
BiasNormalizeInInferClamp>,
Shards,
ShardIndex>{});
add_device_operation_instances(
instances,
ck::util::filter_tuple_by_modulo_t<device_grouped_conv_fwd_xdl_f32_tf32_comp_instances<
2,
NHWGC,
GKYXC,
Tuple<NHWGK, NHWGK, NHWGK, NHWGK, NHWGK>,
NHWGK,
ConvFwd1x1P0,
Tuple<F32, F32, F32, F32, F32>,
BiasNormalizeInInferClamp>,
Shards,
ShardIndex>{});
add_device_operation_instances(
instances,
ck::util::filter_tuple_by_modulo_t<device_grouped_conv_fwd_xdl_f32_tf32_comp_instances<
2,
NHWGC,
GKYXC,
Tuple<NHWGK, NHWGK, NHWGK, NHWGK, NHWGK>,
NHWGK,
ConvFwd1x1S1P0,
Tuple<F32, F32, F32, F32, F32>,
BiasNormalizeInInferClamp>,
Shards,
ShardIndex>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

54
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd_bias_bnorm_clamp/xdl/large_tensor/device_grouped_conv2d_fwd_bias_bn_clamp_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_tf32_instance.in Normal file
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@@ -0,0 +1,54 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_large_tensor_instance.hpp"
#include "ck/utility/filter_tuple.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using device_grouped_conv2d_fwd_bias_bn_clamp_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_tf32_instances =
std::vector<
std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<NHWGK, NHWGK, NHWGK, NHWGK, NHWGK>,
NHWGK,
F32,
F32,
Tuple<F32, F32, F32, F32, F32>,
F32,
PassThrough,
PassThrough,
BiasNormalizeInInferClamp,
TF32,
TF32>>>;
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
template <int Shards, int ShardIndex>
void add_device_grouped_conv2d_fwd_bias_bn_clamp_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_tf32_instances_shard(
device_grouped_conv2d_fwd_bias_bn_clamp_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_tf32_instances&
instances)
{
add_device_operation_instances(instances,
ck::util::filter_tuple_by_modulo_t<
device_grouped_conv_fwd_xdl_large_tensor_f32_tf32_instances<
2,
NHWGC,
GKYXC,
Tuple<NHWGK, NHWGK, NHWGK, NHWGK, NHWGK>,
NHWGK,
ConvFwdDefault,
Tuple<F32, F32, F32, F32, F32>,
BiasNormalizeInInferClamp>,
Shards,
ShardIndex>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

85
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd_bias_bnorm_clamp/xdl/mem/device_grouped_conv2d_fwd_bias_bn_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_inter_instance.in Normal file
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@@ -0,0 +1,85 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_mem_instance.hpp"
#include "ck/utility/filter_tuple.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using device_grouped_conv2d_fwd_bias_bn_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_inter_instances =
std::vector<
std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<NHWGK, NHWGK, NHWGK, NHWGK, NHWGK>,
NHWGK,
F32,
F32,
Tuple<F32, F32, F32, F32, F32>,
F32,
PassThrough,
PassThrough,
BiasNormalizeInInferClamp,
TF32,
TF32>>>;
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
template <int Shards, int ShardIndex>
void add_device_grouped_conv2d_fwd_bias_bn_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_inter_instances_shard(
device_grouped_conv2d_fwd_bias_bn_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_inter_instances&
instances)
{
add_device_operation_instances(
instances,
ck::util::filter_tuple_by_modulo_t<device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<
2,
NHWGC,
GKYXC,
Tuple<NHWGK, NHWGK, NHWGK, NHWGK, NHWGK>,
NHWGK,
ConvFwdDefault,
Interwave,
Tuple<F32, F32, F32, F32, F32>,
BiasNormalizeInInferClamp>,
Shards,
ShardIndex>{});
add_device_operation_instances(
instances,
ck::util::filter_tuple_by_modulo_t<device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<
2,
NHWGC,
GKYXC,
Tuple<NHWGK, NHWGK, NHWGK, NHWGK, NHWGK>,
NHWGK,
ConvFwd1x1P0,
Interwave,
Tuple<F32, F32, F32, F32, F32>,
BiasNormalizeInInferClamp>,
Shards,
ShardIndex>{});
add_device_operation_instances(
instances,
ck::util::filter_tuple_by_modulo_t<device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<
2,
NHWGC,
GKYXC,
Tuple<NHWGK, NHWGK, NHWGK, NHWGK, NHWGK>,
NHWGK,
ConvFwd1x1S1P0,
Interwave,
Tuple<F32, F32, F32, F32, F32>,
BiasNormalizeInInferClamp>,
Shards,
ShardIndex>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

85
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd_bias_bnorm_clamp/xdl/mem/device_grouped_conv2d_fwd_bias_bn_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_intra_instance.in Normal file
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@@ -0,0 +1,85 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_mem_instance.hpp"
#include "ck/utility/filter_tuple.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using device_grouped_conv2d_fwd_bias_bn_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_intra_instances =
std::vector<
std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<NHWGK, NHWGK, NHWGK, NHWGK, NHWGK>,
NHWGK,
F32,
F32,
Tuple<F32, F32, F32, F32, F32>,
F32,
PassThrough,
PassThrough,
BiasNormalizeInInferClamp,
TF32,
TF32>>>;
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
template <int Shards, int ShardIndex>
void add_device_grouped_conv2d_fwd_bias_bn_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_intra_instances_shard(
device_grouped_conv2d_fwd_bias_bn_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_intra_instances&
instances)
{
add_device_operation_instances(
instances,
ck::util::filter_tuple_by_modulo_t<device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<
2,
NHWGC,
GKYXC,
Tuple<NHWGK, NHWGK, NHWGK, NHWGK, NHWGK>,
NHWGK,
ConvFwdDefault,
Intrawave,
Tuple<F32, F32, F32, F32, F32>,
BiasNormalizeInInferClamp>,
Shards,
ShardIndex>{});
add_device_operation_instances(
instances,
ck::util::filter_tuple_by_modulo_t<device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<
2,
NHWGC,
GKYXC,
Tuple<NHWGK, NHWGK, NHWGK, NHWGK, NHWGK>,
NHWGK,
ConvFwd1x1P0,
Intrawave,
Tuple<F32, F32, F32, F32, F32>,
BiasNormalizeInInferClamp>,
Shards,
ShardIndex>{});
add_device_operation_instances(
instances,
ck::util::filter_tuple_by_modulo_t<device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<
2,
NHWGC,
GKYXC,
Tuple<NHWGK, NHWGK, NHWGK, NHWGK, NHWGK>,
NHWGK,
ConvFwd1x1S1P0,
Intrawave,
Tuple<F32, F32, F32, F32, F32>,
BiasNormalizeInInferClamp>,
Shards,
ShardIndex>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

