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Commit b6d3aa5d authored by Andriy Roshchenko's avatar Andriy Roshchenko
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Merge branch 'gfx950' into andriy/lwpck-2430

parents a634647d b6f7cddd
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include/ck_tile/ops/gemm/pipeline/gemm_universal_pipeline_ag_bg_cr_policy.hpp
View file @ b6d3aa5d
...@@ -9,12 +9,8 @@ ...@@ -9,12 +9,8 @@
namespace ck_tile { namespace ck_tile {
// UniversalGemm Policy // UniversalGemm Policy
template <typename LayoutA_, typename LayoutB_, typename LayoutC_>
struct UniversalGemmPipelineAgBgCrPolicy struct UniversalGemmPipelineAgBgCrPolicy
{ {
using LayoutA = remove_cvref_t<LayoutA_>;
using LayoutB = remove_cvref_t<LayoutB_>;
using LayoutC = remove_cvref_t<LayoutC_>;
static constexpr auto I0 = number<0>{}; static constexpr auto I0 = number<0>{};
static constexpr auto I1 = number<1>{}; static constexpr auto I1 = number<1>{};
...@@ -22,286 +18,136 @@ struct UniversalGemmPipelineAgBgCrPolicy ...@@ -22,286 +18,136 @@ struct UniversalGemmPipelineAgBgCrPolicy
static constexpr bool TransposeC = true; static constexpr bool TransposeC = true;
template <typename Problem, typename DataType, index_t MNPerBlock>
CK_TILE_HOST_DEVICE static constexpr auto GetVectorLoadSize()
{
constexpr index_t BlockSize = Problem::kBlockSize;
constexpr index_t KPerBlock = Problem::BlockGemmShape::kK;
constexpr index_t elements_per_thread = MNPerBlock * KPerBlock / BlockSize;
if constexpr(elements_per_thread % (16 / sizeof(DataType)) == 0)
{
return (16 / sizeof(DataType));
}
else if constexpr(elements_per_thread % (8 / sizeof(DataType)) == 0)
{
return (8 / sizeof(DataType));
}
else if constexpr(elements_per_thread % (4 / sizeof(DataType)) == 0 &&
sizeof(DataType) >= 4)
{
return (4 / sizeof(DataType));
}
else if constexpr(elements_per_thread % (2 / sizeof(DataType)) == 0 &&
sizeof(DataType) >= 2)
{
return (2 / sizeof(DataType));
}
else
{
return 1;
}
}
template <typename Problem> template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeALdsBlockDescriptor() CK_TILE_HOST_DEVICE static constexpr auto MakeALdsBlockDescriptor()
{ {
using WarpGemm = WarpGemmMfmaDispatcher<typename Problem::ADataType,
typename Problem::BDataType,
typename Problem::CDataType,
Problem::BlockGemmShape::WarpTile::at(I0),
Problem::BlockGemmShape::WarpTile::at(I1),
Problem::BlockGemmShape::WarpTile::at(I2),
TransposeC>;
using ADataType = remove_cvref_t<typename Problem::ADataType>; using ADataType = remove_cvref_t<typename Problem::ADataType>;
constexpr index_t MPerBlock = Problem::BlockGemmShape::kM; constexpr index_t MPerBlock = Problem::BlockGemmShape::kM;
constexpr index_t KPerBlock = Problem::BlockGemmShape::kK; constexpr index_t KPerBlock = Problem::BlockGemmShape::kK;
constexpr index_t K1 = WarpGemm::kK; constexpr index_t KPack = GetVectorLoadSize<Problem, ADataType, MPerBlock>();
constexpr index_t K0 = KPerBlock / K1;
constexpr auto DataTypeSize = sizeof(ADataType);
if constexpr(std::is_same<tensor_layout::gemm::RowMajor, LayoutA>::value) constexpr auto MLdsLayer =
{ (32 * 4 / KPerBlock / DataTypeSize) < 1 ? 1 : (32 * 4 / KPerBlock / DataTypeSize);
constexpr auto MLdsLayer = 32 * 4 / KPerBlock / sizeof(ADataType) < 1
? 1 constexpr auto a_lds_block_desc_0 = make_naive_tensor_descriptor(
: 32 * 4 / KPerBlock / sizeof(ADataType); make_tuple(number<KPerBlock / KPack * MLdsLayer>{},
constexpr auto a_lds_block_desc = make_naive_tensor_descriptor( number<MPerBlock / MLdsLayer>{},
make_tuple(K0 * number<MLdsLayer>{}, number<MPerBlock / MLdsLayer>{}, K1), number<KPack>{}),
make_tuple(K1, number<KPerBlock * MLdsLayer>{}, I1)); make_tuple(number<KPack>{}, number<KPerBlock * MLdsLayer>{}, number<1>{}),
number<KPack>{},
constexpr auto a_lds_block_desc_permuted = transform_tensor_descriptor( number<1>{});
a_lds_block_desc,
make_tuple(make_xor_transform(make_tuple(number<MPerBlock / MLdsLayer>{}, constexpr auto a_lds_block_desc_permuted = transform_tensor_descriptor(
number<K0 * MLdsLayer>{})), a_lds_block_desc_0,
make_pass_through_transform(K1)), make_tuple(make_xor_transform(make_tuple(number<MPerBlock / MLdsLayer>{},
make_tuple(sequence<1, 0>{}, sequence<2>{}), number<KPerBlock / KPack * MLdsLayer>{})),
make_tuple(sequence<1, 0>{}, sequence<2>{})); make_pass_through_transform(number<KPack>{})),
make_tuple(sequence<1, 0>{}, sequence<2>{}),
constexpr auto a_lds_block_desc_ak0_kMLdsLayer_m_ak1 = transform_tensor_descriptor( make_tuple(sequence<1, 0>{}, sequence<2>{}));
a_lds_block_desc_permuted,
make_tuple(make_unmerge_transform(make_tuple(K0, number<MLdsLayer>{})), constexpr auto a_lds_block_desc_xk0_mnldslayer_mn_xk1 = transform_tensor_descriptor(
make_pass_through_transform(number<MPerBlock / MLdsLayer>{}), a_lds_block_desc_permuted,
make_pass_through_transform(K1)), make_tuple(make_unmerge_transform(
make_tuple(sequence<0>{}, sequence<1>{}, sequence<2>{}), make_tuple(number<KPerBlock / KPack>{}, number<MLdsLayer>{})),
make_tuple(sequence<0, 2>{}, sequence<1>{}, sequence<3>{})); make_pass_through_transform(number<MPerBlock / MLdsLayer>{}),
make_pass_through_transform(number<KPack>{})),
constexpr auto a_lds_block_desc_m_k = transform_tensor_descriptor( make_tuple(sequence<0>{}, sequence<1>{}, sequence<2>{}),
a_lds_block_desc_ak0_kMLdsLayer_m_ak1, make_tuple(sequence<0, 2>{}, sequence<1>{}, sequence<3>{}));
make_tuple(make_merge_transform_v3_division_mod(make_tuple(K0, K1)),
make_merge_transform_v3_division_mod( constexpr auto a_lds_block_desc = transform_tensor_descriptor(
make_tuple(number<MPerBlock / MLdsLayer>{}, number<MLdsLayer>{}))), a_lds_block_desc_xk0_mnldslayer_mn_xk1,
make_tuple(sequence<0, 3>{}, sequence<1, 2>{}), make_tuple(make_merge_transform_v3_division_mod(
make_tuple(sequence<1>{}, sequence<0>{})); make_tuple(number<MPerBlock / MLdsLayer>{}, number<MLdsLayer>{})),
make_merge_transform_v3_division_mod(
return a_lds_block_desc_m_k; make_tuple(number<KPerBlock / KPack>{}, number<KPack>{}))),
} make_tuple(sequence<1, 2>{}, sequence<0, 3>{}),
else // ColumnMajor A make_tuple(sequence<0>{}, sequence<1>{}));
{
// kfold and mpair dimension is not always required. return a_lds_block_desc;
// more dimension in merge_transform increase the difficulty of generating immarg offset
// for compiler.
