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added padding of K into gemm_v2r3 (#887)

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* added kpad support into v2r3

* add generic instances

* fixed comments

* fixed mnk padding

* Update device_batched_gemm_xdl.hpp

---------

Co-authored-by: Jing Zhang <jizha@amd.com>
This commit is contained in:
zjing14
2023-09-06 10:15:52 -05:00
committed by GitHub
parent a61b8b785e
commit 3786bfe1cc
4 changed files with 72 additions and 11 deletions
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45
include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v2r3.hpp
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@@ -194,7 +194,7 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
StrideC{StrideC_},
MPadded{CalculateMPadded(M_)},
NPadded{CalculateNPadded(N_)},
K0{CalculateK0(K)}
K0{CalculateK0(K_)}
{
}
@@ -383,7 +383,7 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
__host__ static constexpr bool CalculateHasMainKBlockLoop(index_t K)
{
const index_t num_loop = K / (K0PerBlock * K1);
const index_t num_loop = math::integer_divide_ceil(K, K0PerBlock * K1);
return GridwiseGemmPipe::CalculateHasMainLoop(num_loop);
}
@@ -840,7 +840,25 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3_ext
}
}();
if constexpr(GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding)
if constexpr(GemmSpec == tensor_operation::device::GemmSpecialization::MNKPadding)
{
const auto K0Pad = math::integer_divide_ceil(K0, K0PerBlock) * K0PerBlock;
const auto KPad = K0Pad * K1Value;
const auto a_grid_desc_m_kpad = transform_tensor_descriptor(
a_grid_desc_m_k,
make_tuple(make_pass_through_transform(M), make_right_pad_transform(K, KPad - K)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
return transform_tensor_descriptor(
a_grid_desc_m_kpad,
make_tuple(make_unmerge_transform(make_tuple(K0Pad, K1Value)),
make_right_pad_transform(M, MPad - M)),
make_tuple(Sequence<1>{}, Sequence<0>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
}
else if constexpr(GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding)
{
return transform_tensor_descriptor(
a_grid_desc_m_k,
@@ -874,7 +892,26 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3_ext
}
}();
if constexpr(GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding)
if constexpr(GemmSpec == tensor_operation::device::GemmSpecialization::MNKPadding)
{
const auto K0Pad = math::integer_divide_ceil(K0, K0PerBlock) * K0PerBlock;
const auto KPad = K0Pad * K1Value;
const auto b_grid_desc_kpad_n = transform_tensor_descriptor(
b_grid_desc_k_n,
make_tuple(make_right_pad_transform(K, KPad - K), make_pass_through_transform(N)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
return transform_tensor_descriptor(
b_grid_desc_kpad_n,
make_tuple(make_unmerge_transform(make_tuple(K0Pad, K1Value)),
make_right_pad_transform(N, NPad - N)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
}
else if constexpr(GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding)
{
return transform_tensor_descriptor(
b_grid_desc_k_n,