68
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd_bias_bnorm_clamp/xdl/merged_groups/device_grouped_conv2d_fwd_bias_bn_clamp_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_tf32_instance.in Normal file
View File

@@ -0,0 +1,68 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_merged_groups_instance.hpp"
#include "ck/utility/filter_tuple.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using device_grouped_conv2d_fwd_bias_bn_clamp_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_tf32_instances =
std::vector<
std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<NHWGK, NHWGK, NHWGK, NHWGK, NHWGK>,
NHWGK,
F32,
F32,
Tuple<F32, F32, F32, F32, F32>,
F32,
PassThrough,
PassThrough,
BiasNormalizeInInferClamp,
TF32,
TF32>>>;
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
template <int Shards, int ShardIndex>
void add_device_grouped_conv2d_fwd_bias_bn_clamp_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_tf32_instances_shard(
device_grouped_conv2d_fwd_bias_bn_clamp_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_tf32_instances&
instances)
{
add_device_operation_instances(instances,
ck::util::filter_tuple_by_modulo_t<
device_grouped_conv_fwd_xdl_merged_groups_f32_tf32_instances<
2,
NHWGC,
GKYXC,
Tuple<NHWGK, NHWGK, NHWGK, NHWGK, NHWGK>,
NHWGK,
ConvFwdDefault,
Tuple<F32, F32, F32, F32, F32>,
BiasNormalizeInInferClamp>,
Shards,
ShardIndex>{});
add_device_operation_instances(instances,
ck::util::filter_tuple_by_modulo_t<
device_grouped_conv_fwd_xdl_merged_groups_f32_tf32_instances<
2,
NHWGC,
GKYXC,
Tuple<NHWGK, NHWGK, NHWGK, NHWGK, NHWGK>,
NHWGK,
ConvFwd3x3,
Tuple<F32, F32, F32, F32, F32>,
BiasNormalizeInInferClamp>,
Shards,
ShardIndex>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

6
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd_bias_clamp/CMakeLists.txt
View File

@@ -21,10 +21,16 @@ add_instance_library(device_grouped_conv2d_fwd_bias_clamp_instance
xdl/comp/device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_fp16_comp_part2_instance.cpp
xdl/device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_fp32_instance.cpp
xdl/device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_fp32_tf32_instance.cpp
xdl/device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_fp32_16x16_instance.cpp
xdl/large_tensor/device_grouped_conv2d_fwd_bias_clamp_xdl_large_tensor_nhwgc_gkyxc_nhwgk_fp32_instance.cpp
xdl/large_tensor/device_grouped_conv2d_fwd_bias_clamp_xdl_large_tensor_nhwgc_gkyxc_nhwgk_fp32_tf32_instance.cpp
xdl/merged_groups/device_grouped_conv2d_fwd_bias_clamp_xdl_merged_groups_nhwgc_gkyxc_nhwgk_fp32_instance.cpp
xdl/merged_groups/device_grouped_conv2d_fwd_bias_clamp_xdl_merged_groups_nhwgc_gkyxc_nhwgk_fp32_tf32_instance.cpp
xdl/mem/device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_fp32_mem_intra_instance.cpp
xdl/mem/device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_fp32_tf32_mem_intra_instance.cpp
xdl/mem/device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_fp32_mem_inter_instance.cpp
xdl/mem/device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_fp32_tf32_mem_inter_instance.cpp
xdl/comp/device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_fp32_comp_instance.cpp
xdl/comp/device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_fp32_tf32_comp_instance.cpp
)

65
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd_bias_clamp/xdl/comp/device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_fp32_tf32_comp_instance.cpp Normal file
View File

@@ -0,0 +1,65 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_comp_instance.hpp"
#include "ck/host_utility/device_prop.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_comp_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
F32,
F32,
Tuple<F32>,
F32,
PassThrough,
PassThrough,
AddClamp,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_comp_instances<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
ConvFwdDefault,
Tuple<F32>,
AddClamp>{});
add_device_operation_instances(instances,
device_grouped_conv_fwd_xdl_f32_tf32_comp_instances<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
ConvFwd1x1P0,
Tuple<F32>,
AddClamp>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_comp_instances<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
ConvFwd1x1S1P0,
Tuple<F32>,
AddClamp>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

62
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd_bias_clamp/xdl/device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_fp32_tf32_instance.cpp Normal file
View File

@@ -0,0 +1,62 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
F32,
F32,
Tuple<F32>,
F32,
PassThrough,
PassThrough,
AddClamp,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(instances,
device_grouped_conv_fwd_xdl_f32_tf32_instances<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
ConvFwdDefault,
Tuple<F32>,
AddClamp>{});
add_device_operation_instances(instances,
device_grouped_conv_fwd_xdl_f32_tf32_instances<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
ConvFwd1x1P0,
Tuple<F32>,
AddClamp>{});
add_device_operation_instances(instances,
device_grouped_conv_fwd_xdl_f32_tf32_instances<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
ConvFwd1x1S1P0,
Tuple<F32>,
AddClamp>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

43
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd_bias_clamp/xdl/large_tensor/device_grouped_conv2d_fwd_bias_clamp_xdl_large_tensor_nhwgc_gkyxc_nhwgk_fp32_tf32_instance.cpp Normal file
View File

@@ -0,0 +1,43 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_large_tensor_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv2d_fwd_bias_clamp_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
F32,
F32,
Tuple<F32>,
F32,
PassThrough,
PassThrough,
AddClamp,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_large_tensor_f32_tf32_instances<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
ConvFwdDefault,
Tuple<F32>,
AddClamp>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

67
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd_bias_clamp/xdl/mem/device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_fp32_tf32_mem_inter_instance.cpp Normal file
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@@ -0,0 +1,67 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_mem_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_inter_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
F32,
F32,
Tuple<F32>,
F32,
PassThrough,
PassThrough,
AddClamp,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
ConvFwdDefault,
Interwave,
Tuple<F32>,
AddClamp>{});
add_device_operation_instances(instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
ConvFwd1x1P0,
Interwave,
Tuple<F32>,
AddClamp>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
ConvFwd1x1S1P0,
Interwave,
Tuple<F32>,
AddClamp>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