constexpr auto M0 = get_warp_size() * Problem::BlockGemmShape::BlockWarps::at(I0);
constexpr auto M1 = MPerBlock / M0;
constexpr auto KThreadWrite = Problem::kBlockSize / M0;
constexpr auto K0PerThreadWrite = K0 / KThreadWrite;
constexpr auto KThreadRead = 64 / WarpGemm::kM;
constexpr auto K0PerThreadRead = K0 / KThreadRead;
constexpr auto kfold =
(K1 * M0 * sizeof(ADataType) > 128) ? 1 : 128 / (K1 * M0 * sizeof(ADataType));
constexpr auto KThreadReadPerm =
(kfold * K0PerThreadWrite / K0PerThreadRead) > 1
? KThreadRead / (kfold * K0PerThreadWrite / K0PerThreadRead)
: KThreadRead;
// 1<=mpair<=kN0
constexpr auto mpair = (K1 * WarpGemm::kM * sizeof(ADataType) > 128)
? 1
: ((128 / (K1 * WarpGemm::kM * sizeof(ADataType))) > M0
? M0
: 128 / (K1 * WarpGemm::kM * sizeof(ADataType)));
constexpr auto a_lds_block_desc = make_naive_tensor_descriptor_packed(
make_tuple(number<KThreadWrite / kfold / KThreadReadPerm>{},
number<K0PerThreadWrite>{},
number<KThreadReadPerm * M1>{},
number<kfold * M0 / mpair>{},
number<mpair>{},
K1));
constexpr auto a_lds_block_desc_permuted = transform_tensor_descriptor(
a_lds_block_desc,
make_tuple(
make_pass_through_transform(number<KThreadWrite / kfold / KThreadReadPerm>{}),
make_pass_through_transform(number<K0PerThreadWrite>{}),
make_xor_transform(
make_tuple(number<KThreadReadPerm * M1>{}, number<kfold * M0 / mpair>{})),
make_pass_through_transform(number<mpair>{}),
make_pass_through_transform(K1)),
make_tuple(
sequence<0>{}, sequence<1>{}, sequence<2, 3>{}, sequence<4>{}, sequence<5>{}),
make_tuple(
sequence<0>{}, sequence<1>{}, sequence<2, 3>{}, sequence<4>{}, sequence<5>{}));
constexpr auto a_lds_block_desc_unmerged = transform_tensor_descriptor(
a_lds_block_desc_permuted,
make_tuple(
make_pass_through_transform(number<KThreadWrite / kfold / KThreadReadPerm>{}),
make_pass_through_transform(number<K0PerThreadWrite>{}),
make_unmerge_transform(make_tuple(number<KThreadReadPerm>{}, number<M1>{})),
make_unmerge_transform(make_tuple(number<kfold>{}, number<M0 / mpair>{})),
make_pass_through_transform(number<mpair>{}),
make_pass_through_transform(K1)),
make_tuple(sequence<0>{},
sequence<1>{},
sequence<2>{},
sequence<3>{},
sequence<4>{},
sequence<5>{}),
make_tuple(sequence<1>{},
sequence<2>{},
sequence<0, 3>{},
sequence<4, 5>{},
sequence<6>{},
sequence<7>{}));
constexpr auto a_lds_block_desc_m_k = transform_tensor_descriptor(
a_lds_block_desc_unmerged,
make_tuple(make_merge_transform_v3_division_mod(
make_tuple(number<KThreadReadPerm>{},
number<KThreadWrite / kfold / KThreadReadPerm>{},
number<kfold>{},
number<K0PerThreadWrite>{},
K1)),
make_merge_transform_v3_division_mod(
make_tuple(number<M0 / mpair>{}, number<mpair>{}, number<M1>{}))),
make_tuple(sequence<0, 1, 4, 2, 7>{}, sequence<5, 6, 3>{}),
make_tuple(sequence<1>{}, sequence<0>{}));
return a_lds_block_desc_m_k;
}
} }
template <typename Problem> template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeBLdsBlockDescriptor() CK_TILE_HOST_DEVICE static constexpr auto MakeBLdsBlockDescriptor()
{ {
using WarpGemm = WarpGemmMfmaDispatcher<typename Problem::ADataType,
typename Problem::BDataType,
typename Problem::CDataType,
Problem::BlockGemmShape::WarpTile::at(I0),
Problem::BlockGemmShape::WarpTile::at(I1),
Problem::BlockGemmShape::WarpTile::at(I2),
TransposeC>;
using BDataType = remove_cvref_t<typename Problem::BDataType>; using BDataType = remove_cvref_t<typename Problem::BDataType>;
constexpr index_t NPerBlock = Problem::BlockGemmShape::kN; constexpr index_t NPerBlock = Problem::BlockGemmShape::kN;
constexpr index_t KPerBlock = Problem::BlockGemmShape::kK; constexpr index_t KPerBlock = Problem::BlockGemmShape::kK;
constexpr index_t KPack = GetVectorLoadSize<Problem, BDataType, NPerBlock>();
constexpr index_t K1 = WarpGemm::kK;
constexpr index_t K0 = KPerBlock / K1; constexpr auto DataTypeSize = sizeof(BDataType);
constexpr auto NLdsLayer =
if constexpr(std::is_same<tensor_layout::gemm::ColumnMajor, LayoutB>::value) (32 * 4 / KPerBlock / DataTypeSize) < 1 ? 1 : (32 * 4 / KPerBlock / DataTypeSize);
{
// NLdsLayer * K0 as logical Bank constexpr auto b_lds_block_desc_0 = make_naive_tensor_descriptor(
constexpr auto NLdsLayer = 32 * 4 / KPerBlock / sizeof(BDataType) < 1 make_tuple(number<KPerBlock / KPack * NLdsLayer>{},
? 1 number<NPerBlock / NLdsLayer>{},
: 32 * 4 / KPerBlock / sizeof(BDataType); number<KPack>{}),
; make_tuple(number<KPack>{}, number<KPerBlock * NLdsLayer>{}, number<1>{}),
constexpr auto b_lds_block_desc = make_naive_tensor_descriptor( number<KPack>{},
make_tuple(K0 * number<NLdsLayer>{}, number<NPerBlock / NLdsLayer>{}, K1), number<1>{});
make_tuple(K1, number<KPerBlock * NLdsLayer>{}, I1));
constexpr auto b_lds_block_desc_permuted = transform_tensor_descriptor(
constexpr auto b_lds_block_desc_permuted = transform_tensor_descriptor( b_lds_block_desc_0,
b_lds_block_desc, make_tuple(make_xor_transform(make_tuple(number<NPerBlock / NLdsLayer>{},
make_tuple(make_xor_transform(make_tuple(number<NPerBlock / NLdsLayer>{}, number<KPerBlock / KPack * NLdsLayer>{})),
number<K0 * NLdsLayer>{})), make_pass_through_transform(number<KPack>{})),
make_pass_through_transform(K1)), make_tuple(sequence<1, 0>{}, sequence<2>{}),
make_tuple(sequence<1, 0>{}, sequence<2>{}), make_tuple(sequence<1, 0>{}, sequence<2>{}));
make_tuple(sequence<1, 0>{}, sequence<2>{}));
constexpr auto b_lds_block_desc_xk0_mnldslayer_mn_xk1 = transform_tensor_descriptor(
constexpr auto b_lds_block_desc_bk0_kNLdsLayer_n_bk1 = transform_tensor_descriptor( b_lds_block_desc_permuted,
b_lds_block_desc_permuted, make_tuple(make_unmerge_transform(
make_tuple(make_unmerge_transform(make_tuple(K0, number<NLdsLayer>{})), make_tuple(number<KPerBlock / KPack>{}, number<NLdsLayer>{})),
make_pass_through_transform(number<NPerBlock / NLdsLayer>{}), make_pass_through_transform(number<NPerBlock / NLdsLayer>{}),
make_pass_through_transform(K1)), make_pass_through_transform(number<KPack>{})),
make_tuple(sequence<0>{}, sequence<1>{}, sequence<2>{}), make_tuple(sequence<0>{}, sequence<1>{}, sequence<2>{}),
make_tuple(sequence<0, 2>{}, sequence<1>{}, sequence<3>{})); make_tuple(sequence<0, 2>{}, sequence<1>{}, sequence<3>{}));
constexpr auto b_lds_block_desc_n_k = transform_tensor_descriptor( constexpr auto b_lds_block_desc = transform_tensor_descriptor(
b_lds_block_desc_bk0_kNLdsLayer_n_bk1, b_lds_block_desc_xk0_mnldslayer_mn_xk1,
make_tuple(make_merge_transform_v3_division_mod(make_tuple(K0, K1)), make_tuple(make_merge_transform_v3_division_mod(
make_merge_transform_v3_division_mod( make_tuple(number<NPerBlock / NLdsLayer>{}, number<NLdsLayer>{})),
make_tuple(number<NPerBlock / NLdsLayer>{}, number<NLdsLayer>{}))), make_merge_transform_v3_division_mod(
make_tuple(sequence<0, 3>{}, sequence<1, 2>{}), make_tuple(number<KPerBlock / KPack>{}, number<KPack>{}))),
make_tuple(sequence<1>{}, sequence<0>{})); make_tuple(sequence<1, 2>{}, sequence<0, 3>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
return b_lds_block_desc_n_k; return b_lds_block_desc;
}
else // RowMajor B
{
constexpr auto N0 = get_warp_size() * Problem::BlockGemmShape::BlockWarps::at(I1);
constexpr auto N1 = NPerBlock / N0;
constexpr auto KThreadWrite = Problem::kBlockSize / N0;
constexpr auto K0PerThreadWrite = K0 / KThreadWrite;
constexpr auto KThreadRead = 64 / WarpGemm::kN;
constexpr auto K0PerThreadRead = K0 / KThreadRead;
constexpr auto kfold =
(K1 * N0 * sizeof(BDataType) > 128) ? 1 : 128 / (K1 * N0 * sizeof(BDataType));
constexpr auto KThreadReadPerm =
(kfold * K0PerThreadWrite / K0PerThreadRead) > 1
? KThreadRead / (kfold * K0PerThreadWrite / K0PerThreadRead)
: KThreadRead;
// 1<=npair<=kN0
constexpr auto npair = (K1 * WarpGemm::kN * sizeof(BDataType) > 128)
? 1
: ((128 / (K1 * WarpGemm::kN * sizeof(BDataType))) > N0
? N0
: 128 / (K1 * WarpGemm::kN * sizeof(BDataType)));
constexpr auto b_lds_block_desc = make_naive_tensor_descriptor_packed(
make_tuple(number<KThreadWrite / kfold / KThreadReadPerm>{},
number<K0PerThreadWrite>{},
number<KThreadReadPerm * N1>{},
number<kfold * N0 / npair>{},
number<npair>{},
K1));
constexpr auto b_lds_block_desc_permuted = transform_tensor_descriptor(
b_lds_block_desc,
make_tuple(
make_pass_through_transform(number<KThreadWrite / kfold / KThreadReadPerm>{}),
make_pass_through_transform(number<K0PerThreadWrite>{}),
make_xor_transform(
make_tuple(number<KThreadReadPerm * N1>{}, number<kfold * N0 / npair>{})),
make_pass_through_transform(number<npair>{}),
make_pass_through_transform(K1)),
make_tuple(
sequence<0>{}, sequence<1>{}, sequence<2, 3>{}, sequence<4>{}, sequence<5>{}),
make_tuple(
sequence<0>{}, sequence<1>{}, sequence<2, 3>{}, sequence<4>{}, sequence<5>{}));
constexpr auto b_lds_block_desc_unmerged = transform_tensor_descriptor(
b_lds_block_desc_permuted,
make_tuple(
make_pass_through_transform(number<KThreadWrite / kfold / KThreadReadPerm>{}),
make_pass_through_transform(number<K0PerThreadWrite>{}),
make_unmerge_transform(make_tuple(number<KThreadReadPerm>{}, number<N1>{})),
make_unmerge_transform(make_tuple(number<kfold>{}, number<N0 / npair>{})),
make_pass_through_transform(number<npair>{}),
make_pass_through_transform(K1)),
make_tuple(sequence<0>{},
sequence<1>{},
sequence<2>{},
sequence<3>{},
