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Unverified Commit 49180fd6 authored by Bartłomiej Kocot's avatar Bartłomiej Kocot Committed by GitHub
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Grouped 3d conv backward data support (#799) 

* Grouped 3d conv backward data support

* Fix comments
parent f82bd593
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Showing with 1581 additions and 606 deletions
include/ck/tensor_operation/gpu/device/convolution_backward_data_specialization.hpp
View file @ 49180fd6
......@@ -19,8 +19,7 @@ getConvBackwardDataSpecializationString(const ConvolutionBackwardDataSpecializat
switch(s)
{
case ConvolutionBackwardDataSpecialization::Default: return "Default";
case ConvolutionBackwardDataSpecialization::Filter1x1Stride1Pad0:
return "FFilter1x1Stride1Pad0";
case ConvolutionBackwardDataSpecialization::Filter1x1Stride1Pad0: return "Filter1x1Stride1Pad0";
default: return "Unrecognized specialization!";
}
}
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_data_multiple_d_xdl_cshuffle_v1.hpp
View file @ 49180fd6
......@@ -258,7 +258,8 @@ struct DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1
CDEElementwiseOp>
{
// FIXME
static_assert(NDimSpatial == 2, "wrong! only implemented for 2D now");
static_assert(NDimSpatial == 2 || NDimSpatial == 3,
"wrong! only implemented for 2D and 3D now");
using DeviceOp = DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1;
......@@ -491,130 +492,172 @@ struct DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1
compute_ptr_offset_of_batch_.BatchStrideDs_(i) = ds_g_n_c_wis_strides[i][0];
});
static constexpr auto NonSpatialDimsNum = Number<3>{};
static constexpr auto DIdx = Number<NonSpatialDimsNum>{};
static constexpr auto HIdx =
NDimSpatial == 2 ? Number<NonSpatialDimsNum>{} : Number<NonSpatialDimsNum + 1>{};
static constexpr auto WIdx = NDimSpatial == 2 ? Number<NonSpatialDimsNum + 1>{}
: Number<NonSpatialDimsNum + 2>{};
static constexpr auto ZIdx = Number<NonSpatialDimsNum>{};
static constexpr auto YIdx =
NDimSpatial == 2 ? Number<NonSpatialDimsNum>{} : Number<NonSpatialDimsNum + 1>{};
static constexpr auto XIdx = NDimSpatial == 2 ? Number<NonSpatialDimsNum + 1>{}
: Number<NonSpatialDimsNum + 2>{};
// problem definition
const index_t Y = b_g_k_c_xs_lengths[3];
const index_t X = b_g_k_c_xs_lengths[4];
const index_t Z = b_g_k_c_xs_lengths[ZIdx];
const index_t Y = b_g_k_c_xs_lengths[YIdx];
const index_t X = b_g_k_c_xs_lengths[XIdx];
const index_t ConvStrideH = conv_filter_strides_[0];
const index_t ConvStrideW = conv_filter_strides_[1];
const index_t ConvStrideD = conv_filter_strides[DIdx - NonSpatialDimsNum];
const index_t ConvStrideH = conv_filter_strides[HIdx - NonSpatialDimsNum];
const index_t ConvStrideW = conv_filter_strides[WIdx - NonSpatialDimsNum];
const index_t ConvDilationH = conv_filter_dilations_[0];
const index_t ConvDilationW = conv_filter_dilations_[1];
const index_t ConvDilationD = conv_filter_dilations[DIdx - NonSpatialDimsNum];
const index_t ConvDilationH = conv_filter_dilations[HIdx - NonSpatialDimsNum];
const index_t ConvDilationW = conv_filter_dilations[WIdx - NonSpatialDimsNum];
const auto GcdStrideDilationD = math::gcd(ConvStrideD, ConvDilationD);
const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
const auto ZTilde = NDimSpatial == 3 ? ConvStrideD / GcdStrideDilationD : 1;
const auto YTilde = ConvStrideH / GcdStrideDilationH;
const auto XTilde = ConvStrideW / GcdStrideDilationW;
for(index_t i_ytilde = 0; i_ytilde < YTilde; ++i_ytilde)
for(index_t i_ztilde = 0; i_ztilde < ZTilde; ++i_ztilde)
{
for(index_t i_xtilde = 0; i_xtilde < XTilde; ++i_xtilde)
{
// check slice is valid
const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilde, YTilde);
const auto XDotSlice = math::integer_divide_ceil(X - i_xtilde, XTilde);
if(YDotSlice * XDotSlice <= 0)
for(index_t i_ytilde = 0; i_ytilde < YTilde; ++i_ytilde)
{
for(index_t i_xtilde = 0; i_xtilde < XTilde; ++i_xtilde)
{
continue;
}
const auto a_grid_desc_ak0_m_ak1 =
transform_conv_to_gemm.template MakeADescriptor_AK0_M_AK1<ALayout>(
a_g_n_k_wos_lengths,
a_g_n_k_wos_strides,
b_g_k_c_xs_lengths,
b_g_k_c_xs_strides,
e_g_n_c_wis_lengths,
e_g_n_c_wis_strides,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
{i_ytilde, i_xtilde});
const auto b_grid_desc_bk0_n_bk1 =
transform_conv_to_gemm.template MakeBDescriptor_BK0_N_BK1<BLayout>(
a_g_n_k_wos_lengths,
a_g_n_k_wos_strides,
b_g_k_c_xs_lengths,
b_g_k_c_xs_strides,
e_g_n_c_wis_lengths,
e_g_n_c_wis_strides,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
{i_ytilde, i_xtilde});
DsGridDesc_M_N ds_grid_desc_m_n;
// populate Ds desc
static_for<0, NumDTensor, 1>{}([&](auto i) {
using DLayout = remove_cvref_t<tuple_element_t<i.value, DsLayout>>;
ds_grid_desc_m_n(i) =
transform_conv_to_gemm.template MakeCDescriptor_M_N<DLayout>(
// check slice is valid
const auto ZDotSlice =
NDimSpatial == 3 ? math::integer_divide_ceil(Z - i_ztilde, ZTilde) : 1;
const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilde, YTilde);
const auto XDotSlice = math::integer_divide_ceil(X - i_xtilde, XTilde);
if(YDotSlice * XDotSlice * ZDotSlice <= 0)
{
continue;
}
std::array<index_t, NDimSpatial> tildes;
if constexpr(NDimSpatial == 2)
{
tildes = {i_ytilde, i_xtilde};
}
else if constexpr(NDimSpatial == 3)
{
tildes = {i_ztilde, i_ytilde, i_xtilde};
}
else
{
throw std::runtime_error("wrong! only implemented for 2D and 3D now");
}
const auto a_grid_desc_ak0_m_ak1 =
transform_conv_to_gemm.template MakeADescriptor_AK0_M_AK1<ALayout>(
a_g_n_k_wos_lengths,
a_g_n_k_wos_strides,
b_g_k_c_xs_lengths,
b_g_k_c_xs_strides,
ds_g_n_c_wis_lengths[i],
ds_g_n_c_wis_strides[i],
e_g_n_c_wis_lengths,
e_g_n_c_wis_strides,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
{i_ytilde, i_xtilde});
});
const auto e_grid_desc_m_n =
transform_conv_to_gemm.template MakeCDescriptor_M_N<ELayout>(
a_g_n_k_wos_lengths,
a_g_n_k_wos_strides,
b_g_k_c_xs_lengths,
b_g_k_c_xs_strides,
e_g_n_c_wis_lengths,
e_g_n_c_wis_strides,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
{i_ytilde, i_xtilde});
// desc for problem definition
const auto a_grid_desc_m_k = transform_k0_m_k1_to_m_k(a_grid_desc_ak0_m_ak1);
const auto b_grid_desc_n_k = transform_k0_m_k1_to_m_k(b_grid_desc_bk0_n_bk1);
a_grid_desc_m_k_container_.push_back(a_grid_desc_m_k);
b_grid_desc_n_k_container_.push_back(b_grid_desc_n_k);
ds_grid_desc_m_n_container_.push_back(ds_grid_desc_m_n);
e_grid_desc_m_n_container_.push_back(e_grid_desc_m_n);
// desc for blockwise copy
a_grid_desc_ak0_m_ak1_container_.push_back(a_grid_desc_ak0_m_ak1);
b_grid_desc_bk0_n_bk1_container_.push_back(b_grid_desc_bk0_n_bk1);
// block-to-e-tile-map
auto block_2_etile_map =
GridwiseGemm::MakeDefaultBlock2ETileMap(e_grid_desc_m_n);
block_2_etile_map_container_.push_back(block_2_etile_map);
if(GridwiseGemm::CheckValidity(a_grid_desc_m_k,
b_grid_desc_n_k,
ds_grid_desc_m_n,
e_grid_desc_m_n,
block_2_etile_map))
{
ds_grid_desc_mblock_mperblock_nblock_nperblock_container_.push_back(
GridwiseGemm::MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
ds_grid_desc_m_n));
tildes);
e_grid_desc_mblock_mperblock_nblock_nperblock_container_.push_back(
GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
e_grid_desc_m_n));
const auto b_grid_desc_bk0_n_bk1 =
transform_conv_to_gemm.template MakeBDescriptor_BK0_N_BK1<BLayout>(
a_g_n_k_wos_lengths,
a_g_n_k_wos_strides,
b_g_k_c_xs_lengths,
b_g_k_c_xs_strides,
e_g_n_c_wis_lengths,
e_g_n_c_wis_strides,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
tildes);
DsGridDesc_M_N ds_grid_desc_m_n;
// populate Ds desc
static_for<0, NumDTensor, 1>{}([&](auto i) {
using DLayout = remove_cvref_t<tuple_element_t<i.value, DsLayout>>;
ds_grid_desc_m_n(i) =
transform_conv_to_gemm.template MakeCDescriptor_M_N<DLayout>(
a_g_n_k_wos_lengths,
a_g_n_k_wos_strides,
b_g_k_c_xs_lengths,
b_g_k_c_xs_strides,
ds_g_n_c_wis_lengths[i],
ds_g_n_c_wis_strides[i],
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
tildes);
});
const auto e_grid_desc_m_n =
transform_conv_to_gemm.template MakeCDescriptor_M_N<ELayout>(
a_g_n_k_wos_lengths,
a_g_n_k_wos_strides,
b_g_k_c_xs_lengths,
b_g_k_c_xs_strides,
e_g_n_c_wis_lengths,
e_g_n_c_wis_strides,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
tildes);
// desc for problem definition
const auto a_grid_desc_m_k =
transform_k0_m_k1_to_m_k(a_grid_desc_ak0_m_ak1);
const auto b_grid_desc_n_k =
transform_k0_m_k1_to_m_k(b_grid_desc_bk0_n_bk1);
a_grid_desc_m_k_container_.push_back(a_grid_desc_m_k);
b_grid_desc_n_k_container_.push_back(b_grid_desc_n_k);
ds_grid_desc_m_n_container_.push_back(ds_grid_desc_m_n);
e_grid_desc_m_n_container_.push_back(e_grid_desc_m_n);
// desc for blockwise copy
a_grid_desc_ak0_m_ak1_container_.push_back(a_grid_desc_ak0_m_ak1);
b_grid_desc_bk0_n_bk1_container_.push_back(b_grid_desc_bk0_n_bk1);
// block-to-e-tile-map
auto block_2_etile_map =
GridwiseGemm::MakeDefaultBlock2ETileMap(e_grid_desc_m_n);
block_2_etile_map_container_.push_back(block_2_etile_map);
if(GridwiseGemm::CheckValidity(a_grid_desc_m_k,
b_grid_desc_n_k,
ds_grid_desc_m_n,
e_grid_desc_m_n,
block_2_etile_map))
{
ds_grid_desc_mblock_mperblock_nblock_nperblock_container_.push_back(
GridwiseGemm::
MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
ds_grid_desc_m_n));
e_grid_desc_mblock_mperblock_nblock_nperblock_container_.push_back(
GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
e_grid_desc_m_n));
}
}
}
}
......@@ -803,7 +846,9 @@ struct DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1
// vector load for A matrix from global memory to LDS
if constexpr(is_same_v<ALayout, tensor_layout::convolution::GNHWK> ||
is_same_v<ALayout, tensor_layout::convolution::NHWGK>)
is_same_v<ALayout, tensor_layout::convolution::GNDHWK> ||
is_same_v<ALayout, tensor_layout::convolution::NHWGK> ||
is_same_v<ALayout, tensor_layout::convolution::NDHWGK>)
{
if(!(ABlockTransferSrcVectorDim == 2 && ConvK % ABlockTransferSrcScalarPerVector == 0))
{
......@@ -816,7 +861,8 @@ struct DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1
}
// vector load for B matrix from global memory to LDS
if constexpr(is_same_v<BLayout, tensor_layout::convolution::GKYXC>)
if constexpr(is_same_v<BLayout, tensor_layout::convolution::GKYXC> ||
is_same_v<BLayout, tensor_layout::convolution::GKZYXC>)
{
if(!(BBlockTransferSrcVectorDim == 1 && ConvC % BBlockTransferSrcScalarPerVector == 0))
{
......@@ -835,7 +881,9 @@ struct DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1
using DLayout = remove_cvref_t<tuple_element_t<i.value, DsLayout>>;
if constexpr(is_same_v<DLayout, tensor_layout::convolution::GNHWC> ||
is_same_v<DLayout, tensor_layout::convolution::GNDHWC> ||
is_same_v<DLayout, tensor_layout::convolution::NHWGC> ||
is_same_v<DLayout, tensor_layout::convolution::NDHWGC> ||
is_same_v<DLayout, tensor_layout::convolution::G_NHW_C> ||
is_same_v<DLayout, tensor_layout::convolution::GC> ||
is_same_v<DLayout, tensor_layout::convolution::G_C>)
......@@ -859,7 +907,9 @@ struct DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1
// vector store for E
if constexpr(is_same_v<ELayout, tensor_layout::convolution::GNHWC> ||
is_same_v<ELayout, tensor_layout::convolution::NHWGC>)
is_same_v<ELayout, tensor_layout::convolution::GNDHWC> ||
is_same_v<ELayout, tensor_layout::convolution::NHWGC> ||
is_same_v<ELayout, tensor_layout::convolution::NDHWGC>)
{
// vector store C matrix into global memory
if(!(ConvC % CDEBlockTransferScalarPerVector_NPerBlock == 0))
......