67
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd_bias_clamp/xdl/mem/device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_fp32_tf32_mem_intra_instance.cpp Normal file
View File

@@ -0,0 +1,67 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_mem_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv2d_fwd_bias_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_intra_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
F32,
F32,
Tuple<F32>,
F32,
PassThrough,
PassThrough,
AddClamp,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
ConvFwdDefault,
Intrawave,
Tuple<F32>,
AddClamp>{});
add_device_operation_instances(instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
ConvFwd1x1P0,
Intrawave,
Tuple<F32>,
AddClamp>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
ConvFwd1x1S1P0,
Intrawave,
Tuple<F32>,
AddClamp>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

56
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd_bias_clamp/xdl/merged_groups/device_grouped_conv2d_fwd_bias_clamp_xdl_merged_groups_nhwgc_gkyxc_nhwgk_fp32_tf32_instance.cpp Normal file
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@@ -0,0 +1,56 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_merged_groups_instance.hpp"
#include "ck/host_utility/device_prop.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv2d_fwd_bias_clamp_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
F32,
F32,
Tuple<F32>,
F32,
PassThrough,
PassThrough,
AddClamp,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_merged_groups_f32_tf32_instances<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
ConvFwdDefault,
Tuple<F32>,
AddClamp>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_merged_groups_f32_tf32_instances<2,
NHWGC,
GKYXC,
Tuple<NHWGK>,
NHWGK,
ConvFwd3x3,
Tuple<F32>,
AddClamp>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

6
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd_clamp/CMakeLists.txt
View File

@@ -21,10 +21,16 @@ add_instance_library(device_grouped_conv2d_fwd_clamp_instance
xdl/comp/device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_fp16_comp_part2_instance.cpp
xdl/device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_fp32_instance.cpp
xdl/device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_fp32_tf32_instance.cpp
xdl/device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_fp32_16x16_instance.cpp
xdl/large_tensor/device_grouped_conv2d_fwd_clamp_xdl_large_tensor_nhwgc_gkyxc_nhwgk_fp32_instance.cpp
xdl/large_tensor/device_grouped_conv2d_fwd_clamp_xdl_large_tensor_nhwgc_gkyxc_nhwgk_fp32_tf32_instance.cpp
xdl/merged_groups/device_grouped_conv2d_fwd_clamp_xdl_merged_groups_nhwgc_gkyxc_nhwgk_fp32_instance.cpp
xdl/merged_groups/device_grouped_conv2d_fwd_clamp_xdl_merged_groups_nhwgc_gkyxc_nhwgk_fp32_tf32_instance.cpp
xdl/mem/device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_fp32_mem_intra_instance.cpp
xdl/mem/device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_fp32_tf32_mem_intra_instance.cpp
xdl/mem/device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_fp32_mem_inter_instance.cpp
xdl/mem/device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_fp32_tf32_mem_inter_instance.cpp
xdl/comp/device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_fp32_comp_instance.cpp
xdl/comp/device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_fp32_tf32_comp_instance.cpp
)

65
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd_clamp/xdl/comp/device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_fp32_tf32_comp_instance.cpp Normal file
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@@ -0,0 +1,65 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_comp_instance.hpp"
#include "ck/host_utility/device_prop.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_comp_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
F32,
F32,
Tuple<>,
F32,
PassThrough,
PassThrough,
Clamp,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_comp_instances<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
ConvFwdDefault,
Tuple<>,
Clamp>{});
add_device_operation_instances(instances,
device_grouped_conv_fwd_xdl_f32_tf32_comp_instances<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
ConvFwd1x1P0,
Tuple<>,
Clamp>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_comp_instances<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
ConvFwd1x1S1P0,
Tuple<>,
Clamp>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

62
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd_clamp/xdl/device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_fp32_tf32_instance.cpp Normal file
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@@ -0,0 +1,62 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
F32,
F32,
Tuple<>,
F32,
PassThrough,
PassThrough,
Clamp,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(instances,
device_grouped_conv_fwd_xdl_f32_tf32_instances<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
ConvFwdDefault,
Tuple<>,
Clamp>{});
add_device_operation_instances(instances,
device_grouped_conv_fwd_xdl_f32_tf32_instances<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
ConvFwd1x1P0,
Tuple<>,
Clamp>{});
add_device_operation_instances(instances,
device_grouped_conv_fwd_xdl_f32_tf32_instances<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
ConvFwd1x1S1P0,
Tuple<>,
Clamp>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

43
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd_clamp/xdl/large_tensor/device_grouped_conv2d_fwd_clamp_xdl_large_tensor_nhwgc_gkyxc_nhwgk_fp32_tf32_instance.cpp Normal file
View File

@@ -0,0 +1,43 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_large_tensor_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv2d_fwd_clamp_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
F32,
F32,
Tuple<>,
F32,
PassThrough,
PassThrough,
Clamp,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_large_tensor_f32_tf32_instances<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
ConvFwdDefault,
Tuple<>,
Clamp>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

67
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd_clamp/xdl/mem/device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_fp32_tf32_mem_inter_instance.cpp Normal file
View File

@@ -0,0 +1,67 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_mem_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_inter_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
F32,
F32,
Tuple<>,
F32,
PassThrough,
PassThrough,
Clamp,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
ConvFwdDefault,
Interwave,
Tuple<>,
Clamp>{});
add_device_operation_instances(instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
ConvFwd1x1P0,
Interwave,
Tuple<>,
Clamp>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
ConvFwd1x1S1P0,
Interwave,
Tuple<>,
Clamp>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

67
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd_clamp/xdl/mem/device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_fp32_tf32_mem_intra_instance.cpp Normal file
View File

@@ -0,0 +1,67 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_mem_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv2d_fwd_clamp_xdl_nhwgc_gkyxc_nhwgk_f32_tf32_mem_intra_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
F32,
F32,
Tuple<>,
F32,
PassThrough,
PassThrough,
Clamp,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
ConvFwdDefault,
Intrawave,
Tuple<>,
Clamp>{});
add_device_operation_instances(instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
ConvFwd1x1P0,
Intrawave,
Tuple<>,
Clamp>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
ConvFwd1x1S1P0,
Intrawave,
Tuple<>,
Clamp>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

55
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd_clamp/xdl/merged_groups/device_grouped_conv2d_fwd_clamp_xdl_merged_groups_nhwgc_gkyxc_nhwgk_fp32_tf32_instance.cpp Normal file
View File