sequence<4>{},
sequence<5>{}),
make_tuple(sequence<1>{},
sequence<2>{},
sequence<0, 3>{},
sequence<4, 5>{},
sequence<6>{},
sequence<7>{}));
constexpr auto b_lds_block_desc_n_k = transform_tensor_descriptor(
b_lds_block_desc_unmerged,
make_tuple(make_merge_transform_v3_division_mod(
make_tuple(number<KThreadReadPerm>{},
number<KThreadWrite / kfold / KThreadReadPerm>{},
number<kfold>{},
number<K0PerThreadWrite>{},
K1)),
make_merge_transform_v3_division_mod(
make_tuple(number<N0 / npair>{}, number<npair>{}, number<N1>{}))),
make_tuple(sequence<0, 1, 4, 2, 7>{}, sequence<5, 6, 3>{}),
make_tuple(sequence<1>{}, sequence<0>{}));
return b_lds_block_desc_n_k;
}
} }
template <typename Problem> template <typename Problem>
...@@ -334,69 +180,268 @@ struct UniversalGemmPipelineAgBgCrPolicy ...@@ -334,69 +180,268 @@ struct UniversalGemmPipelineAgBgCrPolicy
template <typename Problem> template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeADramTileDistribution() CK_TILE_HOST_DEVICE static constexpr auto MakeADramTileDistribution()
{ {
using WarpGemm = WarpGemmMfmaDispatcher<typename Problem::ADataType, using ADataType = remove_cvref_t<typename Problem::ADataType>;
typename Problem::BDataType, using ALayout = remove_cvref_t<typename Problem::ALayout>;
typename Problem::CDataType,
Problem::BlockGemmShape::WarpTile::at(I0),
Problem::BlockGemmShape::WarpTile::at(I1),
Problem::BlockGemmShape::WarpTile::at(I2),
TransposeC>;
constexpr index_t BlockSize = Problem::kBlockSize; constexpr index_t BlockSize = Problem::kBlockSize;
constexpr index_t MPerBlock = Problem::BlockGemmShape::kM; constexpr index_t MPerBlock = Problem::BlockGemmShape::kM;
constexpr index_t KPerBlock = Problem::BlockGemmShape::kK; constexpr index_t KPerBlock = Problem::BlockGemmShape::kK;
constexpr index_t K1 = WarpGemm::kK; if constexpr(std::is_same_v<ALayout, ck_tile::tensor_layout::gemm::ColumnMajor>)
constexpr index_t K0 = KPerBlock / K1; {
constexpr index_t M2 = get_warp_size() / K0; constexpr index_t M1 = Problem::VectorLoadSize / sizeof(ADataType);
constexpr index_t M0 = MPerBlock / M1;
constexpr index_t M1 = BlockSize / get_warp_size(); constexpr index_t total_pixels = MPerBlock * KPerBlock / BlockSize;
static_assert(M2 != 0, "M2 is zero, which will lead to a division by zero error."); static_assert(total_pixels % M1 == 0);
static_assert(M1 != 0, "M1 is zero, which will lead to a division by zero error."); constexpr index_t K3 = total_pixels / M1;
constexpr index_t M0 = MPerBlock / (M2 * M1); constexpr index_t KPack = GetVectorLoadSize<Problem, ADataType, MPerBlock>();
static_assert(KPack % K3 == 0);
return make_static_tile_distribution( constexpr index_t K2 = KPack / K3;
tile_distribution_encoding<sequence<1>, if constexpr(get_warp_size() % (K2 * M0) == 0)
tuple<sequence<M0, M1, M2>, sequence<K0, K1>>, {
tuple<sequence<1>, sequence<1, 2>>, constexpr index_t K1 = get_warp_size() / (K2 * M0);
tuple<sequence<1>, sequence<2, 0>>, constexpr index_t K0 = BlockSize / get_warp_size();
sequence<1, 2>, static_assert(KPerBlock == K0 * K1 * K2 * K3);
sequence<0, 1>>{}); return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<M0, M1>, sequence<K0, K1, K2, K3>>,
tuple<sequence<2>, sequence<2, 1, 2>>,
tuple<sequence<0>, sequence<1, 0, 2>>,
sequence<2, 1>,
sequence<3, 1>>{});
}
else
{
constexpr index_t K1 = (K2 * M0) / get_warp_size();
constexpr index_t K2_m = K2 / K1;
constexpr index_t K0 = BlockSize / get_warp_size() / K1;
static_assert(KPerBlock == K0 * K1 * K2_m * K3);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<M0, M1>, sequence<K0, K1, K2_m, K3>>,
tuple<sequence<2, 2>, sequence<1, 2>>,
tuple<sequence<0, 1>, sequence<0, 2>>,
sequence<2, 1>,
sequence<3, 1>>{});
}
}
else
{
constexpr index_t K1 = Problem::VectorLoadSize / sizeof(ADataType);
constexpr index_t K0 = KPerBlock / K1;
constexpr index_t M2 = get_warp_size() / K0;
if constexpr(get_warp_size() % (M2 * K0) == 0)
{
constexpr index_t M1 = BlockSize / get_warp_size();
static_assert(M2 != 0, "M2 is zero, which will lead to a division by zero error.");
static_assert(M1 != 0, "M1 is zero, which will lead to a division by zero error.");
constexpr index_t M0 = MPerBlock / (M2 * M1);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<M0, M1, M2>, sequence<K0, K1>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<1>, sequence<2, 0>>,
sequence<1, 2>,
sequence<0, 1>>{});
}
else
{
constexpr index_t M0 = BlockSize / get_warp_size();
constexpr index_t M1 = MPerBlock / (M2 * M0);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<M0, M1, M2>, sequence<K0, K1>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<0>, sequence<2, 0>>,
sequence<1, 2>,
sequence<1, 1>>{});
}
}
} }
template <typename Problem> template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeBDramTileDistribution() CK_TILE_HOST_DEVICE static constexpr auto MakeBDramTileDistribution()
{ {
using WarpGemm = WarpGemmMfmaDispatcher<typename Problem::ADataType, using BDataType = remove_cvref_t<typename Problem::BDataType>;
typename Problem::BDataType, using BLayout = remove_cvref_t<typename Problem::BLayout>;
typename Problem::CDataType,
Problem::BlockGemmShape::WarpTile::at(I0), constexpr index_t BlockSize = Problem::kBlockSize;
Problem::BlockGemmShape::WarpTile::at(I1),
Problem::BlockGemmShape::WarpTile::at(I2), constexpr index_t NPerBlock = Problem::BlockGemmShape::kN;
TransposeC>; constexpr index_t KPerBlock = Problem::BlockGemmShape::kK;
if constexpr(std::is_same_v<BLayout, ck_tile::tensor_layout::gemm::RowMajor>)
{
constexpr index_t N1 = Problem::VectorLoadSize / sizeof(BDataType);
constexpr index_t N0 = NPerBlock / N1;
constexpr index_t total_pixels = NPerBlock * KPerBlock / BlockSize;
static_assert(total_pixels % N1 == 0);
constexpr index_t K3 = total_pixels / N1;
constexpr index_t KPack = GetVectorLoadSize<Problem, BDataType, NPerBlock>();
static_assert(KPack % K3 == 0);
constexpr index_t K2 = KPack / K3;
if constexpr(get_warp_size() % (K2 * N0) == 0)
{
constexpr index_t K1 = get_warp_size() / (K2 * N0);
constexpr index_t K0 = BlockSize / get_warp_size();
static_assert(KPerBlock == K0 * K1 * K2 * K3);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<N0, N1>, sequence<K0, K1, K2, K3>>,
tuple<sequence<2>, sequence<2, 1, 2>>,
tuple<sequence<0>, sequence<1, 0, 2>>,
sequence<2, 1>,
sequence<3, 1>>{});
}
else
{
constexpr index_t K1 = (K2 * N0) / get_warp_size();
constexpr index_t K2_m = K2 / K1;
constexpr index_t K0 = BlockSize / get_warp_size() / K1;
static_assert(KPerBlock == K0 * K1 * K2_m * K3);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<N0, N1>, sequence<K0, K1, K2_m, K3>>,
tuple<sequence<2, 2>, sequence<1, 2>>,
tuple<sequence<0, 1>, sequence<0, 2>>,
sequence<2, 1>,
sequence<3, 1>>{});
}
}
else
{
constexpr index_t K1 = Problem::VectorLoadSize / sizeof(BDataType);
constexpr index_t K0 = KPerBlock / K1;
constexpr index_t N2 = get_warp_size() / K0;
// coalesce reading for each blocks
if constexpr(get_warp_size() % (N2 * K0) == 0)
{
constexpr index_t N1 = BlockSize / get_warp_size();
static_assert(N2 != 0, "N2 is zero, which will lead to a division by zero error.");
static_assert(N1 != 0, "N1 is zero, which will lead to a division by zero error.");
constexpr index_t N0 = NPerBlock / (N2 * N1);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<N0, N1, N2>, sequence<K0, K1>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<1>, sequence<2, 0>>,
sequence<1, 2>,
sequence<0, 1>>{});
}
// coalesce reading for each warps
else
{
constexpr index_t N0 = BlockSize / get_warp_size();
constexpr index_t N1 = NPerBlock / (N2 * N0);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<N0, N1, N2>, sequence<K0, K1>>,
tuple<sequence<1>, sequence<1, 2>>,
tuple<sequence<0>, sequence<2, 0>>,
sequence<1, 2>,
sequence<1, 1>>{});
}
}
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeShuffledARegBlockDescriptor()
{
using ALayout = remove_cvref_t<typename Problem::ALayout>;
using ADataType = remove_cvref_t<typename Problem::ADataType>;
static_assert(std::is_same_v<ALayout, ck_tile::tensor_layout::gemm::ColumnMajor>);
constexpr index_t BlockSize = Problem::kBlockSize; constexpr index_t BlockSize = Problem::kBlockSize;
constexpr index_t MPerBlock = Problem::BlockGemmShape::kN;
constexpr index_t KPerBlock = Problem::BlockGemmShape::kK;
constexpr index_t M1 = Problem::VectorLoadSize / sizeof(ADataType);
constexpr index_t M0 = MPerBlock / M1;
constexpr index_t total_pixels = MPerBlock * KPerBlock / BlockSize;
static_assert(total_pixels % M1 == 0);
constexpr index_t K3 = total_pixels / M1;
constexpr index_t kKPack = GetVectorLoadSize<Problem, ADataType, MPerBlock>();
static_assert(kKPack % K3 == 0);
constexpr index_t K2 = kKPack / K3; // TODO: this dimention could be outside single wave
constexpr index_t warp_size = get_warp_size();
if constexpr(warp_size % (K2 * M0) == 0)
{
constexpr index_t K1 = warp_size / (K2 * M0);
constexpr index_t K0 = BlockSize / warp_size;
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<M0, M1>, sequence<K0, K1, K2, K3>>,
tuple<sequence<2>, sequence<2, 1, 2>>,
tuple<sequence<0>, sequence<1, 0, 2>>,
sequence<1, 2>,
sequence<1, 3>>{});
}
else
{
constexpr index_t K1 = (K2 * M0) / get_warp_size();
constexpr index_t K2_m = K2 / K1;