include/ck/tensor_operation/operator_transform/transform_conv_bwd_data_to_gemm_v1.hpp
View file @ 49180fd6
......@@ -18,32 +18,53 @@ template <
index_t NDimSpatial,
typename ALayout,
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization ConvBwdDataSpecialization>
constexpr auto
make_out_n_ho_wo_k_grid_desc(const index_t N,
const index_t Ho,
const index_t Wo,
const index_t K,
const std::array<index_t, NDimSpatial + 3>& out_g_n_k_wos_strides)
constexpr auto make_out_grid_desc(const index_t N,
const index_t Do,
const index_t Ho,
const index_t Wo,
const index_t K,
const std::array<index_t, NDimSpatial + 3>& out_g_n_k_wos_strides)
{
const auto KStride = Number<1>{};
if constexpr(is_same_v<ALayout, tensor_layout::convolution::NHWGK>)
{
const index_t NStride = out_g_n_k_wos_strides[1];
const index_t HiStride = out_g_n_k_wos_strides[3];
const index_t WiStride = out_g_n_k_wos_strides[4];
const auto CStride = Number<1>{};
if constexpr(ConvBwdDataSpecialization ==
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization::
Filter1x1Stride1Pad0)
{
return make_naive_tensor_descriptor(make_tuple(N * Ho * Wo, K),
make_tuple(WiStride, CStride));
make_tuple(WiStride, KStride));
}
else
{
return make_naive_tensor_descriptor(make_tuple(N, Ho, Wo, K),
make_tuple(NStride, HiStride, WiStride, CStride));
make_tuple(NStride, HiStride, WiStride, KStride));
}
}
else if constexpr(is_same_v<ALayout, tensor_layout::convolution::NDHWGK>)
{
const index_t NStride = out_g_n_k_wos_strides[1];
const index_t DoStride = out_g_n_k_wos_strides[3];
const index_t HoStride = out_g_n_k_wos_strides[4];
const index_t WoStride = out_g_n_k_wos_strides[5];
if constexpr(ConvBwdDataSpecialization ==
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization::
Filter1x1Stride1Pad0)
{
return make_naive_tensor_descriptor(make_tuple(N * Do * Ho * Wo, K),
make_tuple(WoStride, KStride));
}
else
{
return make_naive_tensor_descriptor(
make_tuple(N, Do, Ho, Wo, K),
make_tuple(NStride, DoStride, HoStride, WoStride, KStride));
}
}
else if constexpr(is_same_v<ALayout, tensor_layout::convolution::GNHWK>)
......@@ -60,12 +81,80 @@ make_out_n_ho_wo_k_grid_desc(const index_t N,
return make_naive_tensor_descriptor_packed(make_tuple(N, Ho, Wo, K));
}
}
else if constexpr(is_same_v<ALayout, tensor_layout::convolution::GNDHWK>)
{
// assume packed
if constexpr(ConvBwdDataSpecialization ==
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization::
Filter1x1Stride1Pad0)
{
return make_naive_tensor_descriptor_packed(make_tuple(N * Do * Ho * Wo, K));
}
else
{
return make_naive_tensor_descriptor_packed(make_tuple(N, Do, Ho, Wo, K));
}
}
else
{
throw std::runtime_error("wrong! unsupported layout: " + ALayout::name());
}
}
template <typename BLayout>
constexpr auto make_wei_grid_desc(
const index_t K, const index_t Z, const index_t Y, const index_t X, const index_t C)
{
if constexpr(is_same_v<BLayout, tensor_layout::convolution::GKYXC>)
{
return make_naive_tensor_descriptor_packed(make_tuple(K, Y, X, C));
}
else if constexpr(is_same_v<BLayout, tensor_layout::convolution::GKZYXC>)
{
return make_naive_tensor_descriptor_packed(make_tuple(K, Z, Y, X, C));
}
else
{
throw std::runtime_error("wrong! unsupported layout: " + BLayout::name());
}
}
template <index_t NDimSpatial, typename CLayout>
constexpr auto make_in_grid_desc(const index_t N,
const index_t Di,
const index_t Hi,
const index_t Wi,
const index_t C,
const std::array<index_t, NDimSpatial + 3>& in_g_n_c_wis_strides)
{
if constexpr(is_same_v<CLayout, tensor_layout::convolution::GNHWC> ||
is_same_v<CLayout, tensor_layout::convolution::NHWGC> ||
is_same_v<CLayout, tensor_layout::convolution::G_NHW_C>)
{
return make_naive_tensor_descriptor(make_tuple(N, Hi, Wi, C),
make_tuple(in_g_n_c_wis_strides[1],
in_g_n_c_wis_strides[3],
in_g_n_c_wis_strides[4],
in_g_n_c_wis_strides[2]));
}
else if constexpr(is_same_v<CLayout, tensor_layout::convolution::GNDHWC> ||
is_same_v<CLayout, tensor_layout::convolution::NDHWGC>)
{
return make_naive_tensor_descriptor(make_tuple(N, Di, Hi, Wi, C),
make_tuple(in_g_n_c_wis_strides[1],
in_g_n_c_wis_strides[3],
in_g_n_c_wis_strides[4],
in_g_n_c_wis_strides[5],
in_g_n_c_wis_strides[2]));
}
else
{
throw std::runtime_error("wrong! unsupported layout: " + CLayout::name());
}
}
} // namespace
template <
......@@ -82,10 +171,26 @@ struct TransformConvBwdDataToGemm_v1
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
static constexpr auto NonSpatialDimsNum = Number<3>{};
static constexpr auto DIdx = Number<NonSpatialDimsNum>{};
static constexpr auto HIdx =
NDimSpatial == 2 ? Number<NonSpatialDimsNum>{} : Number<NonSpatialDimsNum + 1>{};
static constexpr auto WIdx =
NDimSpatial == 2 ? Number<NonSpatialDimsNum + 1>{} : Number<NonSpatialDimsNum + 2>{};
static constexpr auto ZIdx = Number<NonSpatialDimsNum>{};
static constexpr auto YIdx =
NDimSpatial == 2 ? Number<NonSpatialDimsNum>{} : Number<NonSpatialDimsNum + 1>{};
static constexpr auto XIdx =
NDimSpatial == 2 ? Number<NonSpatialDimsNum + 1>{} : Number<NonSpatialDimsNum + 2>{};
template <typename ALayout,
typename std::enable_if<NDimSpatial == 2 &&
typename std::enable_if<(NDimSpatial == 2 || NDimSpatial == 3) &&
(is_same_v<ALayout, tensor_layout::convolution::GNHWK> ||
is_same_v<ALayout, tensor_layout::convolution::NHWGK>),
is_same_v<ALayout, tensor_layout::convolution::GNDHWK> ||
is_same_v<ALayout, tensor_layout::convolution::NHWGK> ||
is_same_v<ALayout, tensor_layout::convolution::NDHWGK>),
bool>::type = false>
static auto MakeADescriptor_AK0_M_AK1(
const std::array<index_t, NDimSpatial + 3>& out_g_n_k_wos_lengths,
......@@ -100,35 +205,43 @@ struct TransformConvBwdDataToGemm_v1
const std::array<index_t, NDimSpatial>& /* input_right_pads */,
const std::array<index_t, NDimSpatial>& tildes)
{
index_t i_ytilde = tildes[0];
index_t i_xtilde = tildes[1];
index_t i_ztilde = tildes[ZIdx - NonSpatialDimsNum];
index_t i_ytilde = tildes[YIdx - NonSpatialDimsNum];
index_t i_xtilde = tildes[XIdx - NonSpatialDimsNum];
const index_t N = in_g_n_c_wis_lengths[1];
const index_t K = wei_g_k_c_xs_lengths[1];
const index_t Hi = in_g_n_c_wis_lengths[3];
const index_t Wi = in_g_n_c_wis_lengths[4];
const index_t Di = NDimSpatial == 3 ? in_g_n_c_wis_lengths[DIdx] : 1;
const index_t Hi = in_g_n_c_wis_lengths[HIdx];
const index_t Wi = in_g_n_c_wis_lengths[WIdx];
const index_t Ho = out_g_n_k_wos_lengths[3];
const index_t Wo = out_g_n_k_wos_lengths[4];
const index_t Do = NDimSpatial == 3 ? out_g_n_k_wos_lengths[DIdx] : 1;
const index_t Ho = out_g_n_k_wos_lengths[HIdx];
const index_t Wo = out_g_n_k_wos_lengths[WIdx];
const index_t Y = wei_g_k_c_xs_lengths[3];
const index_t X = wei_g_k_c_xs_lengths[4];
const index_t Z = NDimSpatial == 3 ? wei_g_k_c_xs_lengths[ZIdx] : 1;
const index_t Y = wei_g_k_c_xs_lengths[YIdx];
const index_t X = wei_g_k_c_xs_lengths[XIdx];
const index_t InLeftPadH = input_left_pads[0];
const index_t InLeftPadW = input_left_pads[1];
const index_t InLeftPadD = input_left_pads[DIdx - NonSpatialDimsNum];
const index_t InLeftPadH = input_left_pads[HIdx - NonSpatialDimsNum];
const index_t InLeftPadW = input_left_pads[WIdx - NonSpatialDimsNum];
const index_t ConvStrideH = conv_filter_strides[0];
const index_t ConvStrideW = conv_filter_strides[1];
const index_t ConvStrideD = conv_filter_strides[DIdx - NonSpatialDimsNum];
const index_t ConvStrideH = conv_filter_strides[HIdx - NonSpatialDimsNum];
const index_t ConvStrideW = conv_filter_strides[WIdx - NonSpatialDimsNum];
const index_t ConvDilationH = conv_filter_dilations[0];
const index_t ConvDilationW = conv_filter_dilations[1];
const index_t ConvDilationD = conv_filter_dilations[DIdx - NonSpatialDimsNum];
const index_t ConvDilationH = conv_filter_dilations[HIdx - NonSpatialDimsNum];
const index_t ConvDilationW = conv_filter_dilations[WIdx - NonSpatialDimsNum];
const index_t AK0 = K / AK1;
const auto out_n_ho_wo_k_grid_desc =
make_out_n_ho_wo_k_grid_desc<NDimSpatial, ALayout, ConvBwdDataSpecialization>(
N, Ho, Wo, K, out_g_n_k_wos_strides);
// n_do_ho_wo_k for 3d or n_ho_wo_k for 2d
const auto out_grid_desc =
make_out_grid_desc<NDimSpatial, ALayout, ConvBwdDataSpecialization>(
N, Do, Ho, Wo, K, out_g_n_k_wos_strides);
if constexpr(ConvBwdDataSpecialization ==
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization::
......@@ -136,8 +249,8 @@ struct TransformConvBwdDataToGemm_v1
{
// A: output tensor
const auto out_gemmak0_gemmmraw_gemmak1_grid_desc = transform_tensor_descriptor(
out_n_ho_wo_k_grid_desc,
make_tuple(make_pass_through_transform(N * Ho * Wo),
out_grid_desc,
make_tuple(make_pass_through_transform(N * Do * Ho * Wo),
make_unmerge_transform(make_tuple(AK0, AK1))),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<1>{}, Sequence<0, 2>{}));
......@@ -152,103 +265,208 @@ struct TransformConvBwdDataToGemm_v1
}
else
{
const auto GcdStrideDilationD = math::gcd(ConvStrideD, ConvDilationD);
const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
const auto ZTilde = ConvStrideD / GcdStrideDilationD;
const auto YTilde = ConvStrideH / GcdStrideDilationH;
const auto XTilde = ConvStrideW / GcdStrideDilationW;
const auto ZDot = math::integer_divide_ceil(Z, ZTilde);
const auto YDot = math::integer_divide_ceil(Y, YTilde);
const auto XDot = math::integer_divide_ceil(X, XTilde);
const auto DTilde =
Do + math::integer_divide_ceil(ConvDilationD * (Z - I1), ConvStrideD);
const auto HTilde =
Ho + math::integer_divide_ceil(ConvDilationH * (Y - I1), ConvStrideH);
const auto WTilde =
Wo + math::integer_divide_ceil(ConvDilationW * (X - I1), ConvStrideW);
// only work on HTilde and WTilde that contribute to non-padding area of input tensor
const auto IDTildeSliceBegin = math::integer_divide_floor(
math::max(I0, InLeftPadD - ConvDilationD * (ZTilde - I1)), ConvStrideD);
const auto IHTildeSliceBegin = math::integer_divide_floor(
math::max(I0, InLeftPadH - ConvDilationH * (YTilde - I1)), ConvStrideH);
const auto IWTildeSliceBegin = math::integer_divide_floor(
math::max(I0, InLeftPadW - ConvDilationW * (XTilde - I1)), ConvStrideW);
const auto IDTildeSliceEnd = math::min(
DTilde, math::integer_divide_ceil(InLeftPadD + Di - I1, ConvStrideD) + I1);
const auto IHTildeSliceEnd = math::min(