@@ -0,0 +1,55 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_merged_groups_instance.hpp"
#include "ck/host_utility/device_prop.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv2d_fwd_clamp_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
F32,
F32,
Tuple<>,
F32,
PassThrough,
PassThrough,
Clamp,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_merged_groups_f32_tf32_instances<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
ConvFwdDefault,
Tuple<>,
Clamp>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_merged_groups_f32_tf32_instances<2,
NHWGC,
GKYXC,
Tuple<>,
NHWGK,
ConvFwd3x3,
Tuple<>,
Clamp>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

9
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd/CMakeLists.txt
View File

@@ -20,10 +20,12 @@ set(GROUPED_CONV3D_FWD
xdl/large_tensor/device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_bf16_instance.cpp
xdl/large_tensor/device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f16_instance.cpp
xdl/large_tensor/device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_instance.cpp
xdl/large_tensor/device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instance.cpp
xdl/merged_groups/device_grouped_conv3d_fwd_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_bf16_instance.cpp
xdl/merged_groups/device_grouped_conv3d_fwd_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f16_instance.cpp
xdl/merged_groups/device_grouped_conv3d_fwd_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_instance.cpp
xdl/merged_groups/device_grouped_conv3d_fwd_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instance.cpp
xdl/merged_groups/device_grouped_conv3d_fwd_xdl_merged_groups_ngcdhw_gkczyx_ngkdhw_bf16_instance.cpp
xdl/merged_groups/device_grouped_conv3d_fwd_xdl_merged_groups_ngcdhw_gkczyx_ngkdhw_f16_instance.cpp
xdl/merged_groups/device_grouped_conv3d_fwd_xdl_merged_groups_ngcdhw_gkczyx_ngkdhw_f32_instance.cpp
@@ -31,13 +33,16 @@ set(GROUPED_CONV3D_FWD
xdl/mem/device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_mem_inter_instance.cpp
xdl/mem/device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f16_mem_inter_instance.cpp
xdl/mem/device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_mem_inter_instance.cpp
xdl/mem/device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_inter_instance.cpp
xdl/mem/device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_mem_intra_instance.cpp
xdl/mem/device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f16_mem_intra_instance.cpp
xdl/mem/device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_mem_intra_instance.cpp
xdl/mem/device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_intra_instance.cpp
xdl/comp/device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_comp_instance.cpp
xdl/comp/device_grouped_conv3d_fwd_xdl_ngcdhw_gkczyx_ngkdhw_f32_comp_instance.cpp
xdl/comp/device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_comp_instance.cpp
xdl/comp/device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_comp_instance.cpp
xdl/comp/device_grouped_conv3d_fwd_xdl_ngcdhw_gkczyx_ngkdhw_f32_comp_instance.cpp
xdl/comp/device_grouped_conv3d_fwd_xdl_ngcdhw_gkczyx_ngkdhw_bf16_comp_2x_instance.cpp
xdl/comp/device_grouped_conv3d_fwd_xdl_ngcdhw_gkczyx_ngkdhw_f16_comp_2x_instance.cpp
xdl/comp/device_grouped_conv3d_fwd_xdl_ngcdhw_gkczyx_ngkdhw_bf16_comp_part2_instance.cpp

57
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd/xdl/comp/device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_comp_instance.cpp Normal file
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@@ -0,0 +1,57 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_comp_instance.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_comp_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_comp_instances<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
ConvFwdDefault>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_comp_instances<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
ConvFwd1x1P0>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_comp_instances<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
ConvFwd1x1S1P0>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

41
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd/xdl/large_tensor/device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instance.cpp Normal file
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@@ -0,0 +1,41 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_large_tensor_instance.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_large_tensor_f32_tf32_instances<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
ConvFwdDefault>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

59
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd/xdl/mem/device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_inter_instance.cpp Normal file
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@@ -0,0 +1,59 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_mem_instance.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_inter_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
ConvFwdDefault,
Interwave>{});
add_device_operation_instances(instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
ConvFwd1x1P0,
Interwave>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
ConvFwd1x1S1P0,
Interwave>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

59
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd/xdl/mem/device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_intra_instance.cpp Normal file
View File

@@ -0,0 +1,59 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_mem_instance.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_intra_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
ConvFwdDefault,
Intrawave>{});
add_device_operation_instances(instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
ConvFwd1x1P0,
Intrawave>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
ConvFwd1x1S1P0,
Intrawave>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

49
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd/xdl/merged_groups/device_grouped_conv3d_fwd_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instance.cpp Normal file
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@@ -0,0 +1,49 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_merged_groups_instance.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_grouped_conv3d_fwd_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_merged_groups_f32_tf32_instances<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
ConvFwdDefault>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_merged_groups_f32_tf32_instances<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
ConvFwd3x3>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

47
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_bias_bnorm_clamp/CMakeLists.txt
View File