constexpr index_t K0 = BlockSize / get_warp_size() / K1;
static_assert(KPerBlock == K0 * K1 * K2_m * K3);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<M0, M1>, sequence<K0, K1, K2_m, K3>>,
tuple<sequence<2, 2>, sequence<1, 2>>,
tuple<sequence<0, 1>, sequence<0, 2>>,
sequence<1, 2>,
sequence<1, 3>>{});
}
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeShuffledBRegBlockDescriptor()
{
using BLayout = remove_cvref_t<typename Problem::BLayout>;
using BDataType = remove_cvref_t<typename Problem::BDataType>;
static_assert(std::is_same_v<BLayout, ck_tile::tensor_layout::gemm::RowMajor>);
constexpr index_t BlockSize = Problem::kBlockSize;
constexpr index_t NPerBlock = Problem::BlockGemmShape::kN; constexpr index_t NPerBlock = Problem::BlockGemmShape::kN;
constexpr index_t KPerBlock = Problem::BlockGemmShape::kK; constexpr index_t KPerBlock = Problem::BlockGemmShape::kK;
constexpr index_t K1 = WarpGemm::kK; constexpr index_t N1 = Problem::VectorLoadSize / sizeof(BDataType);
constexpr index_t K0 = KPerBlock / K1; constexpr index_t N0 = NPerBlock / N1;
constexpr index_t N2 = get_warp_size() / K0; constexpr index_t total_pixels = NPerBlock * KPerBlock / BlockSize;
static_assert(total_pixels % N1 == 0);
constexpr index_t N1 = BlockSize / get_warp_size(); constexpr index_t K3 = total_pixels / N1;
static_assert(N2 != 0, "M2 is zero, which will lead to a division by zero error."); constexpr index_t kKPack = GetVectorLoadSize<Problem, BDataType, NPerBlock>();
static_assert(N1 != 0, "M1 is zero, which will lead to a division by zero error."); static_assert(kKPack % K3 == 0);
constexpr index_t N0 = NPerBlock / (N2 * N1); constexpr index_t K2 = kKPack / K3; // TODO: this dimention could be outside single wave
constexpr index_t warp_size = get_warp_size();
return make_static_tile_distribution( if constexpr(warp_size % (K2 * N0) == 0)
tile_distribution_encoding<sequence<1>, {
tuple<sequence<N0, N1, N2>, sequence<K0, K1>>, constexpr index_t K1 = warp_size / (K2 * N0);
tuple<sequence<1>, sequence<1, 2>>, constexpr index_t K0 = BlockSize / warp_size;
tuple<sequence<1>, sequence<2, 0>>,
sequence<1, 2>, return make_static_tile_distribution(
sequence<0, 1>>{}); tile_distribution_encoding<sequence<1>,
tuple<sequence<N0, N1>, sequence<K0, K1, K2, K3>>,
tuple<sequence<2>, sequence<2, 1, 2>>,
tuple<sequence<0>, sequence<1, 0, 2>>,
sequence<1, 2>,
sequence<1, 3>>{});
}
else
{
constexpr index_t K1 = (K2 * N0) / get_warp_size();
constexpr index_t K2_m = K2 / K1;
constexpr index_t K0 = BlockSize / get_warp_size() / K1;
static_assert(KPerBlock == K0 * K1 * K2_m * K3);
return make_static_tile_distribution(
tile_distribution_encoding<sequence<1>,
tuple<sequence<N0, N1>, sequence<K0, K1, K2_m, K3>>,
tuple<sequence<2, 2>, sequence<1, 2>>,
tuple<sequence<0, 1>, sequence<0, 2>>,
sequence<1, 2>,
sequence<1, 3>>{});
}
} }
template <typename Problem> template <typename Problem>
......
include/ck_tile/ops/gemm/pipeline/tile_gemm_traits.hpp
View file @ b6d3aa5d
...@@ -3,19 +3,23 @@ ...@@ -3,19 +3,23 @@
#pragma once #pragma once
#include "ck_tile/core.hpp"
namespace ck_tile { namespace ck_tile {
template <bool kPadA_, template <bool kPadM_,
bool kPadB_, bool kPadN_,
bool kPadC_, bool kPadK_,
typename ALayout_, typename ALayout_,
typename BLayout_, typename BLayout_,
typename CLayout_> typename CLayout_>
struct TileGemmTraits struct TileGemmTraits
{ {
static constexpr bool kPadA = kPadA_; static constexpr bool kPadM = kPadM_;
static constexpr bool kPadB = kPadB_; static constexpr bool kPadN = kPadN_;
static constexpr bool kPadC = kPadC_; static constexpr bool kPadK = kPadK_;
static constexpr int _VectorSize = 16;
using ALayout = ALayout_; using ALayout = ALayout_;
using BLayout = BLayout_; using BLayout = BLayout_;
......
include/ck_tile/ops/layernorm2d/kernel/layernorm2d_fwd_kernel.hpp
View file @ b6d3aa5d
...@@ -28,7 +28,10 @@ struct Layernorm2dFwdHostArgs ...@@ -28,7 +28,10 @@ struct Layernorm2dFwdHostArgs
index_t m; index_t m;
index_t n; index_t n;
index_t stride; // row_stride index_t x_stride; // x row_stride
index_t xr_stride; // x residule row stride
index_t y_stride; // y row stride
index_t yr_stride; // y residule row stride
}; };
// TODO: Extract some type to wrapper class // TODO: Extract some type to wrapper class
...@@ -93,7 +96,10 @@ struct Layernorm2dFwd ...@@ -93,7 +96,10 @@ struct Layernorm2dFwd
index_t m; index_t m;
index_t n; index_t n;
index_t stride; // row_stride index_t x_stride; // x row_stride
index_t xr_stride; // x residule row stride
index_t y_stride; // y row stride
index_t yr_stride; // y residule row stride
}; };
using Hargs = Layernorm2dFwdHostArgs; using Hargs = Layernorm2dFwdHostArgs;
...@@ -112,7 +118,10 @@ struct Layernorm2dFwd ...@@ -112,7 +118,10 @@ struct Layernorm2dFwd
hargs.epsilon, hargs.epsilon,
hargs.m, hargs.m,
hargs.n, hargs.n,
hargs.stride}; hargs.x_stride,
hargs.xr_stride,
hargs.y_stride,
hargs.yr_stride};
} }
CK_TILE_HOST static constexpr auto GridSize(const Hargs& hargs) CK_TILE_HOST static constexpr auto GridSize(const Hargs& hargs)
...@@ -182,7 +191,7 @@ struct Layernorm2dFwd ...@@ -182,7 +191,7 @@ struct Layernorm2dFwd
const auto tmp_ = make_naive_tensor_view<address_space_enum::global>( const auto tmp_ = make_naive_tensor_view<address_space_enum::global>(
static_cast<const XDataType*>(kargs.p_x), static_cast<const XDataType*>(kargs.p_x),
make_tuple(kargs.m, kargs.n), make_tuple(kargs.m, kargs.n),
make_tuple(kargs.stride, 1), make_tuple(kargs.x_stride, 1),
number<Vector_N>{}, number<Vector_N>{},
number<1>{}); number<1>{});
...@@ -201,7 +210,7 @@ struct Layernorm2dFwd ...@@ -201,7 +210,7 @@ struct Layernorm2dFwd
const auto tmp_ = make_naive_tensor_view<address_space_enum::global>( const auto tmp_ = make_naive_tensor_view<address_space_enum::global>(
static_cast<const XResidualDataType*>(kargs.p_x_residual), static_cast<const XResidualDataType*>(kargs.p_x_residual),
make_tuple(kargs.m, kargs.n), make_tuple(kargs.m, kargs.n),
make_tuple(kargs.stride, 1), make_tuple(kargs.xr_stride, 1),
number<Vector_N>{}, number<Vector_N>{},
number<1>{}); number<1>{});
...@@ -250,7 +259,7 @@ struct Layernorm2dFwd ...@@ -250,7 +259,7 @@ struct Layernorm2dFwd
auto tmp_ = make_naive_tensor_view<address_space_enum::global>( auto tmp_ = make_naive_tensor_view<address_space_enum::global>(
static_cast<YDataType*>(kargs.p_y), static_cast<YDataType*>(kargs.p_y),
make_tuple(kargs.m, kargs.n), make_tuple(kargs.m, kargs.n),
make_tuple(kargs.stride, 1), make_tuple(kargs.y_stride, 1),
number<Vector_N>{}, number<Vector_N>{},
number<1>{}); number<1>{});
...@@ -266,7 +275,7 @@ struct Layernorm2dFwd ...@@ -266,7 +275,7 @@ struct Layernorm2dFwd
auto tmp_ = make_naive_tensor_view<address_space_enum::global>( auto tmp_ = make_naive_tensor_view<address_space_enum::global>(
static_cast<YResidualDataType*>(kargs.p_y_residual), static_cast<YResidualDataType*>(kargs.p_y_residual),
make_tuple(kargs.m, kargs.n), make_tuple(kargs.m, kargs.n),
make_tuple(kargs.stride, 1), make_tuple(kargs.yr_stride, 1),
number<Vector_N>{}, number<Vector_N>{},
number<1>{}); number<1>{});
......
include/ck_tile/ops/layernorm2d/pipeline/layernorm2d_fwd_pipeline_default_policy.hpp
View file @ b6d3aa5d
...@@ -47,7 +47,8 @@ struct Layernorm2dFwdPipelineDefaultPolicy ...@@ -47,7 +47,8 @@ struct Layernorm2dFwdPipelineDefaultPolicy
{ {
using P_ = BlockWelfordProblem<typename Problem::ComputeDataType, using P_ = BlockWelfordProblem<typename Problem::ComputeDataType,
typename Problem::ComputeDataType, typename Problem::ComputeDataType,
typename Problem::BlockShape>; typename Problem::BlockShape,
Problem::Traits::kFastFDiv>;
return BlockWelford<P_>{}; return BlockWelford<P_>{};
} }
...@@ -57,7 +58,8 @@ struct Layernorm2dFwdPipelineDefaultPolicy ...@@ -57,7 +58,8 @@ struct Layernorm2dFwdPipelineDefaultPolicy
{ {
using P_ = BlockWelfordProblem<typename Problem::ComputeDataType, using P_ = BlockWelfordProblem<typename Problem::ComputeDataType,
typename Problem::ComputeDataType, typename Problem::ComputeDataType,
typename Problem::BlockShape>; typename Problem::BlockShape,
Problem::Traits::kFastFDiv>;
return BlockWelfordSync<P_>{}; return BlockWelfordSync<P_>{};
} }
...@@ -67,7 +69,8 @@ struct Layernorm2dFwdPipelineDefaultPolicy ...@@ -67,7 +69,8 @@ struct Layernorm2dFwdPipelineDefaultPolicy
{ {
using P_ = BlockWelfordProblem<typename Problem::ComputeDataType, using P_ = BlockWelfordProblem<typename Problem::ComputeDataType,
typename Problem::ComputeDataType, typename Problem::ComputeDataType,
typename Problem::BlockShape>; typename Problem::BlockShape,
Problem::Traits::kFastFDiv>;
return BlockWelfordCrossWarpSync<P_>{}; return BlockWelfordCrossWarpSync<P_>{};
} }
...@@ -79,7 +82,8 @@ struct Layernorm2dFwdPipelineDefaultPolicy ...@@ -79,7 +82,8 @@ struct Layernorm2dFwdPipelineDefaultPolicy
{ {
using P_ = BlockWelfordProblem<typename Problem::ComputeDataType, using P_ = BlockWelfordProblem<typename Problem::ComputeDataType,
typename Problem::ComputeDataType, typename Problem::ComputeDataType,
typename Problem::BlockShape>; typename Problem::BlockShape,
Problem::Traits::kFastFDiv>;
using block_welford = BlockWelford<P_>; using block_welford = BlockWelford<P_>;
using x_block_tile = using x_block_tile =
......