HTilde, math::integer_divide_ceil(InLeftPadH + Hi - I1, ConvStrideH) + I1);
const auto IWTildeSliceEnd = math::min(
WTilde, math::integer_divide_ceil(InLeftPadW + Wi - I1, ConvStrideW) + I1);
const auto DTildeSlice = IDTildeSliceEnd - IDTildeSliceBegin;
const auto HTildeSlice = IHTildeSliceEnd - IHTildeSliceBegin;
const auto WTildeSlice = IWTildeSliceEnd - IWTildeSliceBegin;
// GemmK is different for each GEMM
const auto ZDotSlice = math::integer_divide_ceil(Z - i_ztilde, ZTilde);
const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilde, YTilde);
const auto XDotSlice = math::integer_divide_ceil(X - i_xtilde, XTilde);
// A: output tensor
const auto out_n_hop_wop_k_grid_desc = transform_tensor_descriptor(
out_n_ho_wo_k_grid_desc,
make_tuple(make_pass_through_transform(N),
make_pad_transform(Ho, I0, I0),
make_pad_transform(Wo, I0, I0),
make_pass_through_transform(K)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
const auto out_n_ydot_htilde_xdot_wtilde_k_grid_desc = transform_tensor_descriptor(
out_n_hop_wop_k_grid_desc,
make_tuple(
make_pass_through_transform(N),
make_embed_transform(make_tuple(YDot, HTilde),
make_tuple(-ConvDilationH / GcdStrideDilationH, I1)),
make_embed_transform(make_tuple(XDot, WTilde),
make_tuple(-ConvDilationW / GcdStrideDilationW, I1)),
make_pass_through_transform(K)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto out_n_ydotslice_htildeslice_xdotslice_wtildeslice_ak0_ak1_grid_desc =
transform_tensor_descriptor(
out_n_ydot_htilde_xdot_wtilde_k_grid_desc,
if constexpr(NDimSpatial == 2)
{
// A: output tensor
const auto out_n_hop_wop_k_grid_desc = transform_tensor_descriptor(
out_grid_desc,
make_tuple(make_pass_through_transform(N),
make_slice_transform(YDot, I0, YDotSlice),
make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
make_slice_transform(XDot, I0, XDotSlice),
make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
make_unmerge_transform(make_tuple(AK0, AK1))),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<4>{},
Sequence<5>{}),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<4>{},
Sequence<5, 6>{}));
const auto out_gemmak0_gemmmraw_gemmak1_grid_desc = transform_tensor_descriptor(
out_n_ydotslice_htildeslice_xdotslice_wtildeslice_ak0_ak1_grid_desc,
make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, AK0)),
make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
make_pass_through_transform(AK1)),
make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}, Sequence<6>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
const auto out_gemmak0_gemmm_gemmak1_grid_desc =
ck::tensor_operation::device::PadTensorDescriptor(
out_gemmak0_gemmmraw_gemmak1_grid_desc,
make_tuple(AK0, GemmMPerBlock, AK1),
Sequence<false, DoPadGemmM, false>{});
return out_gemmak0_gemmm_gemmak1_grid_desc;
make_pad_transform(Ho, I0, I0),
make_pad_transform(Wo, I0, I0),
make_pass_through_transform(K)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
const auto out_n_ydot_htilde_xdot_wtilde_k_grid_desc = transform_tensor_descriptor(
out_n_hop_wop_k_grid_desc,
make_tuple(
make_pass_through_transform(N),
make_embed_transform(make_tuple(YDot, HTilde),
make_tuple(-ConvDilationH / GcdStrideDilationH, I1)),
make_embed_transform(make_tuple(XDot, WTilde),
make_tuple(-ConvDilationW / GcdStrideDilationW, I1)),
make_pass_through_transform(K)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto out_n_ydotslice_htildeslice_xdotslice_wtildeslice_ak0_ak1_grid_desc =
transform_tensor_descriptor(
out_n_ydot_htilde_xdot_wtilde_k_grid_desc,
make_tuple(make_pass_through_transform(N),
make_slice_transform(YDot, I0, YDotSlice),
make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
make_slice_transform(XDot, I0, XDotSlice),
make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
make_unmerge_transform(make_tuple(AK0, AK1))),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<4>{},
Sequence<5>{}),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<4>{},
Sequence<5, 6>{}));
const auto out_gemmak0_gemmmraw_gemmak1_grid_desc = transform_tensor_descriptor(
out_n_ydotslice_htildeslice_xdotslice_wtildeslice_ak0_ak1_grid_desc,
make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, AK0)),
make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
make_pass_through_transform(AK1)),
make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}, Sequence<6>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
const auto out_gemmak0_gemmm_gemmak1_grid_desc =
ck::tensor_operation::device::PadTensorDescriptor(
out_gemmak0_gemmmraw_gemmak1_grid_desc,
make_tuple(AK0, GemmMPerBlock, AK1),
Sequence<false, DoPadGemmM, false>{});
return out_gemmak0_gemmm_gemmak1_grid_desc;
}
else if constexpr(NDimSpatial == 3)
{
// A: output tensor
const auto out_n_hop_wop_k_grid_desc = transform_tensor_descriptor(
out_grid_desc,
make_tuple(make_pass_through_transform(N),
make_pad_transform(Do, I0, I0),
make_pad_transform(Ho, I0, I0),
make_pad_transform(Wo, I0, I0),
make_pass_through_transform(K)),
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}));
const auto out_n_zdot_dtilde_ydot_htilde_xdot_wtilde_k_grid_desc =
transform_tensor_descriptor(
out_n_hop_wop_k_grid_desc,
make_tuple(make_pass_through_transform(N),
make_embed_transform(
make_tuple(ZDot, DTilde),
make_tuple(-ConvDilationD / GcdStrideDilationD, I1)),
make_embed_transform(
make_tuple(YDot, HTilde),
make_tuple(-ConvDilationH / GcdStrideDilationH, I1)),
make_embed_transform(
make_tuple(XDot, WTilde),
make_tuple(-ConvDilationW / GcdStrideDilationW, I1)),
make_pass_through_transform(K)),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<4>{}),
make_tuple(Sequence<0>{},
Sequence<1, 2>{},
Sequence<3, 4>{},
Sequence<5, 6>{},
Sequence<7>{}));
const auto
out_n_zdotslice_dtildeslice_ydotslice_htildeslice_xdotslice_wtildeslice_ak0_ak1_grid_desc =
transform_tensor_descriptor(
out_n_zdot_dtilde_ydot_htilde_xdot_wtilde_k_grid_desc,
make_tuple(make_pass_through_transform(N),
make_slice_transform(ZDot, I0, ZDotSlice),
make_slice_transform(DTilde, IDTildeSliceBegin, DTildeSlice),
make_slice_transform(YDot, I0, YDotSlice),
make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
make_slice_transform(XDot, I0, XDotSlice),
make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
make_unmerge_transform(make_tuple(AK0, AK1))),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<4>{},
Sequence<5>{},
Sequence<6>{},
Sequence<7>{}),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<4>{},
Sequence<5>{},
Sequence<6>{},
Sequence<7, 8>{}));
const auto out_gemmak0_gemmmraw_gemmak1_grid_desc = transform_tensor_descriptor(
out_n_zdotslice_dtildeslice_ydotslice_htildeslice_xdotslice_wtildeslice_ak0_ak1_grid_desc,
make_tuple(
make_merge_transform(make_tuple(ZDotSlice, YDotSlice, XDotSlice, AK0)),
make_merge_transform(make_tuple(N, DTildeSlice, HTildeSlice, WTildeSlice)),
make_pass_through_transform(AK1)),
make_tuple(Sequence<1, 3, 5, 7>{}, Sequence<0, 2, 4, 6>{}, Sequence<8>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
const auto out_gemmak0_gemmm_gemmak1_grid_desc =
ck::tensor_operation::device::PadTensorDescriptor(
out_gemmak0_gemmmraw_gemmak1_grid_desc,
make_tuple(AK0, GemmMPerBlock, AK1),
Sequence<false, DoPadGemmM, false>{});
return out_gemmak0_gemmm_gemmak1_grid_desc;
}
else
{
throw std::runtime_error("wrong! only implemented for 2D and 3D now");
}
}
}
template <typename BLayout,
typename std::enable_if<NDimSpatial == 2 &&
is_same_v<BLayout, tensor_layout::convolution::GKYXC>,
typename std::enable_if<(NDimSpatial == 2 || NDimSpatial == 3) &&
(is_same_v<BLayout, tensor_layout::convolution::GKYXC> ||
is_same_v<BLayout, tensor_layout::convolution::GKZYXC>),
bool>::type = false>
static auto MakeBDescriptor_BK0_N_BK1(
const std::array<index_t, NDimSpatial + 3>& out_g_n_k_wos_lengths,
......@@ -263,30 +481,35 @@ struct TransformConvBwdDataToGemm_v1
const std::array<index_t, NDimSpatial>& /* input_right_pads */,
const std::array<index_t, NDimSpatial>& tildes)
{
index_t i_ytilde = tildes[0];
index_t i_xtilde = tildes[1];
index_t i_ztilde = tildes[ZIdx - NonSpatialDimsNum];
index_t i_ytilde = tildes[YIdx - NonSpatialDimsNum];
index_t i_xtilde = tildes[XIdx - NonSpatialDimsNum];
const index_t N = in_g_n_c_wis_lengths[1];
const index_t K = wei_g_k_c_xs_lengths[1];
const index_t C = wei_g_k_c_xs_lengths[2];
const index_t Ho = out_g_n_k_wos_lengths[3];
const index_t Wo = out_g_n_k_wos_lengths[4];
const index_t Do = NDimSpatial == 3 ? out_g_n_k_wos_lengths[DIdx] : 1;
const index_t Ho = out_g_n_k_wos_lengths[HIdx];
const index_t Wo = out_g_n_k_wos_lengths[WIdx];
const index_t Y = wei_g_k_c_xs_lengths[3];
const index_t X = wei_g_k_c_xs_lengths[4];
const index_t Z = NDimSpatial == 3 ? wei_g_k_c_xs_lengths[ZIdx] : 1;
const index_t Y = wei_g_k_c_xs_lengths[YIdx];
const index_t X = wei_g_k_c_xs_lengths[XIdx];
const index_t ConvStrideH = conv_filter_strides[0];
const index_t ConvStrideW = conv_filter_strides[1];
const index_t ConvStrideD = conv_filter_strides[DIdx - NonSpatialDimsNum];
const index_t ConvStrideH = conv_filter_strides[HIdx - NonSpatialDimsNum];
const index_t ConvStrideW = conv_filter_strides[WIdx - NonSpatialDimsNum];
const index_t ConvDilationH = conv_filter_dilations[0];
const index_t ConvDilationW = conv_filter_dilations[1];
const index_t ConvDilationD = conv_filter_dilations[DIdx - NonSpatialDimsNum];
const index_t ConvDilationH = conv_filter_dilations[HIdx - NonSpatialDimsNum];
const index_t ConvDilationW = conv_filter_dilations[WIdx - NonSpatialDimsNum];
const index_t BK0 = K / BK1;
// assume packed
const auto wei_k_y_x_c_grid_desc =
make_naive_tensor_descriptor_packed(make_tuple(K, Y, X, C));
// k_y_x_c for 2d or k_z_y_x_c for 3d
const auto wei_grid_desc = make_wei_grid_desc<BLayout>(K, Z, Y, X, C);
if constexpr(ConvBwdDataSpecialization ==
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization::
......@@ -299,7 +522,7 @@ struct TransformConvBwdDataToGemm_v1
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
make_naive_tensor_descriptor(make_tuple(N * Ho * Wo, C), make_tuple(I0, I1));
make_naive_tensor_descriptor(make_tuple(N * Do * Ho * Wo, C), make_tuple(I0, I1));
const auto wei_gemmbk0_gemmn_gemmbk1_grid_desc =
ck::tensor_operation::device::PadTensorDescriptor(