@@ -94,6 +94,16 @@ generate_sharded_instantiations(
SRC_LIST GROUPED_conv3d_FWD_BIAS_BNORM_CLAMP
OUTPUT_DIR ${GENERATED_DIR}/xdl/large_tensor
)
set(GENERATED_DIR ${CMAKE_CURRENT_BINARY_DIR}/generated)
generate_sharded_instantiations(
INSTANCES_NAME device_grouped_conv3d_fwd_bias_bn_clamp_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances
TEMPLATE_FILE xdl/large_tensor/device_grouped_conv3d_fwd_bias_bn_clamp_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instance.in
NUM_SHARDS 2
SRC_LIST GROUPED_conv3d_FWD_BIAS_BNORM_CLAMP
OUTPUT_DIR ${GENERATED_DIR}/xdl/large_tensor
)
# merged groups
# NDHWGC, GKZYXC, NDHWGK
@@ -123,6 +133,15 @@ generate_sharded_instantiations(
SRC_LIST GROUPED_conv3d_FWD_BIAS_BNORM_CLAMP
OUTPUT_DIR ${GENERATED_DIR}/xdl/merged_groups
)
set(GENERATED_DIR ${CMAKE_CURRENT_BINARY_DIR}/generated)
generate_sharded_instantiations(
INSTANCES_NAME device_grouped_conv3d_fwd_bias_bn_clamp_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances
TEMPLATE_FILE xdl/merged_groups/device_grouped_conv3d_fwd_bias_bn_clamp_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instance.in
NUM_SHARDS 3
SRC_LIST GROUPED_conv3d_FWD_BIAS_BNORM_CLAMP
OUTPUT_DIR ${GENERATED_DIR}/xdl/merged_groups
)
#mem
# NDHWGC, GKZYXC, NDHWGK
@@ -154,6 +173,15 @@ generate_sharded_instantiations(
)
# NDHWGC, GKZYXC, NDHWGK
set(GENERATED_DIR ${CMAKE_CURRENT_BINARY_DIR}/generated)
generate_sharded_instantiations(
INSTANCES_NAME device_grouped_conv3d_fwd_bias_bn_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_intra_instances
TEMPLATE_FILE xdl/mem/device_grouped_conv3d_fwd_bias_bn_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_intra_instance.in
NUM_SHARDS 16
SRC_LIST GROUPED_conv3d_FWD_BIAS_BNORM_CLAMP
OUTPUT_DIR ${GENERATED_DIR}/xdl/mem
)
set(GENERATED_DIR ${CMAKE_CURRENT_BINARY_DIR}/generated)
generate_sharded_instantiations(
INSTANCES_NAME device_grouped_conv3d_fwd_bias_bn_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_mem_inter_instances
@@ -180,6 +208,16 @@ generate_sharded_instantiations(
SRC_LIST GROUPED_conv3d_FWD_BIAS_BNORM_CLAMP
OUTPUT_DIR ${GENERATED_DIR}/xdl/mem
)
set(GENERATED_DIR ${CMAKE_CURRENT_BINARY_DIR}/generated)
generate_sharded_instantiations(
INSTANCES_NAME device_grouped_conv3d_fwd_bias_bn_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_inter_instances
TEMPLATE_FILE xdl/mem/device_grouped_conv3d_fwd_bias_bn_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_inter_instance.in
NUM_SHARDS 16
SRC_LIST GROUPED_conv3d_FWD_BIAS_BNORM_CLAMP
OUTPUT_DIR ${GENERATED_DIR}/xdl/mem
)
#comp
# NDHWGC, GKZYXC, NDHWGK
@@ -210,6 +248,15 @@ generate_sharded_instantiations(
OUTPUT_DIR ${GENERATED_DIR}/xdl/comp
)
set(GENERATED_DIR ${CMAKE_CURRENT_BINARY_DIR}/generated)
generate_sharded_instantiations(
INSTANCES_NAME device_grouped_conv3d_fwd_bias_bn_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_comp_instances
TEMPLATE_FILE xdl/comp/device_grouped_conv3d_fwd_bias_bn_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_comp_instance.in
NUM_SHARDS 4
SRC_LIST GROUPED_conv3d_FWD_BIAS_BNORM_CLAMP
OUTPUT_DIR ${GENERATED_DIR}/xdl/comp
)
set(GENERATED_DIR ${CMAKE_CURRENT_BINARY_DIR}/generated)
generate_sharded_instantiations(
INSTANCES_NAME device_grouped_conv3d_fwd_bias_bn_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_comp_2x_instances

82
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_bias_bnorm_clamp/xdl/comp/device_grouped_conv3d_fwd_bias_bn_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_comp_instance.in Normal file
View File

@@ -0,0 +1,82 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_comp_instance.hpp"
#include "ck/utility/filter_tuple.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using device_grouped_conv3d_fwd_bias_bn_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_comp_instances =
std::vector<std::unique_ptr<
DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK, NDHWGK, NDHWGK, NDHWGK, NDHWGK>,
NDHWGK,
F32,
F32,
Tuple<F32, F32, F32, F32, F32>,
F32,
PassThrough,
PassThrough,
BiasNormalizeInInferClamp,
TF32,
TF32>>>;
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
template <int Shards, int ShardIndex>
void add_device_grouped_conv3d_fwd_bias_bn_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_comp_instances_shard(
device_grouped_conv3d_fwd_bias_bn_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_comp_instances&
instances)
{
add_device_operation_instances(
instances,
ck::util::filter_tuple_by_modulo_t<device_grouped_conv_fwd_xdl_f32_tf32_comp_instances<
3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK, NDHWGK, NDHWGK, NDHWGK, NDHWGK>,
NDHWGK,
ConvFwdDefault,
Tuple<F32, F32, F32, F32, F32>,
BiasNormalizeInInferClamp>,
Shards,
ShardIndex>{});
add_device_operation_instances(
instances,
ck::util::filter_tuple_by_modulo_t<device_grouped_conv_fwd_xdl_f32_tf32_comp_instances<
3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK, NDHWGK, NDHWGK, NDHWGK, NDHWGK>,
NDHWGK,
ConvFwd1x1P0,
Tuple<F32, F32, F32, F32, F32>,
BiasNormalizeInInferClamp>,
Shards,
ShardIndex>{});
add_device_operation_instances(
instances,
ck::util::filter_tuple_by_modulo_t<device_grouped_conv_fwd_xdl_f32_tf32_comp_instances<
3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK, NDHWGK, NDHWGK, NDHWGK, NDHWGK>,
NDHWGK,
ConvFwd1x1S1P0,
Tuple<F32, F32, F32, F32, F32>,
BiasNormalizeInInferClamp>,
Shards,
ShardIndex>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

54
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_bias_bnorm_clamp/xdl/large_tensor/device_grouped_conv3d_fwd_bias_bn_clamp_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instance.in Normal file
View File

@@ -0,0 +1,54 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_large_tensor_instance.hpp"
#include "ck/utility/filter_tuple.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using device_grouped_conv3d_fwd_bias_bn_clamp_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances =
std::vector<std::unique_ptr<
DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK, NDHWGK, NDHWGK, NDHWGK, NDHWGK>,
NDHWGK,
F32,
F32,
Tuple<F32, F32, F32, F32, F32>,
F32,
PassThrough,
PassThrough,
BiasNormalizeInInferClamp,
TF32,
TF32>>>;
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
template <int Shards, int ShardIndex>
void add_device_grouped_conv3d_fwd_bias_bn_clamp_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances_shard(
device_grouped_conv3d_fwd_bias_bn_clamp_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances&
instances)
{
add_device_operation_instances(instances,
ck::util::filter_tuple_by_modulo_t<
device_grouped_conv_fwd_xdl_large_tensor_f32_tf32_instances<
3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK, NDHWGK, NDHWGK, NDHWGK, NDHWGK>,
NDHWGK,
ConvFwdDefault,
Tuple<F32, F32, F32, F32, F32>,
BiasNormalizeInInferClamp>,
Shards,
ShardIndex>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