include/ck_tile/ops/layernorm2d/pipeline/layernorm2d_fwd_pipeline_one_pass.hpp
View file @ b6d3aa5d
...@@ -36,6 +36,7 @@ struct Layernorm2dFwdPipelineOnePass ...@@ -36,6 +36,7 @@ struct Layernorm2dFwdPipelineOnePass
static constexpr bool kNeedCrossWarpSync = Problem::kNeedCrossWarpSync; static constexpr bool kNeedCrossWarpSync = Problem::kNeedCrossWarpSync;
static constexpr bool kPadM = false; // TODO - BlockLayernorm2dFwdProblem::kPadM static constexpr bool kPadM = false; // TODO - BlockLayernorm2dFwdProblem::kPadM
static constexpr bool kPadN = Problem::Traits::kPadN; static constexpr bool kPadN = Problem::Traits::kPadN;
static constexpr bool kFastFDiv = Problem::Traits::kFastFDiv;
static constexpr auto kFusedAdd = Problem::Traits::kFusedAdd; static constexpr auto kFusedAdd = Problem::Traits::kFusedAdd;
static constexpr auto kFusedQuant = Problem::Traits::kFusedQuant; static constexpr auto kFusedQuant = Problem::Traits::kFusedQuant;
...@@ -120,12 +121,20 @@ struct Layernorm2dFwdPipelineOnePass ...@@ -120,12 +121,20 @@ struct Layernorm2dFwdPipelineOnePass
auto [mean, var] = block_welford(acc, cur_count, max_count); auto [mean, var] = block_welford(acc, cur_count, max_count);
block_welford_sync(mean, var, cur_count); block_welford_sync(mean, var, cur_count);
block_welford_cross_warp_sync(mean, var, cur_count, smem); block_welford_cross_warp_sync(mean, var, cur_count, smem);
block_tile_welford_post_scale_var(var, cur_count); block_tile_welford_post_scale_var(var, cur_count, constant<kFastFDiv>{});
// compute inv-std // compute inv-std
auto inv_std = tile_elementwise_in( auto inv_std = tile_elementwise_in(
[&](const auto& v_) { [&](const auto& v_) {
return type_convert<ComputeDataType>(1.0f) / (sqrt(v_ + epsilon)); if(kFastFDiv && std::is_same_v<ComputeDataType, float>)
{
return type_convert<ComputeDataType>(1.0f) *
__builtin_amdgcn_rcpf(sqrt(v_ + epsilon));
}
else
{
return type_convert<ComputeDataType>(1.0f) / sqrt(v_ + epsilon);
}
}, },
var); var);
......
include/ck_tile/ops/layernorm2d/pipeline/layernorm2d_fwd_pipeline_two_pass.hpp
View file @ b6d3aa5d
...@@ -35,6 +35,7 @@ struct Layernorm2dFwdPipelineTwoPass ...@@ -35,6 +35,7 @@ struct Layernorm2dFwdPipelineTwoPass
static constexpr bool kNeedCrossWarpSync = Problem::kNeedCrossWarpSync; static constexpr bool kNeedCrossWarpSync = Problem::kNeedCrossWarpSync;
static constexpr bool kPadM = false; // TODO - BlockLayernorm2dFwdProblem::kPadM static constexpr bool kPadM = false; // TODO - BlockLayernorm2dFwdProblem::kPadM
static constexpr bool kPadN = Problem::Traits::kPadN; static constexpr bool kPadN = Problem::Traits::kPadN;
static constexpr bool kFastFDiv = Problem::Traits::kFastFDiv;
static constexpr auto kFusedAdd = Problem::Traits::kFusedAdd; static constexpr auto kFusedAdd = Problem::Traits::kFusedAdd;
static constexpr auto kFusedQuant = Problem::Traits::kFusedQuant; static constexpr auto kFusedQuant = Problem::Traits::kFusedQuant;
...@@ -137,15 +138,22 @@ struct Layernorm2dFwdPipelineTwoPass ...@@ -137,15 +138,22 @@ struct Layernorm2dFwdPipelineTwoPass
block_welford_sync(mean, var, cur_count); block_welford_sync(mean, var, cur_count);
block_welford_cross_warp_sync(mean, var, cur_count, smem); block_welford_cross_warp_sync(mean, var, cur_count, smem);
block_tile_welford_post_scale_var(var, cur_count); block_tile_welford_post_scale_var(var, cur_count, constant<kFastFDiv>{});
// compute inv-std // compute inv-std
auto inv_std = tile_elementwise_in( auto inv_std = tile_elementwise_in(
[&](const auto& v_) { [&](const auto& v_) {
return type_convert<ComputeDataType>(1.0f) / (sqrt(v_ + epsilon)); if(kFastFDiv && std::is_same_v<ComputeDataType, float>)
{
return type_convert<ComputeDataType>(1.0f) *
__builtin_amdgcn_rcpf(sqrt(v_ + epsilon));
}
else
{
return type_convert<ComputeDataType>(1.0f) / sqrt(v_ + epsilon);
}
}, },
var); var);
if constexpr(kSaveMean) if constexpr(kSaveMean)
store_tile(mean_window, cast_tile<MeanDataType>(mean)); store_tile(mean_window, cast_tile<MeanDataType>(mean));
if constexpr(kSaveInvStd) if constexpr(kSaveInvStd)
......
include/ck_tile/ops/layernorm2d/pipeline/layernorm2d_fwd_traits.hpp
View file @ b6d3aa5d
...@@ -39,6 +39,7 @@ template<> struct Layernorm2dFusedQuantEnumName<Layernorm2dFusedQuantEnum::SMOOT ...@@ -39,6 +39,7 @@ template<> struct Layernorm2dFusedQuantEnumName<Layernorm2dFusedQuantEnum::SMOOT
template <bool kPadN_, template <bool kPadN_,
bool kSaveMeanInvStd_, bool kSaveMeanInvStd_,
bool kFastFDiv_,
bool kTwoPass_, bool kTwoPass_,
Layernorm2dFusedAddEnum kFusedAdd_, Layernorm2dFusedAddEnum kFusedAdd_,
Layernorm2dFusedQuantEnum kFusedQuant_> Layernorm2dFusedQuantEnum kFusedQuant_>
...@@ -46,6 +47,7 @@ struct Layernorm2dFwdTraits ...@@ -46,6 +47,7 @@ struct Layernorm2dFwdTraits
{ {
static constexpr bool kPadN = kPadN_; static constexpr bool kPadN = kPadN_;
static constexpr bool kSaveMeanInvStd = kSaveMeanInvStd_; static constexpr bool kSaveMeanInvStd = kSaveMeanInvStd_;
static constexpr bool kFastFDiv = kFastFDiv_;
static constexpr bool kTwoPass = kTwoPass_; static constexpr bool kTwoPass = kTwoPass_;
static constexpr Layernorm2dFusedAddEnum kFusedAdd = kFusedAdd_; static constexpr Layernorm2dFusedAddEnum kFusedAdd = kFusedAdd_;
static constexpr Layernorm2dFusedQuantEnum kFusedQuant = kFusedQuant_; static constexpr Layernorm2dFusedQuantEnum kFusedQuant = kFusedQuant_;
......