......@@ -311,75 +534,163 @@ struct TransformConvBwdDataToGemm_v1
}
else
{
const auto GcdStrideDilationD = math::gcd(ConvStrideD, ConvDilationD);
const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
const auto ZTilde = ConvStrideD / GcdStrideDilationD;
const auto YTilde = ConvStrideH / GcdStrideDilationH;
const auto XTilde = ConvStrideW / GcdStrideDilationW;
const auto ZDot = math::integer_divide_ceil(Z, ZTilde);
const auto YDot = math::integer_divide_ceil(Y, YTilde);
const auto XDot = math::integer_divide_ceil(X, XTilde);
// GemmK is different for each GEMM
const auto ZDotSlice = math::integer_divide_ceil(Z - i_ztilde, ZTilde);
const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilde, YTilde);
const auto XDotSlice = math::integer_divide_ceil(X - i_xtilde, XTilde);
// B weight tensor
const auto wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc = transform_tensor_descriptor(
wei_k_y_x_c_grid_desc,
make_tuple(make_pass_through_transform(K),
make_embed_transform(make_tuple(YDot, YTilde),
make_tuple(ConvStrideH / GcdStrideDilationH, I1)),
make_embed_transform(make_tuple(XDot, XTilde),
make_tuple(ConvStrideW / GcdStrideDilationW, I1)),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto wei_bk0_bk1_ydotslice_xdotslice_c_grid_desc =
transform_tensor_descriptor(wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
make_slice_transform(YDot, I0, YDotSlice),
make_slice_transform(XDot, I0, XDotSlice),
make_freeze_transform(i_ytilde),
make_freeze_transform(i_xtilde),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<3>{},
Sequence<2>{},
Sequence<4>{},
Sequence<5>{}),
make_tuple(Sequence<0, 1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<>{},
Sequence<>{},
Sequence<4>{}));
const auto wei_gemmbk0_gemmnraw_gemmbk1_grid_desc = transform_tensor_descriptor(
wei_bk0_bk1_ydotslice_xdotslice_c_grid_desc,
make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, BK0)),
make_pass_through_transform(C),
make_pass_through_transform(BK1)),
make_tuple(Sequence<2, 3, 0>{}, Sequence<4>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
const auto wei_gemmbk0_gemmn_gemmbk1_grid_desc =
ck::tensor_operation::device::PadTensorDescriptor(
wei_gemmbk0_gemmnraw_gemmbk1_grid_desc,
if constexpr(NDimSpatial == 2)
{
const auto wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc = transform_tensor_descriptor(
wei_grid_desc,
make_tuple(
wei_gemmbk0_gemmnraw_gemmbk1_grid_desc.GetLength(I0), GemmNPerBlock, BK1),
Sequence<false, DoPadGemmN, false>{});
return wei_gemmbk0_gemmn_gemmbk1_grid_desc;
make_pass_through_transform(K),
make_embed_transform(make_tuple(YDot, YTilde),
make_tuple(ConvStrideH / GcdStrideDilationH, I1)),
make_embed_transform(make_tuple(XDot, XTilde),
make_tuple(ConvStrideW / GcdStrideDilationW, I1)),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto wei_bk0_bk1_ydotslice_xdotslice_c_grid_desc =
transform_tensor_descriptor(
wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
make_slice_transform(YDot, I0, YDotSlice),
make_slice_transform(XDot, I0, XDotSlice),
make_freeze_transform(i_ytilde),
make_freeze_transform(i_xtilde),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<3>{},
Sequence<2>{},
Sequence<4>{},
Sequence<5>{}),
make_tuple(Sequence<0, 1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<>{},
Sequence<>{},
Sequence<4>{}));
const auto wei_gemmbk0_gemmnraw_gemmbk1_grid_desc = transform_tensor_descriptor(
wei_bk0_bk1_ydotslice_xdotslice_c_grid_desc,
make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, BK0)),
make_pass_through_transform(C),
make_pass_through_transform(BK1)),
make_tuple(Sequence<2, 3, 0>{}, Sequence<4>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
const auto wei_gemmbk0_gemmn_gemmbk1_grid_desc =
ck::tensor_operation::device::PadTensorDescriptor(
wei_gemmbk0_gemmnraw_gemmbk1_grid_desc,
make_tuple(wei_gemmbk0_gemmnraw_gemmbk1_grid_desc.GetLength(I0),
GemmNPerBlock,
BK1),
Sequence<false, DoPadGemmN, false>{});
return wei_gemmbk0_gemmn_gemmbk1_grid_desc;
}
else if constexpr(NDimSpatial == 3)
{
const auto wei_k_zdot_ztilde_ydot_ytilde_xdot_xtilde_c_grid_desc =
transform_tensor_descriptor(
wei_grid_desc,
make_tuple(
make_pass_through_transform(K),
make_embed_transform(make_tuple(ZDot, ZTilde),
make_tuple(ConvStrideD / GcdStrideDilationD, I1)),
make_embed_transform(make_tuple(YDot, YTilde),
make_tuple(ConvStrideH / GcdStrideDilationH, I1)),
make_embed_transform(make_tuple(XDot, XTilde),
make_tuple(ConvStrideW / GcdStrideDilationW, I1)),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<4>{}),
make_tuple(Sequence<0>{},
Sequence<1, 2>{},
Sequence<3, 4>{},
Sequence<5, 6>{},
Sequence<7>{}));
const auto wei_bk0_bk1_zdotslice_ydotslice_xdotslice_c_grid_desc =
transform_tensor_descriptor(
wei_k_zdot_ztilde_ydot_ytilde_xdot_xtilde_c_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
make_slice_transform(ZDot, I0, ZDotSlice),
make_slice_transform(YDot, I0, YDotSlice),
make_slice_transform(XDot, I0, XDotSlice),
make_freeze_transform(i_ztilde),
make_freeze_transform(i_ytilde),
make_freeze_transform(i_xtilde),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<3>{},
Sequence<5>{},
Sequence<2>{},
Sequence<4>{},
Sequence<6>{},
Sequence<7>{}),
make_tuple(Sequence<0, 1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<4>{},
Sequence<>{},
Sequence<>{},
Sequence<>{},
Sequence<5>{}));
const auto wei_gemmbk0_gemmnraw_gemmbk1_grid_desc = transform_tensor_descriptor(
wei_bk0_bk1_zdotslice_ydotslice_xdotslice_c_grid_desc,
make_tuple(
make_merge_transform(make_tuple(ZDotSlice, YDotSlice, XDotSlice, BK0)),
make_pass_through_transform(C),
make_pass_through_transform(BK1)),
make_tuple(Sequence<2, 3, 4, 0>{}, Sequence<5>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
const auto wei_gemmbk0_gemmn_gemmbk1_grid_desc =
ck::tensor_operation::device::PadTensorDescriptor(
wei_gemmbk0_gemmnraw_gemmbk1_grid_desc,
make_tuple(wei_gemmbk0_gemmnraw_gemmbk1_grid_desc.GetLength(I0),
GemmNPerBlock,
BK1),
Sequence<false, DoPadGemmN, false>{});
return wei_gemmbk0_gemmn_gemmbk1_grid_desc;
}
else
{
throw std::runtime_error("wrong! only implemented for 2D and 3D now");
}
}
}
template <typename CLayout,
typename std::enable_if<NDimSpatial == 2 &&
typename std::enable_if<(NDimSpatial == 2 || NDimSpatial == 3) &&
(is_same_v<CLayout, tensor_layout::convolution::GNHWC> ||
is_same_v<CLayout, tensor_layout::convolution::GNDHWC> ||
is_same_v<CLayout, tensor_layout::convolution::NHWGC> ||
is_same_v<CLayout, tensor_layout::convolution::NDHWGC> ||
is_same_v<CLayout, tensor_layout::convolution::G_NHW_C>),
bool>::type = false>
static auto
......@@ -395,153 +706,309 @@ struct TransformConvBwdDataToGemm_v1
const std::array<index_t, NDimSpatial>& input_right_pads,
const std::array<index_t, NDimSpatial>& tildes)
{
index_t i_ytilde = tildes[0];
index_t i_xtilde = tildes[1];
index_t i_ztilde = tildes[ZIdx - NonSpatialDimsNum];
index_t i_ytilde = tildes[YIdx - NonSpatialDimsNum];
index_t i_xtilde = tildes[XIdx - NonSpatialDimsNum];
const index_t N = in_g_n_c_wis_lengths[1];
const index_t C = wei_g_k_c_xs_lengths[2];
const index_t Hi = in_g_n_c_wis_lengths[3];
const index_t Wi = in_g_n_c_wis_lengths[4];
const index_t Di = NDimSpatial == 3 ? in_g_n_c_wis_lengths[DIdx] : 1;
const index_t Hi = in_g_n_c_wis_lengths[HIdx];
const index_t Wi = in_g_n_c_wis_lengths[WIdx];
const index_t Ho = out_g_n_k_wos_lengths[3];
const index_t Wo = out_g_n_k_wos_lengths[4];
const index_t Do = NDimSpatial == 3 ? out_g_n_k_wos_lengths[DIdx] : 1;
const index_t Ho = out_g_n_k_wos_lengths[HIdx];
const index_t Wo = out_g_n_k_wos_lengths[WIdx];
const index_t Y = wei_g_k_c_xs_lengths[3];
const index_t X = wei_g_k_c_xs_lengths[4];
const index_t Z = NDimSpatial == 3 ? wei_g_k_c_xs_lengths[ZIdx] : 1;
const index_t Y = wei_g_k_c_xs_lengths[YIdx];
const index_t X = wei_g_k_c_xs_lengths[XIdx];
const index_t InLeftPadH = input_left_pads[0];
const index_t InLeftPadW = input_left_pads[1];
const index_t InLeftPadD = input_left_pads[DIdx - NonSpatialDimsNum];
const index_t InLeftPadH = input_left_pads[HIdx - NonSpatialDimsNum];
const index_t InLeftPadW = input_left_pads[WIdx - NonSpatialDimsNum];
const index_t InRightPadH = input_right_pads[0];
const index_t InRightPadW = input_right_pads[1];
const index_t InRightPadD = input_right_pads[DIdx - NonSpatialDimsNum];
const index_t InRightPadH = input_right_pads[HIdx - NonSpatialDimsNum];
const index_t InRightPadW = input_right_pads[WIdx - NonSpatialDimsNum];
const index_t ConvStrideH = conv_filter_strides[0];
const index_t ConvStrideW = conv_filter_strides[1];
const index_t ConvStrideD = conv_filter_strides[DIdx - NonSpatialDimsNum];
const index_t ConvStrideH = conv_filter_strides[HIdx - NonSpatialDimsNum];
const index_t ConvStrideW = conv_filter_strides[WIdx - NonSpatialDimsNum];
const index_t ConvDilationH = conv_filter_dilations[0];
const index_t ConvDilationW = conv_filter_dilations[1];
const index_t ConvDilationD = conv_filter_dilations[DIdx - NonSpatialDimsNum];
const index_t ConvDilationH = conv_filter_dilations[HIdx - NonSpatialDimsNum];
const index_t ConvDilationW = conv_filter_dilations[WIdx - NonSpatialDimsNum];
// assume strided
const auto in_n_hi_wi_c_grid_desc =
make_naive_tensor_descriptor(make_tuple(N, Hi, Wi, C),
make_tuple(in_g_n_c_wis_strides[1],
in_g_n_c_wis_strides[3],
in_g_n_c_wis_strides[4],
in_g_n_c_wis_strides[2]));
// n_hi_wi_c for 2d n_di_hi_wi_c for 3d
const auto in_grid_desc =
make_in_grid_desc<NDimSpatial, CLayout>(N, Di, Hi, Wi, C, in_g_n_c_wis_strides);
if constexpr(ConvBwdDataSpecialization ==
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization::
Filter1x1Stride1Pad0)
{
// C: input tensor
const auto in_n_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
in_n_hi_wi_c_grid_desc,
make_tuple(make_pass_through_transform(N),
make_embed_transform(make_tuple(I1, Ho), make_tuple(I1, ConvStrideH)),
make_embed_transform(make_tuple(I1, Wo), make_tuple(I1, ConvStrideW)),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto in_gemmmraw_gemmnraw_grid_desc = transform_tensor_descriptor(
in_n_y_ho_x_wo_c_grid_desc,
make_tuple(make_freeze_transform(I0),
make_freeze_transform(I0),
make_merge_transform(make_tuple(N, Ho, Wo)),
make_pass_through_transform(C)),