85
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_bias_bnorm_clamp/xdl/mem/device_grouped_conv3d_fwd_bias_bn_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_inter_instance.in Normal file
View File

@@ -0,0 +1,85 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_mem_instance.hpp"
#include "ck/utility/filter_tuple.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using device_grouped_conv3d_fwd_bias_bn_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_inter_instances =
std::vector<std::unique_ptr<
DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK, NDHWGK, NDHWGK, NDHWGK, NDHWGK>,
NDHWGK,
F32,
F32,
Tuple<F32, F32, F32, F32, F32>,
F32,
PassThrough,
PassThrough,
BiasNormalizeInInferClamp,
TF32,
TF32>>>;
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
template <int Shards, int ShardIndex>
void add_device_grouped_conv3d_fwd_bias_bn_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_inter_instances_shard(
device_grouped_conv3d_fwd_bias_bn_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_inter_instances&
instances)
{
add_device_operation_instances(
instances,
ck::util::filter_tuple_by_modulo_t<device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<
3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK, NDHWGK, NDHWGK, NDHWGK, NDHWGK>,
NDHWGK,
ConvFwdDefault,
Interwave,
Tuple<F32, F32, F32, F32, F32>,
BiasNormalizeInInferClamp>,
Shards,
ShardIndex>{});
add_device_operation_instances(
instances,
ck::util::filter_tuple_by_modulo_t<device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<
3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK, NDHWGK, NDHWGK, NDHWGK, NDHWGK>,
NDHWGK,
ConvFwd1x1P0,
Interwave,
Tuple<F32, F32, F32, F32, F32>,
BiasNormalizeInInferClamp>,
Shards,
ShardIndex>{});
add_device_operation_instances(
instances,
ck::util::filter_tuple_by_modulo_t<device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<
3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK, NDHWGK, NDHWGK, NDHWGK, NDHWGK>,
NDHWGK,
ConvFwd1x1S1P0,
Interwave,
Tuple<F32, F32, F32, F32, F32>,
BiasNormalizeInInferClamp>,
Shards,
ShardIndex>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

85
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_bias_bnorm_clamp/xdl/mem/device_grouped_conv3d_fwd_bias_bn_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_intra_instance.in Normal file
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@@ -0,0 +1,85 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_mem_instance.hpp"
#include "ck/utility/filter_tuple.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using device_grouped_conv3d_fwd_bias_bn_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_intra_instances =
std::vector<std::unique_ptr<
DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK, NDHWGK, NDHWGK, NDHWGK, NDHWGK>,
NDHWGK,
F32,
F32,
Tuple<F32, F32, F32, F32, F32>,
F32,
PassThrough,
PassThrough,
BiasNormalizeInInferClamp,
TF32,
TF32>>>;
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
template <int Shards, int ShardIndex>
void add_device_grouped_conv3d_fwd_bias_bn_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_intra_instances_shard(
device_grouped_conv3d_fwd_bias_bn_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_intra_instances&
instances)
{
add_device_operation_instances(
instances,
ck::util::filter_tuple_by_modulo_t<device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<
3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK, NDHWGK, NDHWGK, NDHWGK, NDHWGK>,
NDHWGK,
ConvFwdDefault,
Intrawave,
Tuple<F32, F32, F32, F32, F32>,
BiasNormalizeInInferClamp>,
Shards,
ShardIndex>{});
add_device_operation_instances(
instances,
ck::util::filter_tuple_by_modulo_t<device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<
3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK, NDHWGK, NDHWGK, NDHWGK, NDHWGK>,
NDHWGK,
ConvFwd1x1P0,
Intrawave,
Tuple<F32, F32, F32, F32, F32>,
BiasNormalizeInInferClamp>,
Shards,
ShardIndex>{});
add_device_operation_instances(
instances,
ck::util::filter_tuple_by_modulo_t<device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<
3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK, NDHWGK, NDHWGK, NDHWGK, NDHWGK>,
NDHWGK,
ConvFwd1x1S1P0,
Intrawave,
Tuple<F32, F32, F32, F32, F32>,
BiasNormalizeInInferClamp>,
Shards,
ShardIndex>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

68
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_bias_bnorm_clamp/xdl/merged_groups/device_grouped_conv3d_fwd_bias_bn_clamp_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instance.in Normal file
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@@ -0,0 +1,68 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_merged_groups_instance.hpp"
#include "ck/utility/filter_tuple.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using device_grouped_conv3d_fwd_bias_bn_clamp_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances =
std::vector<std::unique_ptr<
DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK, NDHWGK, NDHWGK, NDHWGK, NDHWGK>,
NDHWGK,
F32,
F32,
Tuple<F32, F32, F32, F32, F32>,
F32,
PassThrough,
PassThrough,
BiasNormalizeInInferClamp,
TF32,
TF32>>>;
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
template <int Shards, int ShardIndex>
void add_device_grouped_conv3d_fwd_bias_bn_clamp_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances_shard(
device_grouped_conv3d_fwd_bias_bn_clamp_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances&
instances)
{
add_device_operation_instances(instances,
ck::util::filter_tuple_by_modulo_t<
device_grouped_conv_fwd_xdl_merged_groups_f32_tf32_instances<
3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK, NDHWGK, NDHWGK, NDHWGK, NDHWGK>,
NDHWGK,
ConvFwdDefault,
Tuple<F32, F32, F32, F32, F32>,
BiasNormalizeInInferClamp>,
Shards,
ShardIndex>{});
add_device_operation_instances(instances,
ck::util::filter_tuple_by_modulo_t<
device_grouped_conv_fwd_xdl_merged_groups_f32_tf32_instances<
3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK, NDHWGK, NDHWGK, NDHWGK, NDHWGK>,
NDHWGK,
ConvFwd3x3,
Tuple<F32, F32, F32, F32, F32>,
BiasNormalizeInInferClamp>,
Shards,
ShardIndex>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

5
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_bias_clamp/CMakeLists.txt
View File