include/ck_tile/ops/moe_sorting.hpp 0 → 100644
View file @ b6d3aa5d
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/ops/fused_moe/kernel/moe_sorting_kernel.hpp"
#include "ck_tile/ops/fused_moe/pipeline/moe_sorting_pipeline.hpp"
#include "ck_tile/ops/fused_moe/pipeline/moe_sorting_policy.hpp"
#include "ck_tile/ops/fused_moe/pipeline/moe_sorting_problem.hpp"
#include "ck_tile/ops/common/generic_2d_block_shape.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp"
include/ck_tile/ops/welford/block/block_welford.hpp
View file @ b6d3aa5d
...@@ -11,9 +11,10 @@ namespace ck_tile { ...@@ -11,9 +11,10 @@ namespace ck_tile {
template <typename Problem_, typename Policy_ = void> template <typename Problem_, typename Policy_ = void>
struct BlockWelford struct BlockWelford
{ {
using Problem = remove_cvref_t<Problem_>; using Problem = remove_cvref_t<Problem_>;
using XDataType = typename Problem::XDataType; using XDataType = typename Problem::XDataType;
using ComputeDataType = typename Problem::ComputeDataType; using ComputeDataType = typename Problem::ComputeDataType;
static constexpr bool kFastFDiv = Problem::kFastFDiv;
CK_TILE_DEVICE constexpr BlockWelford() {} CK_TILE_DEVICE constexpr BlockWelford() {}
...@@ -46,8 +47,11 @@ struct BlockWelford ...@@ -46,8 +47,11 @@ struct BlockWelford
auto x = ck_tile::type_convert<ComputeDataType>(x_tensor[in_dstr_idx]); auto x = ck_tile::type_convert<ComputeDataType>(x_tensor[in_dstr_idx]);
welford_update( welford_update(mean_tensor(out_dstr_idx),
mean_tensor(out_dstr_idx), var_tensor(out_dstr_idx), x, cur_count_); var_tensor(out_dstr_idx),
x,
cur_count_,
constant<kFastFDiv>{});
}); });
} }
}); });
...@@ -89,7 +93,8 @@ struct BlockWelford ...@@ -89,7 +93,8 @@ struct BlockWelford
template <typename Problem_, typename Policy_ = void> template <typename Problem_, typename Policy_ = void>
struct BlockWelfordSync struct BlockWelfordSync
{ {
using Problem = remove_cvref_t<Problem_>; using Problem = remove_cvref_t<Problem_>;
static constexpr bool kFastFDiv = Problem::kFastFDiv;
template <typename MeanDistributedTensor_, typename VarDistributedTensor_> template <typename MeanDistributedTensor_, typename VarDistributedTensor_>
CK_TILE_DEVICE void CK_TILE_DEVICE void
...@@ -157,7 +162,8 @@ struct BlockWelfordSync ...@@ -157,7 +162,8 @@ struct BlockWelfordSync
v_local_count, v_local_count,
v_remote_mean, v_remote_mean,
v_remote_var, v_remote_var,
v_remote_count); v_remote_count,
constant<kFastFDiv>{});
}); });
} }
}); });
...@@ -173,8 +179,9 @@ struct BlockWelfordSync ...@@ -173,8 +179,9 @@ struct BlockWelfordSync
template <typename Problem_, typename Policy_ = void> template <typename Problem_, typename Policy_ = void>
struct BlockWelfordCrossWarpSync struct BlockWelfordCrossWarpSync
{ {
using Problem = remove_cvref_t<Problem_>; using Problem = remove_cvref_t<Problem_>;
using BlockShape = typename Problem::BlockShape; using BlockShape = typename Problem::BlockShape;
static constexpr bool kFastFDiv = Problem::kFastFDiv;
template <typename MeanDistributedTensor_> template <typename MeanDistributedTensor_>
CK_TILE_DEVICE static constexpr index_t GetReduceWarps() CK_TILE_DEVICE static constexpr index_t GetReduceWarps()
...@@ -304,7 +311,8 @@ struct BlockWelfordCrossWarpSync ...@@ -304,7 +311,8 @@ struct BlockWelfordCrossWarpSync
v_local_count, v_local_count,
v_remote_mean, v_remote_mean,
v_remote_var, v_remote_var,
v_remote_count); v_remote_count,
constant<kFastFDiv>{});
}); });
mean_tensor.get_thread_buffer()(i_0) = v_local_mean; mean_tensor.get_thread_buffer()(i_0) = v_local_mean;
...@@ -351,12 +359,23 @@ CK_TILE_DEVICE constexpr index_t block_tile_welford_calculate_max_count(int row_ ...@@ -351,12 +359,23 @@ CK_TILE_DEVICE constexpr index_t block_tile_welford_calculate_max_count(int row_
} }
// Note: this function must be called after all the computation // Note: this function must be called after all the computation
template <typename VarDistributedTensor_> template <typename VarDistributedTensor_, bool FastFdiv_ = false>
CK_TILE_DEVICE constexpr void block_tile_welford_post_scale_var(VarDistributedTensor_& var_tensor, CK_TILE_DEVICE constexpr void block_tile_welford_post_scale_var(VarDistributedTensor_& var_tensor,
int count) int count,
bool_constant<FastFdiv_> = {})
{ {
using DataType = typename VarDistributedTensor_::DataType; using DataType = typename VarDistributedTensor_::DataType;
tile_elementwise_inout([&count](auto& x) { x = x / type_convert<DataType>(count); }, tile_elementwise_inout(
var_tensor); [&count](auto& x) {
if(FastFdiv_ && std::is_same_v<DataType, float>)
{
x = x * __builtin_amdgcn_rcpf(type_convert<DataType>(count));
}
else
{
x = x / type_convert<DataType>(count);
}
},
var_tensor);
} }
} // namespace ck_tile } // namespace ck_tile
include/ck_tile/ops/welford/block/block_welford_problem.hpp
View file @ b6d3aa5d
...@@ -7,12 +7,13 @@ ...@@ -7,12 +7,13 @@
namespace ck_tile { namespace ck_tile {
template <typename XDataType_, typename ComputeDataType_, typename BlockShape_> template <typename XDataType_, typename ComputeDataType_, typename BlockShape_, bool kFastFDiv_>
struct BlockWelfordProblem struct BlockWelfordProblem
{ {
using XDataType = remove_cvref_t<XDataType_>; using XDataType = remove_cvref_t<XDataType_>;
using ComputeDataType = remove_cvref_t<ComputeDataType_>; using ComputeDataType = remove_cvref_t<ComputeDataType_>;
using BlockShape = remove_cvref_t<BlockShape_>; using BlockShape = remove_cvref_t<BlockShape_>;
static constexpr bool kFastFDiv = kFastFDiv_;
}; };
} // namespace ck_tile } // namespace ck_tile
include/ck_tile/ops/welford/thread/thread_welford.hpp
View file @ b6d3aa5d
...@@ -7,25 +7,46 @@ ...@@ -7,25 +7,46 @@
namespace ck_tile { namespace ck_tile {
template <typename T> template <typename T, bool kFastFDiv = false>
CK_TILE_DEVICE void welford_update(T& mean, T& var, T x, int count) CK_TILE_DEVICE void welford_update(T& mean, T& var, T x, int count, bool_constant<kFastFDiv> = {})
{ {
// TODO: check nan? maybe no // TODO: check nan? maybe no
T delta = x - mean; T delta = x - mean;
mean += delta / count; if(kFastFDiv && std::is_same_v<T, float>)
{
mean += delta * __builtin_amdgcn_rcpf(count);
}
else
{
mean += delta / count;
}
T delta2 = x - mean; T delta2 = x - mean;
var += delta * delta2; var += delta * delta2;
} }
template <typename T> template <typename T, bool kFastFDiv = false>
CK_TILE_DEVICE static void CK_TILE_DEVICE static void welford_merge(T& mean_a,
welford_merge(T& mean_a, T& var_a, int& count_a, T mean_b, T var_b, int count_b) T& var_a,
int& count_a,
T mean_b,
T var_b,
int count_b,
bool_constant<kFastFDiv> = {})
{ {
int count = count_a + count_b; int count = count_a + count_b;
T count_ = type_convert<T>(count); T count_ = type_convert<T>(count);
T count_a_ = type_convert<T>(count_a); T count_a_ = type_convert<T>(count_a);
T count_b_ = type_convert<T>(count_b); T count_b_ = type_convert<T>(count_b);
T count_b_over_count = count == 0 ? type_convert<T>(0) : count_b_ / count_; T count_b_over_count;
if(kFastFDiv && std::is_same_v<T, float>)
{
count_b_over_count =
count == 0 ? type_convert<T>(0) : count_b_ * __builtin_amdgcn_rcpf(count_);
}
else
{
count_b_over_count = count == 0 ? type_convert<T>(0) : count_b_ / count_;
}
T delta = mean_b - mean_a; T delta = mean_b - mean_a;
mean_a += delta * count_b_over_count; mean_a += delta * count_b_over_count;
......
library/include/ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_two_stage_xdl_instance.hpp
View file @ b6d3aa5d
...@@ -39,7 +39,25 @@ template <ck::index_t NDimSpatial, ...@@ -39,7 +39,25 @@ template <ck::index_t NDimSpatial,
ConvolutionBackwardWeightSpecialization ConvSpec, ConvolutionBackwardWeightSpecialization ConvSpec,
BlockGemmPipelineScheduler Scheduler, BlockGemmPipelineScheduler Scheduler,
BlockGemmPipelineVersion PipelineVersion> BlockGemmPipelineVersion PipelineVersion>
using device_grouped_conv_bwd_weight_two_stage_xdl_c_shuffle_f16_instances = std::tuple< using device_grouped_conv_bwd_weight_two_stage_nhwgc_xdl_c_shuffle_f16_generic_instances =
std::tuple<
// clang-format off
//#########################################| Num| InLayout| WeiLayout| OutLayout| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer| BlockGemm| BlockGemm| NumGroups|
//#########################################| Dim| | | | Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector| Pipeline| Pipeline| ToMerge|
//#########################################| Spatial| | | | | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl| Scheduler| Version| |
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| | | | |
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 16, 16, 32, 8, 16, 16, 1, 1, S<4, 8, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 1, 4, false, S<4, 8, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 1, 4, false, 1, 1, S<1, 8, 1, 8>, 1, Scheduler, PipelineVersion, 1>
// clang-format on
>;
template <ck::index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename ELayout,
ConvolutionBackwardWeightSpecialization ConvSpec,
BlockGemmPipelineScheduler Scheduler,
BlockGemmPipelineVersion PipelineVersion>
using device_grouped_conv_bwd_weight_two_stage_nhwgc_xdl_c_shuffle_f16_instances = std::tuple<
// clang-format off // clang-format off
//#########################################| Num| InLayout| WeiLayout| OutLayout| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer| BlockGemm| BlockGemm| NumGroups| //#########################################| Num| InLayout| WeiLayout| OutLayout| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer| BlockGemm| BlockGemm| NumGroups|
//#########################################| Dim| | | | Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector| Pipeline| Pipeline| ToMerge| //#########################################| Dim| | | | Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector| Pipeline| Pipeline| ToMerge|
...@@ -64,7 +82,25 @@ template <ck::index_t NDimSpatial, ...@@ -64,7 +82,25 @@ template <ck::index_t NDimSpatial,
ConvolutionBackwardWeightSpecialization ConvSpec, ConvolutionBackwardWeightSpecialization ConvSpec,
BlockGemmPipelineScheduler Scheduler, BlockGemmPipelineScheduler Scheduler,
BlockGemmPipelineVersion PipelineVersion> BlockGemmPipelineVersion PipelineVersion>
using device_grouped_conv_bwd_weight_two_stage_xdl_c_shuffle_bf16_instances = std::tuple< using device_grouped_conv_bwd_weight_two_stage_nhwgc_xdl_c_shuffle_bf16_generic_instances =
std::tuple<
// clang-format off
//#########################################| Num| InLayout| WeiLayout| OutLayout| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer| BlockGemm| BlockGemm| NumGroups|
//#########################################| Dim| | | | Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector| Pipeline| Pipeline| ToMerge|
//#########################################| Spatial| | | | | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl| Scheduler| Version| |
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| | | | |
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 16, 16, 32, 8, 16, 16, 1, 1, S<4, 8, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 1, 4, false, S<4, 8, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 1, 4, false, 1, 1, S<1, 8, 1, 8>, 1, Scheduler, PipelineVersion, 1>
// clang-format on
>;
template <ck::index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename ELayout,
ConvolutionBackwardWeightSpecialization ConvSpec,
BlockGemmPipelineScheduler Scheduler,
BlockGemmPipelineVersion PipelineVersion>
using device_grouped_conv_bwd_weight_two_stage_nhwgc_xdl_c_shuffle_bf16_instances = std::tuple<
// clang-format off // clang-format off
//#########################################| Num| InLayout| WeiLayout| OutLayout| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer| BlockGemm| BlockGemm| NumGroups| //#########################################| Num| InLayout| WeiLayout| OutLayout| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer| BlockGemm| BlockGemm| NumGroups|
//#########################################| Dim| | | | Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector| Pipeline| Pipeline| ToMerge| //#########################################| Dim| | | | Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector| Pipeline| Pipeline| ToMerge|
...@@ -82,6 +118,24 @@ using device_grouped_conv_bwd_weight_two_stage_xdl_c_shuffle_bf16_instances = st ...@@ -82,6 +118,24 @@ using device_grouped_conv_bwd_weight_two_stage_xdl_c_shuffle_bf16_instances = st
// clang-format on // clang-format on
>; >;
template <ck::index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename ELayout,
ConvolutionBackwardWeightSpecialization ConvSpec,
BlockGemmPipelineScheduler Scheduler,
BlockGemmPipelineVersion PipelineVersion>
using device_grouped_conv_bwd_weight_two_stage_ngchw_xdl_c_shuffle_f16_generic_instances =
std::tuple<
// clang-format off
//#########################################| Num| InLayout| WeiLayout| OutLayout| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer| BlockGemm| BlockGemm| NumGroups|
//#########################################| Dim| | | | Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector| Pipeline| Pipeline| ToMerge|
//#########################################| Spatial| | | | | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl| Scheduler| Version| |
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| | | | |
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 16, 16, 32, 8, 16, 16, 1, 1, S<4, 8, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 1, 4, false, S<4, 8, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 1, 4, false, 1, 1, S<1, 8, 1, 8>, 1, Scheduler, PipelineVersion, 1, F16, F16, 1, 1>
// clang-format on
>;