make_tuple(Sequence<1>{}, Sequence<3>{}, Sequence<0, 2, 4>{}, Sequence<5>{}),
make_tuple(Sequence<>{}, Sequence<>{}, Sequence<0>{}, Sequence<1>{}));
const auto in_gemmm_gemmn_grid_desc = ck::tensor_operation::device::PadTensorDescriptor(
in_gemmmraw_gemmnraw_grid_desc,
make_tuple(GemmMPerBlock, GemmNPerBlock),
Sequence<DoPadGemmM, DoPadGemmN>{});
return in_gemmm_gemmn_grid_desc;
if constexpr(NDimSpatial == 2)
{
const auto in_n_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
in_grid_desc,
make_tuple(
make_pass_through_transform(N),
make_embed_transform(make_tuple(I1, Ho), make_tuple(I1, ConvStrideH)),
make_embed_transform(make_tuple(I1, Wo), make_tuple(I1, ConvStrideW)),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto in_gemmmraw_gemmnraw_grid_desc = transform_tensor_descriptor(
in_n_y_ho_x_wo_c_grid_desc,
make_tuple(make_freeze_transform(I0),
make_freeze_transform(I0),
make_merge_transform(make_tuple(N, Ho, Wo)),
make_pass_through_transform(C)),
make_tuple(Sequence<1>{}, Sequence<3>{}, Sequence<0, 2, 4>{}, Sequence<5>{}),
make_tuple(Sequence<>{}, Sequence<>{}, Sequence<0>{}, Sequence<1>{}));
const auto in_gemmm_gemmn_grid_desc =
ck::tensor_operation::device::PadTensorDescriptor(
in_gemmmraw_gemmnraw_grid_desc,
make_tuple(GemmMPerBlock, GemmNPerBlock),
Sequence<DoPadGemmM, DoPadGemmN>{});
return in_gemmm_gemmn_grid_desc;
}
else if constexpr(NDimSpatial == 3)
{
// C: input tensor
const auto in_n_x_do_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
in_grid_desc,
make_tuple(
make_pass_through_transform(N),
make_embed_transform(make_tuple(I1, Do), make_tuple(I1, ConvStrideD)),
make_embed_transform(make_tuple(I1, Ho), make_tuple(I1, ConvStrideH)),
make_embed_transform(make_tuple(I1, Wo), make_tuple(I1, ConvStrideW)),
make_pass_through_transform(C)),
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
make_tuple(Sequence<0>{},
Sequence<1, 2>{},
Sequence<3, 4>{},
Sequence<5, 6>{},
Sequence<7>{}));
const auto in_gemmmraw_gemmnraw_grid_desc = transform_tensor_descriptor(
in_n_x_do_y_ho_x_wo_c_grid_desc,
make_tuple(make_freeze_transform(I0),
make_freeze_transform(I0),
make_freeze_transform(I0),
make_merge_transform(make_tuple(N, Do, Ho, Wo)),
make_pass_through_transform(C)),
make_tuple(Sequence<1>{},
Sequence<3>{},
Sequence<5>{},
Sequence<0, 2, 4, 6>{},
Sequence<7>{}),
make_tuple(
Sequence<>{}, Sequence<>{}, Sequence<>{}, Sequence<0>{}, Sequence<1>{}));
const auto in_gemmm_gemmn_grid_desc =
ck::tensor_operation::device::PadTensorDescriptor(
in_gemmmraw_gemmnraw_grid_desc,
make_tuple(GemmMPerBlock, GemmNPerBlock),
Sequence<DoPadGemmM, DoPadGemmN>{});
return in_gemmm_gemmn_grid_desc;
}
else
{
throw std::runtime_error("wrong! only implemented for 2D and 3D now");
}
}
else
{
const auto GcdStrideDilationD = math::gcd(ConvStrideD, ConvDilationD);
const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
const auto ZTilde = ConvStrideD / GcdStrideDilationD;
const auto YTilde = ConvStrideH / GcdStrideDilationH;
const auto XTilde = ConvStrideW / GcdStrideDilationW;
const auto DTilde =
Do + math::integer_divide_ceil(ConvDilationD * (Z - I1), ConvStrideD);
const auto HTilde =
Ho + math::integer_divide_ceil(ConvDilationH * (Y - I1), ConvStrideH);
const auto WTilde =
Wo + math::integer_divide_ceil(ConvDilationW * (X - I1), ConvStrideW);
// only work on HTilde and WTilde that contribute to non-padding area of input tensor
// only work on DTilde, HTilde and WTilde that contribute to
// non-padding area of input tensor
const auto IDTildeSliceBegin = math::integer_divide_floor(
math::max(I0, InLeftPadD - ConvDilationD * (ZTilde - I1)), ConvStrideD);
const auto IHTildeSliceBegin = math::integer_divide_floor(
math::max(I0, InLeftPadH - ConvDilationH * (YTilde - I1)), ConvStrideH);
const auto IWTildeSliceBegin = math::integer_divide_floor(
math::max(I0, InLeftPadW - ConvDilationW * (XTilde - I1)), ConvStrideW);
const auto IDTildeSliceEnd = math::min(
DTilde, math::integer_divide_ceil(InLeftPadD + Di - I1, ConvStrideD) + I1);
const auto IHTildeSliceEnd = math::min(
HTilde, math::integer_divide_ceil(InLeftPadH + Hi - I1, ConvStrideH) + I1);
const auto IWTildeSliceEnd = math::min(
WTilde, math::integer_divide_ceil(InLeftPadW + Wi - I1, ConvStrideW) + I1);
const auto DTildeSlice = IDTildeSliceEnd - IDTildeSliceBegin;
const auto HTildeSlice = IHTildeSliceEnd - IHTildeSliceBegin;
const auto WTildeSlice = IWTildeSliceEnd - IWTildeSliceBegin;
// C: input tensor
const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
in_n_hi_wi_c_grid_desc,
make_tuple(make_pass_through_transform(N),
make_pad_transform(Hi, InLeftPadH, InRightPadH),
make_pad_transform(Wi, InLeftPadW, InRightPadW),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
const auto in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc = transform_tensor_descriptor(
in_n_hip_wip_c_grid_desc,
make_tuple(make_pass_through_transform(N),
make_embed_transform(make_tuple(YTilde, HTilde),
make_tuple(ConvDilationH, ConvStrideH)),
make_embed_transform(make_tuple(XTilde, WTilde),
make_tuple(ConvDilationW, ConvStrideW)),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto in_n_htildeslice_wtildeslice_c_grid_desc = transform_tensor_descriptor(
in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc,
make_tuple(make_pass_through_transform(N),
make_freeze_transform(i_ytilde),
make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
make_freeze_transform(i_xtilde),
make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<4>{},
Sequence<5>{}),
make_tuple(Sequence<0>{},
Sequence<>{},
Sequence<1>{},
Sequence<>{},
Sequence<2>{},
Sequence<3>{}));
const auto in_gemmmraw_gemmnraw_grid_desc = transform_tensor_descriptor(
in_n_htildeslice_wtildeslice_c_grid_desc,
make_tuple(make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
make_pass_through_transform(C)),
make_tuple(Sequence<0, 1, 2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
const auto in_gemmm_gemmn_grid_desc = ck::tensor_operation::device::PadTensorDescriptor(
in_gemmmraw_gemmnraw_grid_desc,
make_tuple(GemmMPerBlock, GemmNPerBlock),
Sequence<DoPadGemmM, DoPadGemmN>{});
return in_gemmm_gemmn_grid_desc;
if constexpr(NDimSpatial == 2)
{
const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
in_grid_desc,
make_tuple(make_pass_through_transform(N),
make_pad_transform(Hi, InLeftPadH, InRightPadH),
make_pad_transform(Wi, InLeftPadW, InRightPadW),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
const auto in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc =
transform_tensor_descriptor(
in_n_hip_wip_c_grid_desc,
make_tuple(make_pass_through_transform(N),
make_embed_transform(make_tuple(YTilde, HTilde),
make_tuple(ConvDilationH, ConvStrideH)),
make_embed_transform(make_tuple(XTilde, WTilde),
make_tuple(ConvDilationW, ConvStrideW)),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(
Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto in_n_htildeslice_wtildeslice_c_grid_desc = transform_tensor_descriptor(
in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc,
make_tuple(make_pass_through_transform(N),
make_freeze_transform(i_ytilde),
make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
make_freeze_transform(i_xtilde),
make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<4>{},
Sequence<5>{}),
make_tuple(Sequence<0>{},
Sequence<>{},
Sequence<1>{},
Sequence<>{},
Sequence<2>{},
Sequence<3>{}));
const auto in_gemmmraw_gemmnraw_grid_desc = transform_tensor_descriptor(
in_n_htildeslice_wtildeslice_c_grid_desc,
make_tuple(make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
make_pass_through_transform(C)),
make_tuple(Sequence<0, 1, 2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
const auto in_gemmm_gemmn_grid_desc =
ck::tensor_operation::device::PadTensorDescriptor(
in_gemmmraw_gemmnraw_grid_desc,
make_tuple(GemmMPerBlock, GemmNPerBlock),
Sequence<DoPadGemmM, DoPadGemmN>{});
return in_gemmm_gemmn_grid_desc;
}
else if(NDimSpatial == 3)
{
const auto in_n_dip_hip_wip_c_grid_desc = transform_tensor_descriptor(
in_grid_desc,
make_tuple(make_pass_through_transform(N),
make_pad_transform(Di, InLeftPadD, InRightPadD),
make_pad_transform(Hi, InLeftPadH, InRightPadH),
make_pad_transform(Wi, InLeftPadW, InRightPadW),
make_pass_through_transform(C)),
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}));
const auto in_n_ztilde_dtilde_ytilde_htilde_xtilde_wtilde_c_grid_desc =
transform_tensor_descriptor(
in_n_dip_hip_wip_c_grid_desc,
make_tuple(make_pass_through_transform(N),
make_embed_transform(make_tuple(ZTilde, DTilde),
make_tuple(ConvDilationD, ConvStrideD)),
make_embed_transform(make_tuple(YTilde, HTilde),
make_tuple(ConvDilationH, ConvStrideH)),
make_embed_transform(make_tuple(XTilde, WTilde),
make_tuple(ConvDilationW, ConvStrideW)),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<4>{}),
make_tuple(Sequence<0>{},
Sequence<1, 2>{},
Sequence<3, 4>{},
Sequence<5, 6>{},
Sequence<7>{}));
const auto in_n_dtildeslice_htildeslice_wtildeslice_c_grid_desc =
transform_tensor_descriptor(
in_n_ztilde_dtilde_ytilde_htilde_xtilde_wtilde_c_grid_desc,
make_tuple(make_pass_through_transform(N),
make_freeze_transform(i_ztilde),
make_slice_transform(DTilde, IDTildeSliceBegin, DTildeSlice),
make_freeze_transform(i_ytilde),
make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
make_freeze_transform(i_xtilde),
make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<4>{},
Sequence<5>{},
Sequence<6>{},
Sequence<7>{}),
make_tuple(Sequence<0>{},
Sequence<>{},
Sequence<1>{},
Sequence<>{},
Sequence<2>{},
Sequence<>{},
Sequence<3>{},
Sequence<4>{}));
const auto in_gemmmraw_gemmnraw_grid_desc = transform_tensor_descriptor(
in_n_dtildeslice_htildeslice_wtildeslice_c_grid_desc,
make_tuple(
make_merge_transform(make_tuple(N, DTildeSlice, HTildeSlice, WTildeSlice)),
make_pass_through_transform(C)),
make_tuple(Sequence<0, 1, 2, 3>{}, Sequence<4>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
const auto in_gemmm_gemmn_grid_desc =
ck::tensor_operation::device::PadTensorDescriptor(
in_gemmmraw_gemmnraw_grid_desc,
make_tuple(GemmMPerBlock, GemmNPerBlock),
Sequence<DoPadGemmM, DoPadGemmN>{});
return in_gemmm_gemmn_grid_desc;
}
else
{
throw std::runtime_error("wrong! only implemented for 2D and 3D now");
}
}
}
......