@@ -19,10 +19,15 @@ set(GROUPED_CONV3D_FWD
xdl/device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_fp32_instance.cpp
xdl/device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_fp32_16x16_instance.cpp
xdl/large_tensor/device_grouped_conv3d_fwd_bias_clamp_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_fp32_instance.cpp
xdl/large_tensor/device_grouped_conv3d_fwd_bias_clamp_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_fp32_tf32_instance.cpp
xdl/merged_groups/device_grouped_conv3d_fwd_bias_clamp_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_fp32_instance.cpp
xdl/merged_groups/device_grouped_conv3d_fwd_bias_clamp_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_fp32_tf32_instance.cpp
xdl/mem/device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_fp32_mem_inter_instance.cpp
xdl/mem/device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_fp32_tf32_mem_inter_instance.cpp
xdl/mem/device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_fp32_mem_intra_instance.cpp
xdl/mem/device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_fp32_tf32_mem_intra_instance.cpp
xdl/comp/device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_fp32_comp_instance.cpp
xdl/comp/device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_fp32_tf32_comp_instance.cpp
xdl/device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_fp32_tf32_instance.cpp
)

64
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_bias_clamp/xdl/comp/device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_fp32_tf32_comp_instance.cpp Normal file
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@@ -0,0 +1,64 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_comp_instance.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/host_utility/device_prop.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_comp_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK>,
NDHWGK,
F32,
F32,
Tuple<F32>,
F32,
PassThrough,
PassThrough,
AddClamp,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_comp_instances<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK>,
NDHWGK,
ConvFwdDefault,
Tuple<F32>,
AddClamp>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_comp_instances<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK>,
NDHWGK,
ConvFwd1x1P0,
Tuple<F32>,
AddClamp>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_comp_instances<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK>,
NDHWGK,
ConvFwd1x1S1P0,
Tuple<F32>,
AddClamp>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

43
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_bias_clamp/xdl/large_tensor/device_grouped_conv3d_fwd_bias_clamp_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_fp32_tf32_instance.cpp Normal file
View File

@@ -0,0 +1,43 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_large_tensor_instance.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_grouped_conv3d_fwd_bias_clamp_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK>,
NDHWGK,
F32,
F32,
Tuple<F32>,
F32,
PassThrough,
PassThrough,
AddClamp,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_large_tensor_f32_tf32_instances<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK>,
NDHWGK,
ConvFwdDefault,
Tuple<F32>,
AddClamp>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

65
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_bias_clamp/xdl/mem/device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_fp32_tf32_mem_inter_instance.cpp Normal file
View File

@@ -0,0 +1,65 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_mem_instance.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_inter_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK>,
NDHWGK,
F32,
F32,
Tuple<F32>,
F32,
PassThrough,
PassThrough,
AddClamp,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK>,
NDHWGK,
ConvFwdDefault,
Interwave,
Tuple<F32>,
AddClamp>{});
add_device_operation_instances(instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK>,
NDHWGK,
ConvFwd1x1P0,
Interwave,
Tuple<F32>,
AddClamp>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK>,
NDHWGK,
ConvFwd1x1S1P0,
Interwave,
Tuple<F32>,
AddClamp>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

65
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_bias_clamp/xdl/mem/device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_fp32_tf32_mem_intra_instance.cpp Normal file
View File

@@ -0,0 +1,65 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_mem_instance.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_grouped_conv3d_fwd_bias_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_intra_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK>,
NDHWGK,
F32,
F32,
Tuple<F32>,
F32,
PassThrough,
PassThrough,
AddClamp,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK>,
NDHWGK,
ConvFwdDefault,
Intrawave,
Tuple<F32>,
AddClamp>{});
add_device_operation_instances(instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK>,
NDHWGK,
ConvFwd1x1P0,
Intrawave,
Tuple<F32>,
AddClamp>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK>,
NDHWGK,
ConvFwd1x1S1P0,
Intrawave,
Tuple<F32>,
AddClamp>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

53
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_bias_clamp/xdl/merged_groups/device_grouped_conv3d_fwd_bias_clamp_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_fp32_tf32_instance.cpp Normal file
View File

@@ -0,0 +1,53 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_merged_groups_instance.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_grouped_conv3d_fwd_bias_clamp_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK>,
NDHWGK,
F32,
F32,
Tuple<F32>,
F32,
PassThrough,
PassThrough,
AddClamp,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_merged_groups_f32_tf32_instances<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK>,
NDHWGK,
ConvFwdDefault,
Tuple<F32>,
AddClamp>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_merged_groups_f32_tf32_instances<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK>,
NDHWGK,
ConvFwd3x3,
Tuple<F32>,
AddClamp>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

1
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_bilinear/CMakeLists.txt
View File

@@ -3,6 +3,7 @@ set(GROUPED_CONV3D_FWD_BILINEAR
xdl/device_grouped_conv3d_fwd_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_bf16_instance.cpp
xdl/device_grouped_conv3d_fwd_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_f16_instance.cpp
xdl/device_grouped_conv3d_fwd_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_f32_instance.cpp
xdl/device_grouped_conv3d_fwd_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instance.cpp
xdl/device_grouped_conv3d_fwd_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_int8_instance.cpp)
add_instance_library(device_grouped_conv3d_fwd_bilinear_instance ${GROUPED_CONV3D_FWD_BILINEAR})

57
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_bilinear/xdl/device_grouped_conv3d_fwd_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instance.cpp Normal file
View File

@@ -0,0 +1,57 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_bilinear_instance.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_grouped_conv3d_fwd_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
ck::Tuple<NDHWGK>,
NDHWGK,
F32,
F32,
ck::Tuple<F32>,
F32,
PassThrough,
PassThrough,
Bilinear,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_bilinear_f32_tf32_instances<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK>,
NDHWGK,
ConvFwdDefault>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_bilinear_f32_tf32_instances<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK>,
NDHWGK,
ConvFwd1x1P0>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_bilinear_f32_tf32_instances<3,
NDHWGC,
GKZYXC,
Tuple<NDHWGK>,
NDHWGK,
ConvFwd1x1S1P0>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

5
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_clamp/CMakeLists.txt
View File

@@ -19,10 +19,15 @@ set(GROUPED_CONV3D_FWD
xdl/device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_fp32_instance.cpp
xdl/device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_fp32_16x16_instance.cpp
xdl/large_tensor/device_grouped_conv3d_fwd_clamp_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_fp32_instance.cpp
xdl/large_tensor/device_grouped_conv3d_fwd_clamp_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_fp32_tf32_instance.cpp
xdl/merged_groups/device_grouped_conv3d_fwd_clamp_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_fp32_instance.cpp
xdl/merged_groups/device_grouped_conv3d_fwd_clamp_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_fp32_tf32_instance.cpp
xdl/mem/device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_fp32_mem_inter_instance.cpp
xdl/mem/device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_fp32_tf32_mem_inter_instance.cpp
xdl/mem/device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_fp32_mem_intra_instance.cpp
xdl/mem/device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_fp32_tf32_mem_intra_instance.cpp
xdl/comp/device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_fp32_comp_instance.cpp
xdl/comp/device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_fp32_tf32_comp_instance.cpp
xdl/device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_fp32_tf32_instance.cpp
)