// NGCHW requires transpose, we use vector loads and stores params for them // NGCHW requires transpose, we use vector loads and stores params for them
template <ck::index_t NDimSpatial, template <ck::index_t NDimSpatial,
typename ALayout, typename ALayout,
...@@ -122,6 +176,24 @@ using device_grouped_conv_bwd_weight_two_stage_ngchw_xdl_c_shuffle_f16_instances ...@@ -122,6 +176,24 @@ using device_grouped_conv_bwd_weight_two_stage_ngchw_xdl_c_shuffle_f16_instances
// clang-format on // clang-format on
>; >;
template <ck::index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename ELayout,
ConvolutionBackwardWeightSpecialization ConvSpec,
BlockGemmPipelineScheduler Scheduler,
BlockGemmPipelineVersion PipelineVersion>
using device_grouped_conv_bwd_weight_two_stage_ngchw_xdl_c_shuffle_bf16_generic_instances =
std::tuple<
// clang-format off
//#########################################| Num| InLayout| WeiLayout| OutLayout| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer| BlockGemm| BlockGemm| NumGroups|
//#########################################| Dim| | | | Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector| Pipeline| Pipeline| ToMerge|
//#########################################| Spatial| | | | | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl| Scheduler| Version| |
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| | | | |
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 16, 16, 32, 8, 16, 16, 1, 1, S<4, 8, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 1, 4, false, S<4, 8, 1>, S<2, 0, 1>, S<1, 0, 2>, 1, 1, 4, false, 1, 1, S<1, 8, 1, 8>, 1, Scheduler, PipelineVersion, 1, BF16, BF16, 1, 1>
// clang-format on
>;
template <ck::index_t NDimSpatial, template <ck::index_t NDimSpatial,
typename ALayout, typename ALayout,
typename BLayout, typename BLayout,
......
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_backward_weight.hpp
View file @ b6d3aa5d
...@@ -352,6 +352,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe ...@@ -352,6 +352,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
{ {
add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_instances( add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_instances(
op_ptrs); op_ptrs);
add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pipev1_instances(
op_ptrs);
add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pipev2_instances( add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pipev2_instances(
op_ptrs); op_ptrs);
add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pipev5_instances( add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pipev5_instances(
...@@ -375,6 +377,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe ...@@ -375,6 +377,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
{ {
add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_instances( add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_instances(
op_ptrs); op_ptrs);
add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_bf16_pipev1_instances(
op_ptrs);
add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_bf16_pipev2_instances( add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_bf16_pipev2_instances(
op_ptrs); op_ptrs);
add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_bf16_pipev5_instances( add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_bf16_pipev5_instances(
...@@ -390,6 +394,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe ...@@ -390,6 +394,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
is_same_v<OutDataType, half_t> && is_same_v<ComputeTypeA, half_t> && is_same_v<OutDataType, half_t> && is_same_v<ComputeTypeA, half_t> &&
is_same_v<ComputeTypeB, half_t>) is_same_v<ComputeTypeB, half_t>)
{ {
add_device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_f16_pipev1_instances(
op_ptrs);
add_device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_f16_pipev2_instances( add_device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_f16_pipev2_instances(
op_ptrs); op_ptrs);
add_device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_f16_pipev5_instances( add_device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_f16_pipev5_instances(
...@@ -403,6 +409,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe ...@@ -403,6 +409,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
is_same_v<ComputeTypeA, ck::bhalf_t> && is_same_v<ComputeTypeA, ck::bhalf_t> &&
is_same_v<ComputeTypeB, ck::bhalf_t>) is_same_v<ComputeTypeB, ck::bhalf_t>)
{ {
add_device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_bf16_pipev1_instances(
op_ptrs);
add_device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_bf16_pipev2_instances( add_device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_bf16_pipev2_instances(
op_ptrs); op_ptrs);
add_device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_bf16_pipev5_instances( add_device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_bf16_pipev5_instances(
...@@ -464,6 +472,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe ...@@ -464,6 +472,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
{ {
add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_instances( add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_instances(
op_ptrs); op_ptrs);
add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_f16_pipev1_instances(
op_ptrs);
add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_f16_pipev2_instances( add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_f16_pipev2_instances(
op_ptrs); op_ptrs);
add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_f16_pipev5_instances( add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_f16_pipev5_instances(
...@@ -487,6 +497,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe ...@@ -487,6 +497,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
{ {
add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_instances( add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_instances(
op_ptrs); op_ptrs);
add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_pipev1_instances(
op_ptrs);
add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_pipev2_instances( add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_pipev2_instances(
op_ptrs); op_ptrs);
add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_pipev5_instances( add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_pipev5_instances(
...@@ -511,6 +523,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe ...@@ -511,6 +523,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
is_same_v<OutDataType, half_t> && is_same_v<ComputeTypeA, half_t> && is_same_v<OutDataType, half_t> && is_same_v<ComputeTypeA, half_t> &&
is_same_v<ComputeTypeB, half_t>) is_same_v<ComputeTypeB, half_t>)
{ {
add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ngcdhw_gkzyxc_ngkdhw_f16_pipev1_instances(
op_ptrs);
add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ngcdhw_gkzyxc_ngkdhw_f16_pipev2_instances( add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ngcdhw_gkzyxc_ngkdhw_f16_pipev2_instances(
op_ptrs); op_ptrs);
add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ngcdhw_gkzyxc_ngkdhw_f16_pipev5_instances( add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ngcdhw_gkzyxc_ngkdhw_f16_pipev5_instances(
...@@ -524,6 +538,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe ...@@ -524,6 +538,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
is_same_v<ComputeTypeA, ck::bhalf_t> && is_same_v<ComputeTypeA, ck::bhalf_t> &&
is_same_v<ComputeTypeB, ck::bhalf_t>) is_same_v<ComputeTypeB, ck::bhalf_t>)
{ {
add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ngcdhw_gkzyxc_ngkdhw_bf16_pipev1_instances(
op_ptrs);
add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ngcdhw_gkzyxc_ngkdhw_bf16_pipev2_instances( add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ngcdhw_gkzyxc_ngkdhw_bf16_pipev2_instances(
op_ptrs); op_ptrs);
add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ngcdhw_gkzyxc_ngkdhw_bf16_pipev5_instances( add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ngcdhw_gkzyxc_ngkdhw_bf16_pipev5_instances(
......
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_backward_weight_xdl.inc
View file @ b6d3aa5d
...@@ -113,6 +113,18 @@ void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_f32_bf16_in ...@@ -113,6 +113,18 @@ void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_f32_bf16_in
PassThrough, PassThrough,
PassThrough>>>& instances); PassThrough>>>& instances);
void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_bf16_pipev1_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NHWGC,
GKYXC,
NHWGK,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_bf16_pipev2_instances( void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_bf16_pipev2_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2, std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NHWGC, NHWGC,
...@@ -136,6 +148,19 @@ void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_bf16_p ...@@ -136,6 +148,19 @@ void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_bf16_p
PassThrough, PassThrough,
PassThrough, PassThrough,
PassThrough>>>& instances); PassThrough>>>& instances);
void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_bf16_pipev1_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NGCHW,
GKYXC,
NGKHW,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_bf16_pipev2_instances( void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_bf16_pipev2_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2, std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NGCHW, NGCHW,
...@@ -173,6 +198,18 @@ void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_instances( ...@@ -173,6 +198,18 @@ void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_instances(
PassThrough, PassThrough,
PassThrough>>>& instances); PassThrough>>>& instances);
void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pipev1_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NHWGC,
GKYXC,
NHWGK,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pipev2_instances( void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pipev2_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2, std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NHWGC, NHWGC,
...@@ -196,6 +233,19 @@ void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pi ...@@ -196,6 +233,19 @@ void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pi
PassThrough, PassThrough,
PassThrough, PassThrough,
PassThrough>>>& instances); PassThrough>>>& instances);
void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_f16_pipev1_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NGCHW,
GKYXC,
NGKHW,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_f16_pipev2_instances( void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_f16_pipev2_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2, std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NGCHW, NGCHW,
...@@ -298,6 +348,18 @@ void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_f32_bf16 ...@@ -298,6 +348,18 @@ void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_f32_bf16
PassThrough, PassThrough,
PassThrough>>>& instances); PassThrough>>>& instances);
void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_pipev1_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
NDHWGC,
GKZYXC,
NDHWGK,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_pipev2_instances( void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_pipev2_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3, std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
NDHWGC, NDHWGC,
...@@ -321,6 +383,19 @@ void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_bf1 ...@@ -321,6 +383,19 @@ void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_bf1
PassThrough, PassThrough,
PassThrough, PassThrough,
PassThrough>>>& instances); PassThrough>>>& instances);
void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ngcdhw_gkzyxc_ngkdhw_bf16_pipev1_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
NGCDHW,
GKZYXC,
NGKDHW,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ngcdhw_gkzyxc_ngkdhw_bf16_pipev2_instances( void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ngcdhw_gkzyxc_ngkdhw_bf16_pipev2_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3, std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
NGCDHW, NGCDHW,
...@@ -358,6 +433,18 @@ void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_instances ...@@ -358,6 +433,18 @@ void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_instances
PassThrough, PassThrough,
PassThrough>>>& instances); PassThrough>>>& instances);
void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_f16_pipev1_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
NDHWGC,
GKZYXC,
NDHWGK,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_f16_pipev2_instances( void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_f16_pipev2_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3, std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
NDHWGC, NDHWGC,
...@@ -381,6 +468,19 @@ void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_f16 ...@@ -381,6 +468,19 @@ void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_f16
PassThrough, PassThrough,
PassThrough, PassThrough,
PassThrough>>>& instances); PassThrough>>>& instances);
void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ngcdhw_gkzyxc_ngkdhw_f16_pipev1_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
NGCDHW,
GKZYXC,
NGKDHW,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ngcdhw_gkzyxc_ngkdhw_f16_pipev2_instances( void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ngcdhw_gkzyxc_ngkdhw_f16_pipev2_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3, std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
NGCDHW, NGCDHW,
......
library/include/ck/library/utility/check_err.hpp
View file @ b6d3aa5d
...@@ -24,7 +24,7 @@ namespace ck { ...@@ -24,7 +24,7 @@ namespace ck {
namespace utils { namespace utils {
template <typename ComputeDataType, typename OutDataType, typename AccDataType = ComputeDataType> template <typename ComputeDataType, typename OutDataType, typename AccDataType = ComputeDataType>
double get_relative_threshold(const int numberOfAccumulations = 1) double get_relative_threshold(const int number_of_accumulations = 1)
{ {
using F8 = ck::f8_t; using F8 = ck::f8_t;
using F16 = ck::half_t; using F16 = ck::half_t;
...@@ -79,13 +79,13 @@ double get_relative_threshold(const int numberOfAccumulations = 1) ...@@ -79,13 +79,13 @@ double get_relative_threshold(const int numberOfAccumulations = 1)
} }
else else
{ {
acc_error = std::pow(2, -NumericUtils<AccDataType>::mant) * 0.5 * numberOfAccumulations; acc_error = std::pow(2, -NumericUtils<AccDataType>::mant) * 0.5 * number_of_accumulations;
} }
return std::max(acc_error, midway_error); return std::max(acc_error, midway_error);
} }
template <typename ComputeDataType, typename OutDataType, typename AccDataType = ComputeDataType> template <typename ComputeDataType, typename OutDataType, typename AccDataType = ComputeDataType>
double get_absolute_threshold(const double max_possible_num, const int numberOfAccumulations = 1) double get_absolute_threshold(const double max_possible_num, const int number_of_accumulations = 1)
{ {
using F8 = ck::f8_t; using F8 = ck::f8_t;
using F16 = ck::half_t; using F16 = ck::half_t;
...@@ -142,7 +142,7 @@ double get_absolute_threshold(const double max_possible_num, const int numberOfA ...@@ -142,7 +142,7 @@ double get_absolute_threshold(const double max_possible_num, const int numberOfA
else else
{ {
acc_error = acc_error =
std::pow(2, expo - NumericUtils<AccDataType>::mant) * 0.5 * numberOfAccumulations; std::pow(2, expo - NumericUtils<AccDataType>::mant) * 0.5 * number_of_accumulations;
} }
return std::max(acc_error, midway_error); return std::max(acc_error, midway_error);
} }
......
library/src/tensor_operation_instance/gpu/CMakeLists.txt
View file @ b6d3aa5d
...@@ -88,19 +88,19 @@ function(add_instance_library INSTANCE_NAME) ...@@ -88,19 +88,19 @@ function(add_instance_library INSTANCE_NAME)
foreach(source IN LISTS ARGN) foreach(source IN LISTS ARGN)
set(INST_TARGETS ${SUPPORTED_GPU_TARGETS}) set(INST_TARGETS ${SUPPORTED_GPU_TARGETS})
if(source MATCHES "_xdl") if(source MATCHES "_xdl")
list(REMOVE_ITEM INST_TARGETS gfx900 gfx906 gfx906:xnack- gfx1030 gfx1100 gfx1101 gfx1102 gfx1103 gfx1200 gfx1201) list(REMOVE_ITEM INST_TARGETS gfx900 gfx906 gfx906:xnack- gfx1030 gfx1100 gfx1101 gfx1102 gfx1103 gfx1200 gfx1201 gfx10.3-generic gfx11-generic gfx12-generic)
elseif(source MATCHES "_wmma") elseif(source MATCHES "_wmma")
list(REMOVE_ITEM INST_TARGETS gfx900 gfx906 gfx906:xnack- gfx908:xnack+ gfx908:xnack- gfx90a:xnack+ gfx90a:xnack- gfx908 gfx90a gfx940 gfx941 gfx942 gfx1030 gfx950) list(REMOVE_ITEM INST_TARGETS gfx900 gfx906 gfx906:xnack- gfx908:xnack+ gfx908:xnack- gfx90a:xnack+ gfx90a:xnack- gfx908 gfx90a gfx940 gfx941 gfx942 gfx1030 gfx950)
elseif(source MATCHES "mha") elseif(source MATCHES "mha")
list(REMOVE_ITEM INST_TARGETS gfx900 gfx906 gfx906:xnack- gfx908:xnack- gfx908:xnack+ gfx908 gfx1030 gfx1100 gfx1101 gfx1102 gfx1103 gfx1200 gfx1201) list(REMOVE_ITEM INST_TARGETS gfx900 gfx906 gfx906:xnack- gfx908:xnack- gfx908:xnack+ gfx908 gfx1030 gfx1100 gfx1101 gfx1102 gfx1103 gfx1200 gfx1201 gfx10.3-generic gfx11-generic gfx12-generic)
endif() endif()
#only build the fp8 gemm instances for gfx908/90a if the build argument is set #only build the fp8 gemm instances for gfx908/90a if the build argument is set
if(NOT CK_USE_FP8_ON_UNSUPPORTED_ARCH) if(NOT CK_USE_FP8_ON_UNSUPPORTED_ARCH)
if(source MATCHES "gemm_xdl_universal" AND source MATCHES "f8") if(source MATCHES "gemm_xdl_universal" AND source MATCHES "f8")
list(REMOVE_ITEM INST_TARGETS gfx900 gfx906 gfx906:xnack- gfx908:xnack- gfx908:xnack+ gfx90a:xnack+ gfx90a:xnack- gfx908 gfx90a gfx1030 gfx1100 gfx1101 gfx1102 gfx1103 gfx1200 gfx1201) list(REMOVE_ITEM INST_TARGETS gfx900 gfx906 gfx906:xnack- gfx908:xnack- gfx908:xnack+ gfx90a:xnack+ gfx90a:xnack- gfx908 gfx90a gfx1030 gfx1100 gfx1101 gfx1102 gfx1103 gfx1200 gfx1201 gfx10.3-generic gfx11-generic gfx12-generic)
endif() endif()
if(source MATCHES "gemm_multiply_multiply_f8") if(source MATCHES "gemm_multiply_multiply_f8")
list(REMOVE_ITEM INST_TARGETS gfx900 gfx906 gfx906:xnack- gfx908:xnack- gfx908:xnack+ gfx90a:xnack+ gfx90a:xnack- gfx908 gfx90a gfx1030 gfx1100 gfx1101 gfx1102 gfx1103 gfx1200 gfx1201) list(REMOVE_ITEM INST_TARGETS gfx900 gfx906 gfx906:xnack- gfx908:xnack- gfx908:xnack+ gfx90a:xnack+ gfx90a:xnack- gfx908 gfx90a gfx1030 gfx1100 gfx1101 gfx1102 gfx1103 gfx1200 gfx1201 gfx10.3-generic gfx11-generic gfx12-generic)
endif() endif()
endif() endif()
set(offload_targets) set(offload_targets)
......
library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/CMakeLists.txt
View file @ b6d3aa5d
...@@ -15,6 +15,10 @@ set(GROUPED_CONV2D_BWD_WEIGHT ...@@ -15,6 +15,10 @@ set(GROUPED_CONV2D_BWD_WEIGHT
xdl/device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_bf16_pipev5_instance.cpp xdl/device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_bf16_pipev5_instance.cpp
xdl/device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_bf16_pipev2_instance.cpp xdl/device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_bf16_pipev2_instance.cpp
xdl/device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_bf16_pipev5_instance.cpp xdl/device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_bf16_pipev5_instance.cpp
xdl/device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pipev1_instance.cpp
xdl/device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_f16_pipev1_instance.cpp
xdl/device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_bf16_pipev1_instance.cpp
xdl/device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_bf16_pipev1_instance.cpp
) )
if(DL_KERNELS) if(DL_KERNELS)
......
library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/xdl/device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_bf16_pipev1_instance.cpp 0 → 100644
View file @ b6d3aa5d
// 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_bwd_weight/device_grouped_conv_bwd_weight_two_stage_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_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_bf16_pipev1_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NGCHW,
GKYXC,
NGKHW,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv_bwd_weight_two_stage_ngchw_xdl_c_shuffle_bf16_generic_instances<
2,
NGCHW,
GKYXC,
NGKHW,
ConvBwdWeightDefault,
BlockGemmPipelineScheduler::Intrawave,
BlockGemmPipelineVersion::v1>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/xdl/device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_f16_pipev1_instance.cpp 0 → 100644
View file @ b6d3aa5d
// 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_bwd_weight/device_grouped_conv_bwd_weight_two_stage_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_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_f16_pipev1_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NGCHW,
GKYXC,
NGKHW,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv_bwd_weight_two_stage_ngchw_xdl_c_shuffle_f16_generic_instances<
2,
NGCHW,
GKYXC,
NGKHW,
ConvBwdWeightDefault,
BlockGemmPipelineScheduler::Intrawave,
BlockGemmPipelineVersion::v1>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/xdl/device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_bf16_pipev1_instance.cpp 0 → 100644
View file @ b6d3aa5d
// 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_bwd_weight/device_grouped_conv_bwd_weight_two_stage_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_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_bf16_pipev1_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NHWGC,
GKYXC,
NHWGK,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv_bwd_weight_two_stage_nhwgc_xdl_c_shuffle_bf16_generic_instances<
2,
NHWGC,
GKYXC,
NHWGK,
ConvBwdWeightDefault,
BlockGemmPipelineScheduler::Intrawave,
BlockGemmPipelineVersion::v1>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
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