library/include/ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_data/device_grouped_conv_bwd_data_xdl_instance.hpp 0 → 100644
View file @ 49180fd6
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_data_multiple_d_xdl_cshuffle_v1.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using BF16 = ck::bhalf_t;
using F16 = ck::half_t;
using F32 = float;
using Empty_Tuple = ck::Tuple<>;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using namespace ck::tensor_layout::convolution;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto ConvBwdDataDefault = ConvolutionBackwardDataSpecialization::Default;
static constexpr auto ConvBwdDataFilter1x1Stride1Pad0 =
ConvolutionBackwardDataSpecialization::Filter1x1Stride1Pad0;
// f16_f16_f32_f16
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionBackwardDataSpecialization ConvSpec>
using device_grouped_conv_bwd_data_xdl_f16_instances =
std::tuple<
// clang-format off
// ##############################################| NDim| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| AElementwise| BElementwise| CDEElementwise| ConvolutionBackward| DoPad| DoPad| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffleMXdl| CShuffleNXdl| CDEBlockTransfer| CDEBlockTransfer|
// ##############################################| Spatial| | | | | Type| Type| Type| DataType| Type| Type| Operation| Operation| Operation| DataSpecialization| GemmM| GemmN| PrefetchStage| Size| Block| Block| Block| | | XDL| XDL| PerWave| PerWave| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| PerWave| PerWave| _MBlock_MPerBlock| ScalarPerVector|
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | Lengths_AK0_M_AK1| ArrangeOrder| | | PerVector| PerVector_AK1| | Lengths_BK0_N_BK1| ArrangeOrder| | | PerVector| PerVector_BK1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock|
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 256, 32, 8, 2, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>
#ifdef CK_WORKAROUND_SWDEV_3318619
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 128, 128, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 128, 32, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 64, 64, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 64, 32, 64, 32, 8, 8, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>,
#endif
// clang-format on
>;
// bf16_bf16_f32_bf16
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionBackwardDataSpecialization ConvSpec>
using device_grouped_conv_bwd_data_xdl_bf16_instances =
std::tuple<
// clang-format off
// ##############################################| NDim| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| AElementwise| BElementwise| CDEElementwise| ConvolutionBackward| DoPad| DoPad| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffleMXdl| CShuffleNXdl| CDEBlockTransfer| CDEBlockTransfer|
// ##############################################| Spatial| | | | | Type| Type| Type| DataType| Type| Type| Operation| Operation| Operation| DataSpecialization| GemmM| GemmN| PrefetchStage| Size| Block| Block| Block| | | XDL| XDL| PerWave| PerWave| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| PerWave| PerWave| _MBlock_MPerBlock| ScalarPerVector|
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | Lengths_AK0_M_AK1| ArrangeOrder| | | PerVector| PerVector_AK1| | Lengths_BK0_N_BK1| ArrangeOrder| | | PerVector| PerVector_BK1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock|
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 256, 32, 8, 2, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>
#ifdef CK_WORKAROUND_SWDEV_3318619
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 32, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 64, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 32, 64, 32, 8, 8, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>
#endif
// clang-format on
>;
// f32_f32_f32_f32
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionBackwardDataSpecialization ConvSpec>
using device_grouped_conv_bwd_data_xdl_f32_instances =
std::tuple<
// clang-format off
// ##############################################| NDim| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| AElementwise| BElementwise| CDEElementwise| ConvolutionBackward| DoPad| DoPad| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffleMXdl| CShuffleNXdl| CDEBlockTransfer| CDEBlockTransfer|
// ##############################################| Spatial| | | | | Type| Type| Type| DataType| Type| Type| Operation| Operation| Operation| DataSpecialization| GemmM| GemmN| PrefetchStage| Size| Block| Block| Block| | | XDL| XDL| PerWave| PerWave| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| PerWave| PerWave| _MBlock_MPerBlock| ScalarPerVector|
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | Lengths_AK0_M_AK1| ArrangeOrder| | | PerVector| PerVector_AK1| | Lengths_BK0_N_BK1| ArrangeOrder| | | PerVector| PerVector_BK1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock|
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 256, 32, 8, 2, 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<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 4>
#ifdef CK_WORKAROUND_SWDEV_3318619
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 256, 128, 32, 8, 8, 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>, 1, 4, 4, 1, 1, 1, S<1, 32, 1, 8>, 4>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 256, 32, 8, 8, 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>, 1, 4, 4, 1, 1, 1, S<1, 32, 1, 8>, 4>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 128, 32, 8, 8, 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>, 1, 4, 4, 1, 1, 1, S<1, 16, 1, 8>, 4>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 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, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 4, 4, 1, 1, 1, S<1, 32, 1, 8>, 4>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 64, 32, 8, 8, 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>, 1, 4, 4, 1, 1, 1, S<1, 32, 1, 4>, 4>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 64, 128, 32, 8, 8, 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>, 1, 4, 4, 1, 1, 1, S<1, 16, 1, 8>, 4>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 64, 64, 32, 8, 8, 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>, 1, 4, 4, 1, 1, 1, S<1, 16, 1, 4>, 4>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 64, 32, 8, 8, 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>, 1, 4, 4, 1, 1, 1, S<1, 32, 1, 8>, 4>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 64, 128, 32, 8, 8, 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>, 1, 4, 4, 1, 1, 1, S<1, 32, 1, 8>, 4>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 32, 32, 8, 8, 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>, 1, 4, 4, 1, 1, 1, S<1, 32, 1, 4>, 4>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 32, 128, 32, 8, 8, 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>, 1, 4, 4, 1, 1, 1, S<1, 16, 1, 8>, 4>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 64, 32, 32, 8, 8, 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>, 1, 4, 4, 1, 1, 1, S<1, 16, 1, 4>, 4>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 32, 64, 32, 8, 8, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>,
#endif
// clang-format on
>;
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_backward_data.hpp
View file @ 49180fd6
......@@ -16,7 +16,7 @@ namespace device {
namespace instance {
// conv2d backward data
void add_device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_f16_instances(
void add_device_grouped_conv2d_bwd_data_xdl_gnhwk_gkyxc_gnhwc_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<2,
GNHWK,
GKYXC,
......@@ -30,7 +30,7 @@ void add_device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_f16_instances(
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_f32_instances(
void add_device_grouped_conv2d_bwd_data_xdl_gnhwk_gkyxc_gnhwc_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<2,
GNHWK,
GKYXC,
......@@ -44,7 +44,7 @@ void add_device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_f32_instances(
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_bf16_instances(
void add_device_grouped_conv2d_bwd_data_xdl_gnhwk_gkyxc_gnhwc_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<2,
GNHWK,
GKYXC,
......@@ -58,7 +58,7 @@ void add_device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_bf16_instances(
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv2d_bwd_data_xdl_nhwgc_gkyxc_nhwgk_f16_instances(
void add_device_grouped_conv2d_bwd_data_xdl_nhwgk_gkyxc_nhwgc_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<2,
NHWGK,
GKYXC,
......@@ -72,7 +72,7 @@ void add_device_grouped_conv2d_bwd_data_xdl_nhwgc_gkyxc_nhwgk_f16_instances(
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv2d_bwd_data_xdl_nhwgc_gkyxc_nhwgk_f32_instances(
void add_device_grouped_conv2d_bwd_data_xdl_nhwgk_gkyxc_nhwgc_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<2,
NHWGK,
GKYXC,
......@@ -86,7 +86,7 @@ void add_device_grouped_conv2d_bwd_data_xdl_nhwgc_gkyxc_nhwgk_f32_instances(
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv2d_bwd_data_xdl_nhwgc_gkyxc_nhwgk_bf16_instances(
void add_device_grouped_conv2d_bwd_data_xdl_nhwgk_gkyxc_nhwgc_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<2,
NHWGK,
GKYXC,
......@@ -100,6 +100,91 @@ void add_device_grouped_conv2d_bwd_data_xdl_nhwgc_gkyxc_nhwgk_bf16_instances(
PassThrough,
PassThrough>>>& instances);
// conv3d backward data
void add_device_grouped_conv3d_bwd_data_xdl_gndhwk_gkzyxc_gndhwc_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<3,
GNDHWK,
GKZYXC,
Empty_Tuple,
GNDHWC,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv3d_bwd_data_xdl_gndhwk_gkzyxc_gndhwc_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<3,
GNDHWK,
GKZYXC,
Empty_Tuple,
GNDHWC,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv3d_bwd_data_xdl_gndhwk_gkzyxc_gndhwc_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<3,
GNDHWK,
GKZYXC,
Empty_Tuple,
GNDHWC,
BF16,
BF16,
Empty_Tuple,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv3d_bwd_data_xdl_ndhwgk_gkzyxc_ndhwgc_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<3,
NDHWGK,
GKZYXC,
Empty_Tuple,
NDHWGC,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv3d_bwd_data_xdl_ndhwgk_gkzyxc_ndhwgc_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<3,
NDHWGK,
GKZYXC,
Empty_Tuple,
NDHWGC,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv3d_bwd_data_xdl_ndhwgk_gkzyxc_ndhwgc_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<3,
NDHWGK,
GKZYXC,
Empty_Tuple,
NDHWGC,
BF16,
BF16,
Empty_Tuple,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
template <ck::index_t NumDimSpatial,
typename OutLayout,
typename WeiLayout,
......@@ -139,43 +224,96 @@ struct DeviceOperationInstanceFactory<
static auto GetInstances()
{
std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
if constexpr(NumDimSpatial == 2 && is_same_v<InLayout, GNHWC> &&
is_same_v<WeiLayout, GKYXC> && is_same_v<OutLayout, GNHWK>)
if constexpr(NumDimSpatial == 2)
{
if constexpr(is_same_v<InDataType, F16> && is_same_v<WeiDataType, F16> &&
is_same_v<OutDataType, F16>)
{
add_device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_f16_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, F32> && is_same_v<WeiDataType, F32> &&
is_same_v<OutDataType, F32>)
if constexpr(is_same_v<InLayout, GNHWC> && is_same_v<WeiLayout, GKYXC> &&
is_same_v<OutLayout, GNHWK>)
{
add_device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_f32_instances(op_ptrs);
if constexpr(is_same_v<InDataType, F16> && is_same_v<WeiDataType, F16> &&
is_same_v<OutDataType, F16>)
{
add_device_grouped_conv2d_bwd_data_xdl_gnhwk_gkyxc_gnhwc_f16_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, F32> && is_same_v<WeiDataType, F32> &&
is_same_v<OutDataType, F32>)
{
add_device_grouped_conv2d_bwd_data_xdl_gnhwk_gkyxc_gnhwc_f32_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, BF16> && is_same_v<WeiDataType, BF16> &&
is_same_v<OutDataType, BF16>)
{
add_device_grouped_conv2d_bwd_data_xdl_gnhwk_gkyxc_gnhwc_bf16_instances(
op_ptrs);
}
}
else if constexpr(is_same_v<InDataType, BF16> && is_same_v<WeiDataType, BF16> &&
is_same_v<OutDataType, BF16>)
else if constexpr(is_same_v<InLayout, NHWGC> && is_same_v<WeiLayout, GKYXC> &&
is_same_v<OutLayout, NHWGK>)
{
add_device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_bf16_instances(op_ptrs);
if constexpr(is_same_v<InDataType, F16> && is_same_v<WeiDataType, F16> &&
is_same_v<OutDataType, F16>)
{
add_device_grouped_conv2d_bwd_data_xdl_nhwgk_gkyxc_nhwgc_f16_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, F32> && is_same_v<WeiDataType, F32> &&
is_same_v<OutDataType, F32>)
{
add_device_grouped_conv2d_bwd_data_xdl_nhwgk_gkyxc_nhwgc_f32_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, BF16> && is_same_v<WeiDataType, BF16> &&
is_same_v<OutDataType, BF16>)
{
add_device_grouped_conv2d_bwd_data_xdl_nhwgk_gkyxc_nhwgc_bf16_instances(
op_ptrs);
}
}
}
else if constexpr(NumDimSpatial == 2 && is_same_v<InLayout, NHWGC> &&
is_same_v<WeiLayout, GKYXC> && is_same_v<OutLayout, NHWGK>)
else if constexpr(NumDimSpatial == 3)
{
if constexpr(is_same_v<InDataType, F16> && is_same_v<WeiDataType, F16> &&
is_same_v<OutDataType, F16>)
{
add_device_grouped_conv2d_bwd_data_xdl_nhwgc_gkyxc_nhwgk_f16_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, F32> && is_same_v<WeiDataType, F32> &&
is_same_v<OutDataType, F32>)
if constexpr(is_same_v<InLayout, GNDHWC> && is_same_v<WeiLayout, GKZYXC> &&
is_same_v<OutLayout, GNDHWK>)
{
add_device_grouped_conv2d_bwd_data_xdl_nhwgc_gkyxc_nhwgk_f32_instances(op_ptrs);
if constexpr(is_same_v<InDataType, F16> && is_same_v<WeiDataType, F16> &&
is_same_v<OutDataType, F16>)
{
add_device_grouped_conv3d_bwd_data_xdl_gndhwk_gkzyxc_gndhwc_f16_instances(
op_ptrs);
}
else if constexpr(is_same_v<InDataType, F32> && is_same_v<WeiDataType, F32> &&
is_same_v<OutDataType, F32>)
{
add_device_grouped_conv3d_bwd_data_xdl_gndhwk_gkzyxc_gndhwc_f32_instances(
op_ptrs);
}
else if constexpr(is_same_v<InDataType, BF16> && is_same_v<WeiDataType, BF16> &&
is_same_v<OutDataType, BF16>)
{
add_device_grouped_conv3d_bwd_data_xdl_gndhwk_gkzyxc_gndhwc_bf16_instances(
op_ptrs);
}
}
else if constexpr(is_same_v<InDataType, BF16> && is_same_v<WeiDataType, BF16> &&
is_same_v<OutDataType, BF16>)
else if constexpr(is_same_v<InLayout, NDHWGC> && is_same_v<WeiLayout, GKZYXC> &&
is_same_v<OutLayout, NDHWGK>)
{
add_device_grouped_conv2d_bwd_data_xdl_nhwgc_gkyxc_nhwgk_bf16_instances(op_ptrs);
if constexpr(is_same_v<InDataType, F16> && is_same_v<WeiDataType, F16> &&
is_same_v<OutDataType, F16>)
{
add_device_grouped_conv3d_bwd_data_xdl_ndhwgk_gkzyxc_ndhwgc_f16_instances(
op_ptrs);
}
else if constexpr(is_same_v<InDataType, F32> && is_same_v<WeiDataType, F32> &&
is_same_v<OutDataType, F32>)
{
add_device_grouped_conv3d_bwd_data_xdl_ndhwgk_gkzyxc_ndhwgc_f32_instances(
op_ptrs);
}
else if constexpr(is_same_v<InDataType, BF16> && is_same_v<WeiDataType, BF16> &&
is_same_v<OutDataType, BF16>)
{
add_device_grouped_conv3d_bwd_data_xdl_ndhwgk_gkzyxc_ndhwgc_bf16_instances(
op_ptrs);
}
}
}
......
library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_data/device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_bf16_instance.cpp
View file @ 49180fd6
......@@ -2,14 +2,14 @@
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "device_grouped_conv2d_bwd_data_xdl_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_data/device_grouped_conv_bwd_data_xdl_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for out[g, n, hi, wi, c] * wei[g, k, y, x, c] = in[g, n, ho, wo, k]
void add_device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_bf16_instances(
void add_device_grouped_conv2d_bwd_data_xdl_gnhwk_gkyxc_gnhwc_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<2,
GNHWK,
GKYXC,
......@@ -26,19 +26,21 @@ void add_device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_bf16_instances(
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv2d_bwd_data_xdl_bf16_instances<GNHWK,
GKYXC,
Empty_Tuple,
GNHWC,
ConvBwdDataDefault>{});
device_grouped_conv_bwd_data_xdl_bf16_instances<2,
GNHWK,
GKYXC,
Empty_Tuple,
GNHWC,
ConvBwdDataDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(
instances,
device_grouped_conv2d_bwd_data_xdl_bf16_instances<GNHWK,
GKYXC,
Empty_Tuple,
GNHWC,
ConvBwdDataFilter1x1Stride1Pad0>{});
device_grouped_conv_bwd_data_xdl_bf16_instances<2,
GNHWK,
GKYXC,
Empty_Tuple,
GNHWC,
ConvBwdDataFilter1x1Stride1Pad0>{});
}
} // namespace instance
......
library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_data/device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_f16_instance.cpp
View file @ 49180fd6
......@@ -2,14 +2,14 @@
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "device_grouped_conv2d_bwd_data_xdl_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_data/device_grouped_conv_bwd_data_xdl_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for out[g, n, hi, wi, c] * wei[g, k, y, x, c] = in[g, n, ho, wo, k]
void add_device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_f16_instances(
void add_device_grouped_conv2d_bwd_data_xdl_gnhwk_gkyxc_gnhwc_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<2,
GNHWK,
GKYXC,
......@@ -26,19 +26,21 @@ void add_device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_f16_instances(
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv2d_bwd_data_xdl_f16_instances<GNHWK,
GKYXC,
Empty_Tuple,
GNHWC,
ConvBwdDataDefault>{});
device_grouped_conv_bwd_data_xdl_f16_instances<2,
GNHWK,
GKYXC,
Empty_Tuple,
GNHWC,
ConvBwdDataDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(
instances,
device_grouped_conv2d_bwd_data_xdl_f16_instances<GNHWK,
GKYXC,
Empty_Tuple,
GNHWC,
ConvBwdDataFilter1x1Stride1Pad0>{});
device_grouped_conv_bwd_data_xdl_f16_instances<2,
GNHWK,
GKYXC,
Empty_Tuple,
GNHWC,
ConvBwdDataFilter1x1Stride1Pad0>{});
}
} // namespace instance
......
library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_data/device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_f32_instance.cpp
View file @ 49180fd6
......@@ -2,14 +2,14 @@
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "device_grouped_conv2d_bwd_data_xdl_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_data/device_grouped_conv_bwd_data_xdl_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for out[g, n, hi, wi, c] * wei[g, k, y, x, c] = in[g, n, ho, wo, k]
void add_device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_f32_instances(
void add_device_grouped_conv2d_bwd_data_xdl_gnhwk_gkyxc_gnhwc_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<2,
GNHWK,
GKYXC,
......@@ -26,19 +26,21 @@ void add_device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_f32_instances(
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv2d_bwd_data_xdl_f32_instances<GNHWK,
GKYXC,
Empty_Tuple,
GNHWC,
ConvBwdDataDefault>{});
device_grouped_conv_bwd_data_xdl_f32_instances<2,
GNHWK,
GKYXC,
Empty_Tuple,
GNHWC,
ConvBwdDataDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(
instances,
device_grouped_conv2d_bwd_data_xdl_f32_instances<GNHWK,
GKYXC,
Empty_Tuple,
GNHWC,
ConvBwdDataFilter1x1Stride1Pad0>{});
device_grouped_conv_bwd_data_xdl_f32_instances<2,
GNHWK,
GKYXC,
Empty_Tuple,
GNHWC,
ConvBwdDataFilter1x1Stride1Pad0>{});
}
} // namespace instance
......
library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_data/device_grouped_conv2d_bwd_data_xdl_instance.hpp deleted 100644 → 0
View file @ f82bd593
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_data_multiple_d_xdl_cshuffle_v1.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using BF16 = ck::bhalf_t;
using F16 = ck::half_t;
using F32 = float;
using Empty_Tuple = ck::Tuple<>;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using NHWGC = ck::tensor_layout::convolution::NHWGC;
using GNHWC = ck::tensor_layout::convolution::GNHWC;
using GKYXC = ck::tensor_layout::convolution::GKYXC;
using NHWGK = ck::tensor_layout::convolution::NHWGK;
using GNHWK = ck::tensor_layout::convolution::GNHWK;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto ConvBwdDataDefault = ConvolutionBackwardDataSpecialization::Default;
static constexpr auto ConvBwdDataFilter1x1Stride1Pad0 =
ConvolutionBackwardDataSpecialization::Filter1x1Stride1Pad0;
// f16_f16_f32_f16
template <typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionBackwardDataSpecialization ConvSpec>
using device_grouped_conv2d_bwd_data_xdl_f16_instances = std::tuple<
// clang-format off
// ##############################################| NDim| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| AElementwise| BElementwise| CDEElementwise| ConvolutionBackward| DoPad| DoPad| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffleMXdl| CShuffleNXdl| CDEBlockTransfer| CDEBlockTransfer|
// ##############################################| Spatial| | | | | Type| Type| Type| DataType| Type| Type| Operation| Operation| Operation| DataSpecialization| GemmM| GemmN| PrefetchStage| Size| Block| Block| Block| | | XDL| XDL| PerWave| PerWave| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| PerWave| PerWave| _MBlock_MPerBlock| ScalarPerVector|
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | Lengths_AK0_M_AK1| ArrangeOrder| | | PerVector| PerVector_AK1| | Lengths_BK0_N_BK1| ArrangeOrder| | | PerVector| PerVector_BK1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock|
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 256, 32, 8, 2, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>
#ifdef CK_WORKAROUND_SWDEV_3318619
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 128, 128, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 128, 32, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 64, 64, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 64, 32, 64, 32, 8, 8, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>,
#endif
// clang-format on
>;
// bf16_bf16_f32_bf16
template <typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionBackwardDataSpecialization ConvSpec>
using device_grouped_conv2d_bwd_data_xdl_bf16_instances = std::tuple<
// clang-format off
// ##############################################| NDim| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| AElementwise| BElementwise| CDEElementwise| ConvolutionBackward| DoPad| DoPad| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffleMXdl| CShuffleNXdl| CDEBlockTransfer| CDEBlockTransfer|
// ##############################################| Spatial| | | | | Type| Type| Type| DataType| Type| Type| Operation| Operation| Operation| DataSpecialization| GemmM| GemmN| PrefetchStage| Size| Block| Block| Block| | | XDL| XDL| PerWave| PerWave| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| PerWave| PerWave| _MBlock_MPerBlock| ScalarPerVector|
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | Lengths_AK0_M_AK1| ArrangeOrder| | | PerVector| PerVector_AK1| | Lengths_BK0_N_BK1| ArrangeOrder| | | PerVector| PerVector_BK1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock|
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 256, 32, 8, 2, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>
#ifdef CK_WORKAROUND_SWDEV_3318619
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 32, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 64, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, BF16, BF16, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 32, 64, 32, 8, 8, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>
#endif
// clang-format on
>;
// f32_f32_f32_f32
template <typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionBackwardDataSpecialization ConvSpec>
using device_grouped_conv2d_bwd_data_xdl_f32_instances = std::tuple<
// clang-format off
// ##############################################| NDim| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| AElementwise| BElementwise| CDEElementwise| ConvolutionBackward| DoPad| DoPad| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffleMXdl| CShuffleNXdl| CDEBlockTransfer| CDEBlockTransfer|
// ##############################################| Spatial| | | | | Type| Type| Type| DataType| Type| Type| Operation| Operation| Operation| DataSpecialization| GemmM| GemmN| PrefetchStage| Size| Block| Block| Block| | | XDL| XDL| PerWave| PerWave| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| PerWave| PerWave| _MBlock_MPerBlock| ScalarPerVector|
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | Lengths_AK0_M_AK1| ArrangeOrder| | | PerVector| PerVector_AK1| | Lengths_BK0_N_BK1| ArrangeOrder| | | PerVector| PerVector_BK1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock|
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 256, 32, 8, 2, 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<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 4>
#ifdef CK_WORKAROUND_SWDEV_3318619
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 256, 128, 32, 8, 8, 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>, 1, 4, 4, 1, 1, 1, S<1, 32, 1, 8>, 4>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 256, 32, 8, 8, 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>, 1, 4, 4, 1, 1, 1, S<1, 32, 1, 8>, 4>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 128, 32, 8, 8, 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>, 1, 4, 4, 1, 1, 1, S<1, 16, 1, 8>, 4>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 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, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 4, 4, 1, 1, 1, S<1, 32, 1, 8>, 4>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 64, 32, 8, 8, 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>, 1, 4, 4, 1, 1, 1, S<1, 32, 1, 4>, 4>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 64, 128, 32, 8, 8, 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>, 1, 4, 4, 1, 1, 1, S<1, 16, 1, 8>, 4>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 64, 64, 32, 8, 8, 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>, 1, 4, 4, 1, 1, 1, S<1, 16, 1, 4>, 4>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 128, 64, 32, 8, 8, 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>, 1, 4, 4, 1, 1, 1, S<1, 32, 1, 8>, 4>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 256, 64, 128, 32, 8, 8, 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>, 1, 4, 4, 1, 1, 1, S<1, 32, 1, 8>, 4>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 128, 32, 32, 8, 8, 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>, 1, 4, 4, 1, 1, 1, S<1, 32, 1, 4>, 4>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 128, 32, 128, 32, 8, 8, 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>, 1, 4, 4, 1, 1, 1, S<1, 16, 1, 8>, 4>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 64, 32, 32, 8, 8, 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>, 1, 4, 4, 1, 1, 1, S<1, 16, 1, 4>, 4>,
// DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, ALayout, BLayout, DsLayout, ELayout, F32, F32, F32, F32, Empty_Tuple, F32, PassThrough, PassThrough, PassThrough, ConvSpec, true, true, 1, 64, 32, 64, 32, 8, 8, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>,
#endif
// clang-format on
>;
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_data/device_grouped_conv2d_bwd_data_xdl_nhwgc_gkyxc_nhwgk_bf16_instance.cpp
View file @ 49180fd6
......@@ -2,14 +2,14 @@
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "device_grouped_conv2d_bwd_data_xdl_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_data/device_grouped_conv_bwd_data_xdl_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for out[n, hi, wi, g, c] * wei[g, k, y, x, c] = in[n, ho, wo, g, k]
void add_device_grouped_conv2d_bwd_data_xdl_nhwgc_gkyxc_nhwgk_bf16_instances(
void add_device_grouped_conv2d_bwd_data_xdl_nhwgk_gkyxc_nhwgc_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<2,
NHWGK,
GKYXC,
......@@ -26,19 +26,21 @@ void add_device_grouped_conv2d_bwd_data_xdl_nhwgc_gkyxc_nhwgk_bf16_instances(
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv2d_bwd_data_xdl_bf16_instances<NHWGK,
GKYXC,
Empty_Tuple,
NHWGC,
ConvBwdDataDefault>{});
device_grouped_conv_bwd_data_xdl_bf16_instances<2,
NHWGK,
GKYXC,
Empty_Tuple,
NHWGC,
ConvBwdDataDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(
instances,
device_grouped_conv2d_bwd_data_xdl_bf16_instances<NHWGK,
GKYXC,
Empty_Tuple,
NHWGC,
ConvBwdDataFilter1x1Stride1Pad0>{});
device_grouped_conv_bwd_data_xdl_bf16_instances<2,
NHWGK,
GKYXC,
Empty_Tuple,
NHWGC,
ConvBwdDataFilter1x1Stride1Pad0>{});
}
} // namespace instance
......
library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_data/device_grouped_conv2d_bwd_data_xdl_nhwgc_gkyxc_nhwgk_f16_instance.cpp
View file @ 49180fd6
......@@ -2,14 +2,14 @@
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "device_grouped_conv2d_bwd_data_xdl_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_data/device_grouped_conv_bwd_data_xdl_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for out[n, hi, wi, g, c] * wei[g, k, y, x, c] = in[n, ho, wo, g, k]
void add_device_grouped_conv2d_bwd_data_xdl_nhwgc_gkyxc_nhwgk_f16_instances(
void add_device_grouped_conv2d_bwd_data_xdl_nhwgk_gkyxc_nhwgc_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<2,
NHWGK,
GKYXC,
......@@ -26,19 +26,21 @@ void add_device_grouped_conv2d_bwd_data_xdl_nhwgc_gkyxc_nhwgk_f16_instances(
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv2d_bwd_data_xdl_f16_instances<NHWGK,
GKYXC,
Empty_Tuple,
NHWGC,
ConvBwdDataDefault>{});
device_grouped_conv_bwd_data_xdl_f16_instances<2,
NHWGK,
GKYXC,
Empty_Tuple,
NHWGC,
ConvBwdDataDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(
instances,
device_grouped_conv2d_bwd_data_xdl_f16_instances<NHWGK,
GKYXC,
Empty_Tuple,
NHWGC,
ConvBwdDataFilter1x1Stride1Pad0>{});
device_grouped_conv_bwd_data_xdl_f16_instances<2,
NHWGK,
GKYXC,
Empty_Tuple,
NHWGC,
ConvBwdDataFilter1x1Stride1Pad0>{});
}
} // namespace instance
......
library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_data/device_grouped_conv2d_bwd_data_xdl_nhwgc_gkyxc_nhwgk_f32_instance.cpp
View file @ 49180fd6
......@@ -2,14 +2,14 @@
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "device_grouped_conv2d_bwd_data_xdl_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_data/device_grouped_conv_bwd_data_xdl_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for out[n, hi, wi, g, c] * wei[g, k, y, x, c] = in[n, ho, wo, g, k]
void add_device_grouped_conv2d_bwd_data_xdl_nhwgc_gkyxc_nhwgk_f32_instances(
void add_device_grouped_conv2d_bwd_data_xdl_nhwgk_gkyxc_nhwgc_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<2,
NHWGK,
GKYXC,
......@@ -26,19 +26,21 @@ void add_device_grouped_conv2d_bwd_data_xdl_nhwgc_gkyxc_nhwgk_f32_instances(
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv2d_bwd_data_xdl_f32_instances<NHWGK,
GKYXC,
Empty_Tuple,
NHWGC,
ConvBwdDataDefault>{});
device_grouped_conv_bwd_data_xdl_f32_instances<2,
NHWGK,
GKYXC,
Empty_Tuple,
NHWGC,
ConvBwdDataDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(
instances,
device_grouped_conv2d_bwd_data_xdl_f32_instances<NHWGK,
GKYXC,
Empty_Tuple,
NHWGC,
ConvBwdDataFilter1x1Stride1Pad0>{});
device_grouped_conv_bwd_data_xdl_f32_instances<2,
NHWGK,
GKYXC,
Empty_Tuple,
NHWGC,
ConvBwdDataFilter1x1Stride1Pad0>{});
}
} // namespace instance
......
library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_data/CMakeLists.txt 0 → 100644
View file @ 49180fd6
add_instance_library(device_grouped_conv3d_bwd_data_instance
device_grouped_conv3d_bwd_data_xdl_gndhwc_gkzyxc_gndhwk_f16_instance.cpp
device_grouped_conv3d_bwd_data_xdl_gndhwc_gkzyxc_gndhwk_bf16_instance.cpp
device_grouped_conv3d_bwd_data_xdl_gndhwc_gkzyxc_gndhwk_f32_instance.cpp
device_grouped_conv3d_bwd_data_xdl_ndhwgc_gkzyxc_ndhwgk_f16_instance.cpp
device_grouped_conv3d_bwd_data_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_instance.cpp
device_grouped_conv3d_bwd_data_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instance.cpp
)
library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_data/device_grouped_conv3d_bwd_data_xdl_gndhwc_gkzyxc_gndhwk_bf16_instance.cpp 0 → 100644
View file @ 49180fd6
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, 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_data/device_grouped_conv_bwd_data_xdl_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for out[g, n, di, hi, wi, c] * wei[g, k, z, y, x, c] = in[g, n, do, ho,
// wo, k]
void add_device_grouped_conv3d_bwd_data_xdl_gndhwk_gkzyxc_gndhwc_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<3,
GNDHWK,
GKZYXC,
Empty_Tuple,
GNDHWC,
BF16,
BF16,
Empty_Tuple,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv_bwd_data_xdl_bf16_instances<3,
GNDHWK,
GKZYXC,
Empty_Tuple,
GNDHWC,
ConvBwdDataDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(
instances,
device_grouped_conv_bwd_data_xdl_bf16_instances<3,
GNDHWK,
GKZYXC,
Empty_Tuple,
GNDHWC,
ConvBwdDataFilter1x1Stride1Pad0>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_data/device_grouped_conv3d_bwd_data_xdl_gndhwc_gkzyxc_gndhwk_f16_instance.cpp 0 → 100644
View file @ 49180fd6
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, 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_data/device_grouped_conv_bwd_data_xdl_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for out[g, n, di, hi, wi, c] * wei[g, k, z, y, x, c] = in[g, n, do, ho,
// wo, k]
void add_device_grouped_conv3d_bwd_data_xdl_gndhwk_gkzyxc_gndhwc_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<3,
GNDHWK,
GKZYXC,
Empty_Tuple,
GNDHWC,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv_bwd_data_xdl_f16_instances<3,
GNDHWK,
GKZYXC,
Empty_Tuple,
GNDHWC,
ConvBwdDataDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(
instances,
device_grouped_conv_bwd_data_xdl_f16_instances<3,
GNDHWK,
GKZYXC,
Empty_Tuple,
GNDHWC,
ConvBwdDataFilter1x1Stride1Pad0>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_data/device_grouped_conv3d_bwd_data_xdl_gndhwc_gkzyxc_gndhwk_f32_instance.cpp 0 → 100644
View file @ 49180fd6
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, 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_data/device_grouped_conv_bwd_data_xdl_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for out[g, n, di, hi, wi, c] * wei[g, k, z, y, x, c] = in[g, n, do, ho,
// wo, k]
void add_device_grouped_conv3d_bwd_data_xdl_gndhwk_gkzyxc_gndhwc_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<3,
GNDHWK,
GKZYXC,
Empty_Tuple,
GNDHWC,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv_bwd_data_xdl_f32_instances<3,
GNDHWK,
GKZYXC,
Empty_Tuple,
GNDHWC,
ConvBwdDataDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(
instances,
device_grouped_conv_bwd_data_xdl_f32_instances<3,
GNDHWK,
GKZYXC,
Empty_Tuple,
GNDHWC,
ConvBwdDataFilter1x1Stride1Pad0>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_data/device_grouped_conv3d_bwd_data_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_instance.cpp 0 → 100644
View file @ 49180fd6
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, 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_data/device_grouped_conv_bwd_data_xdl_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for out[n, di, hi, wi, g, c] * wei[g, k, z, y, x, c] = in[n, do, ho, wo,
// g, k]
void add_device_grouped_conv3d_bwd_data_xdl_ndhwgk_gkzyxc_ndhwgc_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<3,
NDHWGK,
GKZYXC,
Empty_Tuple,
NDHWGC,
BF16,
BF16,
Empty_Tuple,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv_bwd_data_xdl_bf16_instances<3,
NDHWGK,
GKZYXC,
Empty_Tuple,
NDHWGC,
ConvBwdDataDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(
instances,
device_grouped_conv_bwd_data_xdl_bf16_instances<3,
NDHWGK,
GKZYXC,
Empty_Tuple,
NDHWGC,
ConvBwdDataFilter1x1Stride1Pad0>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_data/device_grouped_conv3d_bwd_data_xdl_ndhwgc_gkzyxc_ndhwgk_f16_instance.cpp 0 → 100644
View file @ 49180fd6
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, 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_data/device_grouped_conv_bwd_data_xdl_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for out[n, di, hi, wi, g, c] * wei[g, k, z, y, x, c] = in[n, do, ho, wo,
// g, k]
void add_device_grouped_conv3d_bwd_data_xdl_ndhwgk_gkzyxc_ndhwgc_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<3,
NDHWGK,
GKZYXC,
Empty_Tuple,
NDHWGC,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv_bwd_data_xdl_f16_instances<3,
NDHWGK,
GKZYXC,
Empty_Tuple,
NDHWGC,
ConvBwdDataDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(
instances,
device_grouped_conv_bwd_data_xdl_f16_instances<3,
NDHWGK,
GKZYXC,
Empty_Tuple,
NDHWGC,
ConvBwdDataFilter1x1Stride1Pad0>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_data/device_grouped_conv3d_bwd_data_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instance.cpp 0 → 100644
View file @ 49180fd6
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, 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_data/device_grouped_conv_bwd_data_xdl_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for out[n, di, hi, wi, g, c] * wei[g, k, z, y, x, c] = in[n, do, ho, wo,
// g, k]
void add_device_grouped_conv3d_bwd_data_xdl_ndhwgk_gkzyxc_ndhwgc_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<3,
NDHWGK,
GKZYXC,
Empty_Tuple,
NDHWGC,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv_bwd_data_xdl_f32_instances<3,
NDHWGK,
GKZYXC,
Empty_Tuple,
NDHWGC,
ConvBwdDataDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(
instances,
device_grouped_conv_bwd_data_xdl_f32_instances<3,
NDHWGK,
GKZYXC,
Empty_Tuple,
NDHWGC,
ConvBwdDataFilter1x1Stride1Pad0>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
profiler/src/CMakeLists.txt
View file @ 49180fd6
......@@ -81,4 +81,5 @@ target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_contraction_scale_in
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_pool_fwd_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_multi_d_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv2d_bwd_data_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_bwd_data_instance)
rocm_install(TARGETS ${PROFILER_EXECUTABLE} COMPONENT profiler)
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