63
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_clamp/xdl/comp/device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_fp32_tf32_comp_instance.cpp Normal file
View File

@@ -0,0 +1,63 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_comp_instance.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/host_utility/device_prop.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_comp_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Tuple<>,
NDHWGK,
F32,
F32,
Tuple<>,
F32,
PassThrough,
PassThrough,
Clamp,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_comp_instances<3,
NDHWGC,
GKZYXC,
Tuple<>,
NDHWGK,
ConvFwdDefault,
Tuple<>,
Clamp>{});
add_device_operation_instances(instances,
device_grouped_conv_fwd_xdl_f32_tf32_comp_instances<3,
NDHWGC,
GKZYXC,
Tuple<>,
NDHWGK,
ConvFwd1x1P0,
Tuple<>,
Clamp>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_comp_instances<3,
NDHWGC,
GKZYXC,
Tuple<>,
NDHWGK,
ConvFwd1x1S1P0,
Tuple<>,
Clamp>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

43
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_clamp/xdl/large_tensor/device_grouped_conv3d_fwd_clamp_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_fp32_tf32_instance.cpp Normal file
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@@ -0,0 +1,43 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_large_tensor_instance.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_grouped_conv3d_fwd_clamp_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Tuple<>,
NDHWGK,
F32,
F32,
Tuple<>,
F32,
PassThrough,
PassThrough,
Clamp,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_large_tensor_f32_tf32_instances<3,
NDHWGC,
GKZYXC,
Tuple<>,
NDHWGK,
ConvFwdDefault,
Tuple<>,
Clamp>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

65
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_clamp/xdl/mem/device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_fp32_tf32_mem_inter_instance.cpp Normal file
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@@ -0,0 +1,65 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_mem_instance.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_inter_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Tuple<>,
NDHWGK,
F32,
F32,
Tuple<>,
F32,
PassThrough,
PassThrough,
Clamp,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<3,
NDHWGC,
GKZYXC,
Tuple<>,
NDHWGK,
ConvFwdDefault,
Interwave,
Tuple<>,
Clamp>{});
add_device_operation_instances(instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<3,
NDHWGC,
GKZYXC,
Tuple<>,
NDHWGK,
ConvFwd1x1P0,
Interwave,
Tuple<>,
Clamp>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<3,
NDHWGC,
GKZYXC,
Tuple<>,
NDHWGK,
ConvFwd1x1S1P0,
Interwave,
Tuple<>,
Clamp>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

65
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_clamp/xdl/mem/device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_fp32_tf32_mem_intra_instance.cpp Normal file
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@@ -0,0 +1,65 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_mem_instance.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_grouped_conv3d_fwd_clamp_xdl_ndhwgc_gkzyxc_ndhwgk_f32_tf32_mem_intra_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Tuple<>,
NDHWGK,
F32,
F32,
Tuple<>,
F32,
PassThrough,
PassThrough,
Clamp,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<3,
NDHWGC,
GKZYXC,
Tuple<>,
NDHWGK,
ConvFwdDefault,
Intrawave,
Tuple<>,
Clamp>{});
add_device_operation_instances(instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<3,
NDHWGC,
GKZYXC,
Tuple<>,
NDHWGK,
ConvFwd1x1P0,
Intrawave,
Tuple<>,
Clamp>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f32_tf32_mem_instances<3,
NDHWGC,
GKZYXC,
Tuple<>,
NDHWGK,
ConvFwd1x1S1P0,
Intrawave,
Tuple<>,
Clamp>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

53
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_clamp/xdl/merged_groups/device_grouped_conv3d_fwd_clamp_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_fp32_tf32_instance.cpp Normal file
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@@ -0,0 +1,53 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_merged_groups_instance.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_grouped_conv3d_fwd_clamp_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Tuple<>,
NDHWGK,
F32,
F32,
Tuple<>,
F32,
PassThrough,
PassThrough,
Clamp,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_merged_groups_f32_tf32_instances<3,
NDHWGC,
GKZYXC,
Tuple<>,
NDHWGK,
ConvFwdDefault,
Tuple<>,
Clamp>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_merged_groups_f32_tf32_instances<3,
NDHWGC,
GKZYXC,
Tuple<>,
NDHWGK,
ConvFwd3x3,
Tuple<>,
Clamp>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

1
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_scale/CMakeLists.txt
View File

@@ -3,6 +3,7 @@ set(GROUPED_CONV3D_FWD_BILINEAR
xdl/device_grouped_conv3d_fwd_xdl_scale_ndhwgc_gkzyxc_ndhwgk_bf16_instance.cpp
xdl/device_grouped_conv3d_fwd_xdl_scale_ndhwgc_gkzyxc_ndhwgk_f16_instance.cpp
xdl/device_grouped_conv3d_fwd_xdl_scale_ndhwgc_gkzyxc_ndhwgk_f32_instance.cpp
xdl/device_grouped_conv3d_fwd_xdl_scale_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instance.cpp
xdl/device_grouped_conv3d_fwd_xdl_scale_ndhwgc_gkzyxc_ndhwgk_int8_instance.cpp)
add_instance_library(device_grouped_conv3d_fwd_scale_instance ${GROUPED_CONV3D_FWD_BILINEAR})

57
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd_scale/xdl/device_grouped_conv3d_fwd_xdl_scale_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instance.cpp Normal file
View File

@@ -0,0 +1,57 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_scale_instance.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_grouped_conv3d_fwd_xdl_scale_ndhwgc_gkzyxc_ndhwgk_f32_tf32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
ck::Tuple<>,
NDHWGK,
F32,
F32,
ck::Tuple<>,
F32,
PassThrough,
PassThrough,
Scale,
TF32,
TF32>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_scale_f32_tf32_instances<3,
NDHWGC,
GKZYXC,
Tuple<>,
NDHWGK,
ConvFwdDefault>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_scale_f32_tf32_instances<3,
NDHWGC,
GKZYXC,
Tuple<>,
NDHWGK,
ConvFwd1x1P0>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_scale_f32_tf32_instances<3,
NDHWGC,
GKZYXC,
Tuple<>,
NDHWGK,
ConvFwd1x1S1P0>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck